Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges
---
paper_title: The Sensor Web: systems of sensor systems

paper_content:

Abstract Global Earth Observing System of Systems (GEOSS) presents a great challenge of System of Systems integration across organisational and political boundaries. One existing paradigm that can address the scale of the challenge is that of the Sensor Web. In this paradigm, the internet is evolving into an active, macro sensing instrument, capable of drawing sensory data from around the globe to the fingertips of individuals. The Sensor Web will support scientific research and facilitate transparent political decision making. This article presents some of the technologies explored and activities engaged in by the GEOSS Sensor Web community, towards achieving GEOSS goals.
---
paper_title: Predicting human mobility with activity changes

paper_content:

Human mobility patterns can provide valuable information in understanding the impact of human behavioral regularities in urban systems, usually with a specific focus on traffic prediction, public health or urban planning. While existing studies on human movement have placed huge emphasis on spatial location to predict where people go next, the time dimension component is usually being treated with oversimplification or even being neglected. Time dimension is crucial to understanding and detecting human activity changes, which play a negative role in prediction and thus may affect the predictive accuracy. This study aims to predict human movement from a spatio-temporal perspective by taking into account the impact of activity changes. We analyze and define changes of human activity and propose an algorithm to detect such changes, based on which a Markov chain model is used to predict human movement. The Microsoft GeoLife dataset is used to test our methodology, and the data of two selected users is used to evaluate the performance of the prediction. We compare the predictive accuracy R2 derived from the data with and without implementing the activity change detection. The results show that the R2 is improved from 0.295 to 0.762 for the user with obvious activity changes and from 0.965 to 0.971 for the user without obvious activity changes. The method proposed by this study improves the accuracy in analyzing and predicting human movement and lays the foundation for related urban studies.
---
paper_title: Crowdsourcing geographic information for disaster response: a research frontier

paper_content:

Abstract Geographic data and tools are essential in all aspects of emergency management: preparedness, response, recovery, and mitigation. Geographic information created by amateur citizens, often known as volunteered geographic information, has recently provided an interesting alternative to traditional authoritative information from mapping agencies and corporations, and several recent papers have provided the beginnings of a literature on the more fundamental issues raised by this new source. Data quality is a major concern, since volunteered information is asserted and carries none of the assurances that lead to trust in officially created data. During emergencies time is the essence, and the risks associated with volunteered information are often outweighed by the benefits of its use. An example is discussed using the four wildfires that impacted the Santa Barbara area in 2007–2009, and lessons are drawn.
---
paper_title: Big data classification: problems and challenges in network intrusion prediction with machine learning

paper_content:

This paper focuses on the specific problem of Big Data classification of network intrusion traffic. It discusses the system challenges presented by the Big Data problems associated with network intrusion prediction. The prediction of a possible intrusion attack in a network requires continuous collection of traffic data and learning of their characteristics on the fly. The continuous collection of traffic data by the network leads to Big Data problems that are caused by the volume, variety and velocity properties of Big Data. The learning of the network characteristics require machine learning techniques that capture global knowledge of the traffic patterns. The Big Data properties will lead to significant system challenges to implement machine learning frameworks. This paper discusses the problems and challenges in handling Big Data classification using geometric representation-learning techniques and the modern Big Data networking technologies. In particular this paper discusses the issues related to combining supervised learning techniques, representation-learning techniques, machine lifelong learning techniques and Big Data technologies (e.g. Hadoop, Hive and Cloud) for solving network traffic classification problems.
---
paper_title: Micro-mapping with smartphones for monitoring agricultural development

paper_content:

Monitoring and evaluating progress and impact of development projects is a critical aspect. In this paper, we show how we can use a smartphone based system to intuitively retrieve the exact geometry of smaller objects, making it suitable to assess agricultural entities like fields or ponds with their exact extent and location. The system's simple and intuitive workflow can be used by laymen and thus allows for crowdsourcing geo-data on a local level.
---
paper_title: Tracing the German centennial flood in the stream of tweets: first lessons learned

paper_content:

Social microblogging services such as Twitter result in massive streams of georeferenced messages and geolocated status updates. This real-time source of information is invaluable for many application areas, in particular for disaster detection and response scenarios. Consequently, a considerable number of works has dealt with issues of their acquisition, analysis and visualization. Most of these works not only assume an appropriate percentage of georeferenced messages that allows for detecting relevant events for a specific region and time frame, but also that these geolocations are reasonably correct in representing places and times of the underlying spatio-temporal situation. In this paper, we review these two key assumption based on the results of applying a visual analytics approach to a dataset of georeferenced Tweets from Germany over eight months witnessing several large-scale flooding situations throughout the country. Our results confirm the potential of Twitter as a distributed 'social sensor' but at the same time highlight some caveats in interpreting immediate results. To overcome these limits we explore incorporating evidence from other data sources including further social media and mobile phone network metrics to detect, confirm and refine events with respect to location and time. We summarize the lessons learned from our initial analysis by proposing recommendations and outline possible future work directions.
---
paper_title: Assessing Volunteered Geographic Information for Rapid Flood Damage Estimation

paper_content:

For disaster management, a broad overview of the situation, resulting impairments and damages is required. For this overview, information from many different sources has to be acquired and integrated. Observations from the affected population can be an important source of information. New Internet technologies facilitate fast and easy data collection from the public. A major obstacle for using this information is its unknown quality. The aim of this research is to develop methods to assess the quality of observations from the affected population for rapid loss estimation after flood events. In a first step, data about flood events and associated losses collected in telephone interviews are assessed for their reliability and fitness for use for flood loss modelling. First results show that water level can be estimated by observers with a similar accuracy as it can be modelled by hydraulic models. Flow velocity, however, is very difficult to estimate and the estimates differ significantly from modelled values. The results of this analysis will be used in a second step to develop an automated procedure for quality assessment of observations from the public to be used in a prototypical implementation of web-based data collection for flood events.
---
paper_title: Citizens as Sensors: The World of Volunteered Geography

paper_content:

In recent months there has been an explosion of interest in using the Web to create, assemble, and disseminate geographic information provided voluntarily by individuals. Sites such as Wikimapia and OpenStreetMap are empowering citizens to create a global patchwork of geographic information, while Google Earth and other virtual globes are encouraging volunteers to develop interesting applications using their own data. I review this phenomenon, and examine associated issues: what drives people to do this, how accurate are the results, will they threaten individual privacy, and how can they augment more conventional sources? I compare this new phenomenon to more traditional citizen science and the role of the amateur in geographic observation.
---
paper_title: Tiers of ontology and consistency constraints in geographical information systems

paper_content:

Consistency constraints placed on a database to assure, that values incorporated in the database are consistent, are a well known foundation of Geographical Information Systems. Unfortunately in real situations rules for consistency constraints are not so clear, and inconsistent ontologies are common place, not least in geographical information, covering as it does a much wider realm than many other information systems I have suggested elsewhere 5-tiers of ontology for GIS. Such an ontology can integrate different ontological approaches in a unified system. In this paper the relation of the 5-tier ontology and consistency constraints is explored, and it is shown that different constraints are appropriate to different tiers.
---
paper_title: A Comparative Study of Proprietary Geodata and Volunteered Geographic Information for Germany

paper_content:

In connection with the Web 2.0 movement of the Internet (O’Reilly, 2005) and the progressive development of tools and applications for the collection and provision of spatial information (Turner, 2006), the quality and quantity of so-called Volunteered Geographic Information (VGI) (Goodchild, 2007) underwent a fast-paced worldwide development. Some even speak of a “Wikification of GIS” (Sui, 2008). This spatial data, mostly collected by volunteers, is freely available for the Internet user and can (under certain licensing conditions) be applied to numerous GIS projects and applications. Through advanced data donations, but also by a variety of other non-proprietary data sources, some of these free data providers are able to offer a vast variety of different information. This development in recent years stands in strong contrast to the very expensive commercial spatial data provided by a few companies. Much of this proprietary data is widely used today, for example, in car navigation devices or cell phones. The strong demand for freely available spatial data, though, has boosted the number of VGI available on the Internet. They can be found in very simple forms such as in Wikipedia entries that provide some spatial information like lat-long coordinates (geotag), or in so-called mashups in Google Earth or Google Maps, which combine different information sources. One of the most complex and promising projects in recent years, however, is OpenStreetMap (OSM).
---
paper_title: Citizens as database: conscious ubiquity in data collection

paper_content:

Crowd sourcing [1], citzens as sensors [2], user-generated content [3,4], or volunteered geographic information [5] describe a relatively recent phenomenon that points to dramatic changes in our information economy. Users of a system, who often are not trained in the matter at hand, contribute data that they collected without a central authority managing or supervising the data collection process. The individual approaches vary and cover a spectrum from conscious user actions (‘volunteered’) to passive modes (‘citizens as sensors’). Volunteered user-generated content is often used to replace existing commercial or authoritative datasets, for example, Wikipedia as an open encyclopaedia, or OpenStreetMap as an open topographic dataset of the world. Other volunteered content exploits the rapid update cycles of such mechanisms to provide improved services. For example, fixmystreet.com reports damages related to streets; Google, TomTom and other dataset providers encourage their users to report updates of their spatial data. In some cases, the database itself is the service; for example, Flickr allows users to upload and share photos. At the passive end of the spectrum, data mining methods can be used to further elicit hidden information out of the data. Researchers identified, for example, landmarks defining a town from Flickr photo collections [6], and commercial services track anonymized mobile phone locations to estimate traffic flow and enable real-time route planning.
---
paper_title: An application of problem and product ontologies for the revision of beach nourishments

paper_content:

An ontological approach in GIS serves as a framework for the conceptualization of processes in the real world. In this paper, we examine an application in coastal change in the Netherlands, whereby beaches are subject to artificial nourishment to offset the effect of severe erosion. The use of ontologies helps to define two scenarios: S-I determined by the regulations from the Ministry for Public Works; S-II grounded on the abilities from an existing spatial dataset. A comparison between S-I and S-II shows that 72.8\% of the objects suitable and unsuitable for nourishment are correctly classified. A higher overlap is found in areas where actual beach nourishments were carried out. Inaccuracies in attributes influence the determination of the objects. A sensitivity analysis applied to altitude illustrates a significant increase of objects suitable for nourishment for both scenarios, when altitude is decreased within the lower limit of the root mean square error for the 95\% confidence interval. Moreover, the sensitivity of altitude shows that artificial boundaries for beach nourishment objects are not reasonable and consequently should be treated as vague objects.
---
paper_title: Privacy in Location-Aware Computing Environments

paper_content:

This study explores how privacy preferences vary with place and social context. These findings are useful for designing privacy policies and user interfaces for pervasive computing.
---
paper_title: Data-driven geography

paper_content:

The context for geographic research has shifted from a data-scarce to a data-rich environment, in which the most fundamental changes are not just the volume of data, but the variety and the velocity at which we can capture georeferenced data; trends often associated with the concept of Big Data. A data-driven geography may be emerging in response to the wealth of georeferenced data flowing from sensors and people in the environment. Although this may seem revolutionary, in fact it may be better described as evolutionary. Some of the issues raised by data-driven geography have in fact been longstanding issues in geographic research, namely, large data volumes, dealing with populations and messy data, and tensions between idiographic versus nomothetic knowledge. The belief that spatial context matters is a major theme in geographic thought and a major motivation behind approaches such as time geography, disaggregate spatial statistics and GIScience. There is potential to use Big Data to inform both geographic knowledge-discovery and spatial modeling. However, there are challenges, such as how to formalize geographic knowledge to clean data and to ignore spurious patterns, and how to build data-driven models that are both true and understandable.
---
paper_title: Modeling dynamic beach objects using spatio - temporal ontologies

paper_content:

This study models geospatial landscape features using spatio-temporal ontologies. A spatio-temporal ontology explicitly specifies a dynamic conceptualization of real world objects. Spatio-temporal ontologies are applied to the Isle of Ameland, relating beach nourishment to elevation, vegetation and wetness. The spatio-temporal ontology contains full membership functions for crisp objects, partial membership functions for fuzzy objects and temporal membership functions for dynamic fuzzy objects. The temporal membership functions include seasonal changes of vegetation and daily changes in wetness. Quality elements are discussed by means of a quality matrix, where ontological features (i.e. objects, attributes, relationships) are projected against their qualities. A sensitivity analysis showed that the calculated beach nourishment volumes are practically insensitive in relation to assumptions on the temporal membership functions. Spatio-temporal ontologies are shown to extend spatial ontologies to model dynamic processes in environmental studies in a more realistic way.
---
paper_title: Efficient spatial query processing for big data

paper_content:

Spatial queries are widely used in many data mining and analytics applications. However, a huge and growing size of spatial data makes it challenging to process the spatial queries efficiently. In this paper we present a lightweight and scalable spatial index for big data stored in distributed storage systems. Experimental results show the efficiency and effectiveness of our spatial indexing technique for different spatial queries.
---
paper_title: Applications of the MapReduce programming framework to clinical big data analysis: current landscape and future trends

paper_content:

The emergence of massive datasets in a clinical setting presents both challenges and opportunities in data storage and analysis. This so called “big data” challenges traditional analytic tools and will increasingly require novel solutions adapted from other fields. Advances in information and communication technology present the most viable solutions to big data analysis in terms of efficiency and scalability. It is vital those big data solutions are multithreaded and that data access approaches be precisely tailored to large volumes of semi-structured/unstructured data.The MapReduce programming framework uses two tasks common in functional programming: Map and Reduce. MapReduce is a new parallel processing framework and Hadoop is its open-source implementation on a single computing node or on clusters. Compared with existing parallel processing paradigms (e.g. grid computing and graphical processing unit (GPU)), MapReduce and Hadoop have two advantages: 1) fault-tolerant storage resulting in reliable data processing by replicating the computing tasks, and cloning the data chunks on different computing nodes across the computing cluster; 2) high-throughput data processing via a batch processing framework and the Hadoop distributed file system (HDFS). Data are stored in the HDFS and made available to the slave nodes for computation.In this paper, we review the existing applications of the MapReduce programming framework and its implementation platform Hadoop in clinical big data and related medical health informatics fields. The usage of MapReduce and Hadoop on a distributed system represents a significant advance in clinical big data processing and utilization, and opens up new opportunities in the emerging era of big data analytics. The objective of this paper is to summarize the state-of-the-art efforts in clinical big data analytics and highlight what might be needed to enhance the outcomes of clinical big data analytics tools. This paper is concluded by summarizing the potential usage of the MapReduce programming framework and Hadoop platform to process huge volumes of clinical data in medical health informatics related fields.
---
paper_title: Streaming big data with self-adjusting computation

paper_content:

Many big data computations involve processing data that changes incrementally or dynamically over time. Using existing techniques, such computations quickly become impractical. For example, computing the frequency of words in the first ten thousand paragraphs of a publicly available Wikipedia data set in a streaming fashion using MapReduce can take as much as a full day. In this paper, we propose an approach based on self-adjusting computation that can dramatically improve the efficiency of such computations. As an example, we can perform the aforementioned streaming computation in just a couple of minutes.
---
paper_title: Spatial big-data challenges intersecting mobility and cloud computing

paper_content:

Increasingly, location-aware datasets are of a size, variety, and update rate that exceeds the capability of spatial computing technologies. This paper addresses the emerging challenges posed by such datasets, which we call Spatial Big Data (SBD). SBD examples include trajectories of cellphones and GPS devices, vehicle engine measurements, temporally detailed road maps, etc. SBD has the potential to transform society via next-generation routing services such as eco-routing. However, the envisaged SBD-based next-generation routing services pose several significant challenges for current routing techniques. SBD magnifies the impact of partial information and ambiguity of traditional routing queries specified by a start location and an end location. In addition, SBD challenges the assumption that a single algorithm utilizing a specific dataset is appropriate for all situations. The tremendous diversity of SBD sources substantially increases the diversity of solution methods. Newer algorithms may emerge as new SBD becomes available, creating the need for a flexible architecture to rapidly integrate new datasets and associated algorithms.
---
paper_title: User–Defined Rules Made Simple with Functional Programming

paper_content:

To be successful, any new business information system must address the needs of business users, and have a short ‘time–to–value’. Depending on the requirements, the appropriate tools and techniques would vary. Sometimes a good way to meet the needs of business users is by providing them with a domain–specific language (DSL) in which they can model their problems or seek solutions.
---
paper_title: Handling Big Data Efficiently by Using Map Reduce Technique

paper_content:

Extremely large amount of data is being captured by today's organizations and is continue to increase. It becomes computationally inefficient to analyze such huge data. Researchers has addressed problem in discovering knowledge from these continuously growing large data sets. Quantity of available raw data has been increasing at a very high rate. The precious information is concealed in large databases. Data mining has become an interesting area to extract the embedded precious information from them. For many years it has been found its root in all kinds of application areas. Thus, gave evolution to many data mining methods which started to get applied in several real life fields. But not all the methods possess the capability to deal with and handle the huge collection of data. In recent years, numbers of computation and data intensive scientific data analyses are established. To perform the large scale data mining analyses so as to meet the scalability and performance requirements of big data, several efficient parallel and concurrent algorithms got applied. A lot of parallel algorithms are put into action using different parallelization techniques, such as-threads, MPI, MapReduce etc. Which yield different performance and usability characteristics. The MPI model works efficiently in computing rigorous problems but it is a complicated task to bring this model into the practical use. There is currently considerable enthusiasm around the MapReduce paradigm for large-scale data analysis. It is inspired by functional programming which allows expressing distributed computations on massive amounts of data. It is designed for large-scale data processing as it allows to run on clusters of commodity hardware. A prominent parallel data processing tool MapReduce is gaining significant momentum from both industry and academia as the volume of data to analyze grows rapidly. In this paper, we are going to work around MapReduce, its advantages, disadvantages and how it can be used in integration with other technology.
---
paper_title: User–Defined Rules Made Simple with Functional Programming

paper_content:

To be successful, any new business information system must address the needs of business users, and have a short ‘time–to–value’. Depending on the requirements, the appropriate tools and techniques would vary. Sometimes a good way to meet the needs of business users is by providing them with a domain–specific language (DSL) in which they can model their problems or seek solutions.
---
paper_title: Handling Big Data Efficiently by Using Map Reduce Technique

paper_content:

Extremely large amount of data is being captured by today's organizations and is continue to increase. It becomes computationally inefficient to analyze such huge data. Researchers has addressed problem in discovering knowledge from these continuously growing large data sets. Quantity of available raw data has been increasing at a very high rate. The precious information is concealed in large databases. Data mining has become an interesting area to extract the embedded precious information from them. For many years it has been found its root in all kinds of application areas. Thus, gave evolution to many data mining methods which started to get applied in several real life fields. But not all the methods possess the capability to deal with and handle the huge collection of data. In recent years, numbers of computation and data intensive scientific data analyses are established. To perform the large scale data mining analyses so as to meet the scalability and performance requirements of big data, several efficient parallel and concurrent algorithms got applied. A lot of parallel algorithms are put into action using different parallelization techniques, such as-threads, MPI, MapReduce etc. Which yield different performance and usability characteristics. The MPI model works efficiently in computing rigorous problems but it is a complicated task to bring this model into the practical use. There is currently considerable enthusiasm around the MapReduce paradigm for large-scale data analysis. It is inspired by functional programming which allows expressing distributed computations on massive amounts of data. It is designed for large-scale data processing as it allows to run on clusters of commodity hardware. A prominent parallel data processing tool MapReduce is gaining significant momentum from both industry and academia as the volume of data to analyze grows rapidly. In this paper, we are going to work around MapReduce, its advantages, disadvantages and how it can be used in integration with other technology.
---
paper_title: RF power consumption emulation optimized with interval valued homotopies

paper_content:

This paper presents a methodology towards the emulation of the electrical power consumption of the RF device during the cellular phone/handset transmission mode using the LTE technology. The emulation methodology takes the physical environmental variables and the logical interface between the baseband and the RF system as inputs to compute the emulated power dissipation of the RF device. The emulated power, in between the measured points corresponding to the discrete values of the logical interface parameters is computed as a polynomial interpolation using polynomial basis functions. The evaluation of polynomial and spline curve fitting models showed a respective divergence (test error) of 8% and 0.02% from the physically measured power consumption. The precisions of the instruments used for the physical measurements have been modeled as intervals. We have been able to model the power consumption of the RF device operating at 5MHz using homotopy between 2 continuous power consumptions of the RF device operating at the bandwidths 3MHz and 10MHz. Keywords—Radio frequency, High Power Amplifier, Baseband, LTE, Power, Emulation, Homotopy, Interval analysis, Tx power, Register-Transfer Level.
---
paper_title: Spatial big-data challenges intersecting mobility and cloud computing

paper_content:

Increasingly, location-aware datasets are of a size, variety, and update rate that exceeds the capability of spatial computing technologies. This paper addresses the emerging challenges posed by such datasets, which we call Spatial Big Data (SBD). SBD examples include trajectories of cellphones and GPS devices, vehicle engine measurements, temporally detailed road maps, etc. SBD has the potential to transform society via next-generation routing services such as eco-routing. However, the envisaged SBD-based next-generation routing services pose several significant challenges for current routing techniques. SBD magnifies the impact of partial information and ambiguity of traditional routing queries specified by a start location and an end location. In addition, SBD challenges the assumption that a single algorithm utilizing a specific dataset is appropriate for all situations. The tremendous diversity of SBD sources substantially increases the diversity of solution methods. Newer algorithms may emerge as new SBD becomes available, creating the need for a flexible architecture to rapidly integrate new datasets and associated algorithms.
---
paper_title: 3D Hilbert Space Filling Curves in 3D City Modeling for Faster Spatial Queries

paper_content:

The advantages of three dimensional (3D) city models can be seen in various applications including photogrammetry, urban and regional planning, computer games, etc. They expand the visualization and analysis capabilities of Geographic Information Systems on cities, and they can be developed using web standards. However, these 3D city models consume much more storage compared to two dimensional (2 D) spatial data. They involve extra geometrical and topological information together with semantic data. Without a proper spatial data clustering method and its corresponding spatial data access method, retrieving portions of and especially searching these 3D city models, will not be done optimally. Even though current developments are based on an open data model allotted by the Open Geospatial Consortium (OGC) called CityGML, its XML-based structure makes it challenging to cluster the 3D urban objects. In this research, the authors propose an opponent data constellation technique of space-filling curves (3D Hilbert curves) for 3D city model data representation. Unlike previous methods, that try to project 3D or n-dimensional data down to 2D or 3D using Principal Component Analysis (PCA) or Hilbert mappings, in this research, they extend the Hilbert space-filling curve to one higher dimension for 3D city model data implementations. The query performance was tested for single object, nearest neighbor and range search queries using a CityGML dataset of 1,000 building blocks and the results are presented in this paper. The advantages of implementing space-filling curves in 3D city modeling will improve data retrieval time by means of optimized 3D adjacency, nearest neighbor information and 3D indexing. The Hilbert mapping, which maps a sub-interval of the ([0,1]) interval to the corresponding portion of the d-dimensional Hilbert's curve, preserves the Lebesgue measure and is Lipschitz continuous. Depending on the applications, several alternatives are possible in order to cluster spatial data together in the third dimension compared to its clustering in 2 D.
---
paper_title: Nonlinear Black-Box Models in System Identification: Mathematical Foundations

paper_content:

We discuss several aspects of the mathematical foundations of the nonlinear black-box identification problem. We shall see that the quality of the identification procedure is always a result of a certain trade-off between the expressive power of the model we try to identify (the larger the number of parameters used to describe the model, the more flexible is the approximation), and the stochastic error (which is proportional to the number of parameters). A consequence of this trade-off is the simple fact that a good approximation technique can be the basis of a good identification algorithm. From this point of view, we consider different approximation methods, and pay special attention to spatially adaptive approximants. We introduce wavelet and ‘neuron’ approximations, and show that they are spatially adaptive. Then we apply the acquired approximation experience to estimation problems. Finally, we consider some implications of these theoretical developments for the practically implemented versions of the ‘spatially adaptive’ algorithms.
---
paper_title: Verifying quantitative reliability for programs that execute on unreliable hardware

paper_content:

Emerging high-performance architectures are anticipated to contain unreliable components that may exhibit soft errors, which silently corrupt the results of computations. Full detection and masking of soft errors is challenging, expensive, and, for some applications, unnecessary. For example, approximate computing applications (such as multimedia processing, machine learning, and big data analytics) can often naturally tolerate soft errors. We present Rely a programming language that enables developers to reason about the quantitative reliability of an application -- namely, the probability that it produces the correct result when executed on unreliable hardware. Rely allows developers to specify the reliability requirements for each value that a function produces. We present a static quantitative reliability analysis that verifies quantitative requirements on the reliability of an application, enabling a developer to perform sound and verified reliability engineering. The analysis takes a Rely program with a reliability specification and a hardware specification that characterizes the reliability of the underlying hardware components and verifies that the program satisfies its reliability specification when executed on the underlying unreliable hardware platform. We demonstrate the application of quantitative reliability analysis on six computations implemented in Rely.
---
paper_title: A review of overview+detail, zooming, and focus+context interfaces

paper_content:

There are many interface schemes that allow users to work at, and move between, focused and contextual views of a dataset. We review and categorize these schemes according to the interface mechanisms used to separate and blend views. The four approaches are overview+detail, which uses a spatial separation between focused and contextual views; zooming, which uses a temporal separation; focus+context, which minimizes the seam between views by displaying the focus within the context; and cue-based techniques which selectively highlight or suppress items within the information space. Critical features of these categories, and empirical evidence of their success, are discussed. The aim is to provide a succinct summary of the state-of-the-art, to illuminate both successful and unsuccessful interface strategies, and to identify potentially fruitful areas for further work.
---
paper_title: The quality of big (geo)data

paper_content:

Big data is distinguished by volume, velocity, and variety. A large proportion of all big data is likely to be geographically referenced, and much may be real time. While examples can be found of high-quality big data, problems arise in meeting the normal scientific standards of replicability and rigorous sampling. These standards can be relaxed in certain stages of science, during hypothesis generation and exploration. Three methods of quality improvement and assurance are proposed. Only the third is sufficiently robust and rapid, especially in time-critical situations.
---
paper_title: Area of Interest Based Interaction and GeoVisualization with WebGL

paper_content:

In this paper we present an experimental approach and a prototype implementation to manage level of detail in geovisualizations. The approach is based on identifying the area of interest of the user (mouse location or gaze point) and removing perceptually irrelevant detail in the visual field. Prototype implementation achieves this in real time using WebGL. By utilizing this test environment we want to systematically reveal the potential of integrating gaze based interaction modalities to the mobile webmapping applications.
---
paper_title: imMens: Real-time Visual Querying of Big Data

paper_content:

Data analysts must make sense of increasingly large data sets, sometimes with billions or more records. We present methods for interactive visualization of big data, following the principle that perceptual and interactive scalability should be limited by the chosen resolution of the visualized data, not the number of records. We first describe a design space of scalable visual summaries that use data reduction methods (such as binned aggregation or sampling) to visualize a variety of data types. We then contribute methods for interactive querying (e.g., brushing & linking) among binned plots through a combination of multivariate data tiles and parallel query processing. We implement our techniques in imMens, a browser-based visual analysis system that uses WebGL for data processing and rendering on the GPU. In benchmarks imMens sustains 50 frames-per-second brushing & linking among dozens of visualizations, with invariant performance on data sizes ranging from thousands to billions of records.
---
paper_title: Towards (Re)Constructing Narratives from Georeferenced Photographs through Visual Analytics

paper_content:

AbstractWe present a study that explores methodological steps towards (re)constructing collective narratives from the photo-taking behaviour of two groups (foreign tourists and inhabitants of Switzerland) by analysing spatial and temporal patterns in user-contributed, georeferenced photographs of Zurich, Switzerland. We reason that the photographers typically capture a scene or a moment because they want to remember or share it, thus these scenes or moments are meaningful to them. Various scholars suggest that the human experience (i.e. this meaningfulness) is what separates a place from the mathematical descriptions of space. While this notion is well known in larger geographic literature, it is under-explored in cartographic research. We respond to this research gap and reconstruct static and dynamic patterns of photo-taking and -sharing behaviour to assist in capturing the implicit meaning in the studied locations. These locations may be meaningful to only a certain group of people in certain moments; ...
---
paper_title: Space-variant image coding for stereoscopic media

paper_content:

This paper presents a brief overview of space variant image coding for stereoscopic media and reports on findings from a study using foveation for stereoscopic imaging. Foveation is a perceptually motivated approach to image coding based on the structure of human fovea and it is well studied in image and video processing domains. However it is less exploited for three-dimensional (3D) space even though it is potentially well suited also for 3D, e.g. for level of detail management in gaze contingent stereoscopic displays. In this paper we present results from a stereoscopic foveation implementation to test this argument. A brief discussion on computational as well as human factors for successful management and presentation of stereoscopic media is also provided based on current literature.
---
paper_title: High Quality Geographic Services and Bandwidth Limitations

paper_content:

In this paper we provide a critical overview of the state of the art in human-centric intelligent data management approaches for geographic visualizations when we are faced with bandwidth limitations. These limitations often force us to rethink how we design displays for geographic visualizations. We need ways to reduce the amount of data to be visualized and transmitted. This is partly because modern instruments effortlessly produce large volumes of data and Web 2.0 further allows bottom-up creation of rich and diverse content. Therefore, the amount of information we have today for creating useful and usable cartographic products is higher than ever before. However, how much of it can we really use online? To answer this question, we first calculate the bandwidth needs for geographic data sets in terms of waiting times. The calculations are based on various data volumes estimated by scholars for different scenarios. Documenting the waiting times clearly demonstrates the magnitude of the problem. Following this, we summarize the current hardware and software solutions, then the current human-centric design approaches trying to address the constraints such as various screen sizes and information overload. We also discuss a limited set of social issues touching upon the digital divide and its implications. We hope that our systematic documentation and critical review will help researchers and practitioners in the field to better understand the current state of the art.
---
paper_title: VAST Challenge 2012: Visual Analytics for Big Data

paper_content:

The 2012 Visual Analytics Science and Technology (VAST) Challenge posed two challenge problems for participants to solve using a combination of visual analytics software and their own analytic reasoning abilities. Challenge 1 (C1) involved visualizing the network health of the fictitious Bank of Money to provide situation awareness and identify emerging trends that could signify network issues. Challenge 2 (C2) involved identifying the issues of concern within a region of the Bank of Money network experiencing operational difficulties utilizing the provided network logs. Participants were asked to analyze the data and provide solutions and explanations for both challenges. The data sets were downloaded by nearly 1100 people by the close of submissions. The VAST Challenge received 40 submissions with participants from 12 different countries, and 14 awards were given.
---
paper_title: Beyond the Geotag: Situating 'Big Data' and Leveraging the Potential of the Geoweb

paper_content:

This paper presents an overview and initial results of geoweb analysis performed by our group as the foundation for a continued discussion of the potential impacts of "big" geodata for the practice of critical human geography. While Haklay’s (2012a) observation that social media content is generated by a tiny fraction of participants ("outliers") is correct, we explore the methods and limits of going beyond geotagged datasets to overcome these issues. The results suggest a cautious approach towards the use of big geodata that is as mindful of its shortcomings as its potential.The principal case study focuses on the widely reported riots following the University of Kentucky men's basketball team’s 2012 championship, and its manifestation within the geoweb. Drawing upon a Twitter database we have been developing — which among other things has collected all geo-tagged tweets (about 5 million a day) since December 2011 — we analyze the geography of tweets that used a specific hashtag (#LexingtonPoliceScanner). The hashtag refers to the online feed of the Lexington Police Department (LPD) and by itself sheds light on spatially-determinable events as news of them diffuses over time and space.We propose five extensions to the typical practice of mapping georeferenced data that we call going beyond the geotag: (1) examining social media that is not explicitly geographic; (2) spatialities beyond the "here and now"; (3) going beyond the proximate; (4) going beyond the human to data produced by bots and automated systems and (5) leveraging tweets against ancillary data (such as news reports and census data). These extensions create an "information-amplifier" effect that — at least partially — overcomes the partiality of geoweb social media.We discuss our effort to develop a geoweb big data analytic engine which provides basic geovisualization functionality for a range of geo-tagged big data, enabling the combination of more conventional, “top-down” datasets or qualitative methods with user-generated geo-data.
---
paper_title: Using social media to quantify nature-based tourism and recreation

paper_content:

Scientists have traditionally studied recreation in nature by conducting surveys at entrances to major attractions such as national parks. This method is expensive and provides limited spatial and temporal coverage. A new source of information is available from online social media websites such as flickr. Here, we test whether this source of “big data” can be used to approximate visitation rates. We use the locations of photographs in flickr to estimate visitation rates at 836 recreational sites around the world, and use information from the profiles of the photographers to derive travelers' origins. We compare these estimates to empirical data at each site and conclude that the crowd-sourced information can indeed serve as a reliable proxy for empirical visitation rates. This new approach offers opportunities to understand which elements of nature attract people to locations around the globe, and whether changes in ecosystems will alter visitation rates.
---
paper_title: The Big Picture for Big Data: Visualization

paper_content:

To convert information from massive data sets into insights, data centers will need to support the humans who are trying to make sense of it all. Fortunately, innovations in information visualization are demonstrating that a good user interface is worth a thousand petabytes (2013 Visualization
---
paper_title: Big data and human geography : Opportunities , challenges and risks

paper_content:

We are entering an era of big data – data sets that are characterised by high volume, velocity, variety, exhaustivity, resolution and indexicality, relationality and flexibility. Much of these data are spatially and temporally referenced and offer many possibilities for enhancing geographical understanding, including for post-positivist scholars. Big data also, however, poses a number of challenges and risks to geographic scholarship and raises a number of taxing epistemological, methodological and ethical questions. Geographers need to grasp the opportunities whilst at the same time tackling the challenges, ameliorating the risks and thinking critically about big data as well as conducting big data studies. Failing to do so could be quite costly as the discipline gets left behind as others leverage insights from the growing data deluge.
---
paper_title: Customizing Computational Methods for Visual Analytics with Big Data

paper_content:

The volume of available data has been growing exponentially, increasing data problem's complexity and obscurity. In response, visual analytics (VA) has gained attention, yet its solutions haven't scaled well for big data. Computational methods can improve VA's scalability by giving users compact, meaningful information about the input data. However, the significant computation time these methods require hinders real-time interactive visualization of big data. By addressing crucial discrepancies between these methods and VA regarding precision and convergence, researchers have proposed ways to customize them for VA. These approaches, which include low-precision computation and iteration-level interactive visualization, ensure real-time interactive VA for big data.
---
paper_title: SksOpen: Efficient Indexing, Querying, and Visualization of Geo-spatial Big Data

paper_content:

With the fast growing use of web-based map services, the performance of indexing and querying of location-based data is becoming a critical quality of service aspect. Spatial indexing is typically time-consuming and is not available to end-users. To address this challenge, we have developed and open-sourced an Online Indexing and Querying System for Big Geospatial Data, sksOpen. Integrated with the TerraFly Geospatial database [1], TerraFly sksOpen is an efficient indexing and query engine for processing Top-k Spatial Boolean Queries. Further, we provide ergonomic visualization of query results on interactive maps to facilitate the user's data analysis.
---
paper_title: The cognitive science of visual-spatial displays: implications for design.

paper_content:

This paper reviews cognitive science perspectives on the design of visual-spatial displays and introduces the other papers in this topic. It begins by classifying different types of visual-spatial displays, followed by a discussion of ways in which visual-spatial displays augment cognition and an overview of the perceptual and cognitive processes involved in using displays. The paper then argues for the importance of cognitive science methods to the design of visual displays and reviews some of the main principles of display design that have emerged from these approaches to date. Cognitive scientists have had good success in characterizing the performance of well-defined tasks with relatively simple visual displays, but many challenges remain in understanding the use of complex displays for ill-defined tasks. Current research exemplified by the papers in this topic extends empirical approaches to new displays and domains, informs the development of general principles of graphic design, and addresses current challenges in display design raised by the recent explosion in availability of complex data sets and new technologies for visualizing and interacting with these data.
---
paper_title: Visual analytics for the big data era — A comparative review of state-of-the-art commercial systems

paper_content:

Visual analytics (VA) system development started in academic research institutions where novel visualization techniques and open source toolkits were developed. Simultaneously, small software companies, sometimes spin-offs from academic research institutions, built solutions for specific application domains. In recent years we observed the following trend: some small VA companies grew exponentially; at the same time some big software vendors such as IBM and SAP started to acquire successful VA companies and integrated the acquired VA components into their existing frameworks. Generally the application domains of VA systems have broadened substantially. This phenomenon is driven by the generation of more and more data of high volume and complexity, which leads to an increasing demand for VA solutions from many application domains. In this paper we survey a selection of state-of-the-art commercial VA frameworks, complementary to an existing survey on open source VA tools. From the survey results we identify several improvement opportunities as future research directions.
---
paper_title: Big-Data Visualization

paper_content:

Almost all fields of study and practice sooner or later will confront the big-data problem. Visualization has proven effective for not only presenting essential information in vast amounts of data but also driving complex analyses. Big-data analytics and discovery present new research opportunities to the computer graphics and visualization community. This special issue highlights the latest advancements in solving the big-data problem through visual means, with four articles on new techniques, systems, or applications.
---
paper_title: Q&A: The data visualizer

paper_content:

Aaron Koblin, head of the Data Arts Team in Google's Creative Lab, uses data visualization and crowdsourcing to reveal the changing relationship between people and technology. As he presents his work at the Eyeo Festival of digital creativity and prepares to release a collaboration with Google, London's Tate Modern and artist Chris Milk, he talks about the beauty of big data.
---
paper_title: The Australia urban research gateway

paper_content:

The $20m Australian Urban Research Infrastructure Network (AURIN) project (www.aurin.org.au) began in July 2010. AURIN has been tasked with developing a secure,Web-based virtual environment (e-Infrastructure) offering seamless, secure access to diverse, distributed and extremely heterogeneous data sets from numerous agencies with an extensive portfolio of targeted analytical and visualization tools. This is being provisioned for Australia-wide urban and built environment researchers – itself a highly heterogeneous collection of research communities with diverse demands, through a unified urban research gateway. This paper describes these demands and how the e-Infrastructure and gateway is being designed and implemented to accommodate this diversity of requirements, both from the user/researcher perspective and from the data provider perspective. The scaling of the infrastructure is presented and the way in which it copes with the spectrum of big data challenges (volume, veracity, variability and velocity) and associated big data analytics. The utility of the e-Infrastructure is also demonstrated through a range of scenarios illustrating and reflecting the interdisciplinary urban research now possible.
---
paper_title: Big data: Distilling meaning from data

paper_content:

Buried in vast streams of data are clues to new science. But we may need to craft new lenses to see them, explain Felice Frankel and Rosalind Reid.
---
paper_title: Choosing and using geospatial displays: effects of design on performance and metacognition.

paper_content:

Interactive display systems give users flexibility to tailor their visual displays to different tasks and situations. However, in order for such flexibility to be beneficial, users need to understand how to tailor displays to different tasks (to possess "metarepresentational competence"). Recent research suggests that people may desire more complex and realistic displays than are most effective (Smallman & St. John, 2005). In Experiment 1, undergraduate students were tested on a comprehension task with geospatial displays (weather maps) that varied in the number of extraneous variables displayed. Their metacognitive judgments about the relative effectiveness of the displays were also solicited. Extraneous variables slowed response time and increased errors, but participants favored complex maps that looked more realistic about one third of the time. In Experiment 2, the eye fixations of undergraduate students were monitored as they performed the comprehension task. Complex maps that looked more realistic led to more eye fixations on both task-relevant and task-irrelevant regions of the displays. Experiment 3 compared performance of experienced meteorologists and undergraduate students on the comprehension and metacognitive tasks. Meteorologists were as likely as undergraduate students to prefer geographically complex (realistic) displays and more likely than undergraduates to opt for displays that added extraneous weather variables. However, meteorologists were also slower and less accurate with complex than with simple displays. This work highlights the importance of empirically testing principles of visual display design and suggests some limits to metarepresentational competence.
---
paper_title: Random forests for classification in ecology.

paper_content:

Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature.
---
paper_title: Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions

paper_content:

Most literature on short-term traffic flow forecasting focused mainly on normal, or non-incident, conditions and, hence, limited their applicability when traffic flow forecasting is most needed, i.e., incident and atypical conditions. Accurate prediction of short-term traffic flow under atypical conditions, such as vehicular crashes, inclement weather, work zone, and holidays, is crucial to effective and proactive traffic management systems in the context of intelligent transportation systems (ITS) and, more specifically, dynamic traffic assignment (DTA). To this end, this paper presents an application of a supervised statistical learning technique called Online Support Vector machine for Regression, or OL-SVR, for the prediction of short-term freeway traffic flow under both typical and atypical conditions. The OL-SVR model is compared with three well-known prediction models including Gaussian maximum likelihood (GML), Holt exponential smoothing, and artificial neural net models. The resultant performance comparisons suggest that GML, which relies heavily on the recurring characteristics of day-to-day traffic, performs slightly better than other models under typical traffic conditions, as demonstrated by previous studies. Yet OL-SVR is the best performer under non-recurring atypical traffic conditions. It appears that for deployed ITS systems that gear toward timely response to real-world atypical and incident situations, OL-SVR may be a better tool than GML.
---
paper_title: Mining Association Rules in Spatio-Temporal Data : An Analysis of Urban Socioeconomic and Land Cover Change

paper_content:

This research demonstrates the application of association rule mining to spatio-temporal data. Association rule mining seeks to discover associations among transactions encoded in a database. An association rule takes the form A → B where A (the antecedent) and B (the consequent) are sets of predicates. A spatio-temporal association rule occurs when there is a spatio-temporal relationship in the antecedent or consequent of the rule. As a case study, association rule mining is used to explore the spatial and temporal relationships among a set of variables that characterize socioeconomic and land cover change in the Denver, Colorado, USA region from 1970–1990. Geographic Information Systems (GIS)-based data pre-processing is used to integrate diverse data sets, extract spatio-temporal relationships, classify numeric data into ordinal categories, and encode spatio-temporal relationship data in tabular format for use by conventional (non-spatio-temporal) association rule mining software. Multiple level association rule mining is supported by the development of a hierarchical classification scheme (concept hierarchy) for each variable. Further research in spatiotemporal association rule mining should address issues of data integration, data classification, the representation and calculation of spatial relationships, and strategies for finding ‘interesting’ rules.
---
paper_title: Space-Time Geostatistics for Geography: A Case Study of Radiation Monitoring Across Parts of Germany

paper_content:

Many branches within geography deal with variables that vary not only in space but also in time. Therefore, conventional geostatistics needs to be extended with methods that estimate and quantify spatiotemporal variation and use it in spatiotemporal interpolation and stochastic simulation. This article briefly summarizes the main concepts of space–time geostatistics. Kriging in space and time can be done in much the same way as it is in a purely spatial setting. The main difficulties are in defining a realistic stochastic model that is assumed to have generated data and in characterizing and estimating the space–time correlation of that model. This article uses a model-based geostatistical approach to characterize space–time variability. The space–time variable of interest is treated as a sum of independent stationary spatial, temporal, and spatiotemporal components, which leads to a sum-metric space–time variogram model. Methods are illustrated with a case study of space–time interpolation of monthly averages of detected background radiation for a 5-year period in four German states.
---
paper_title: Grid-enabling Geographically Weighted Regression: A Case Study of Participation in Higher Education in England

paper_content:

Geographically Weighted Regression (GWR) is a method of spatial statistical analysis used to explore geographical differences in the effect of one or more predictor variables upon a response variable. However, as a form of local analysis, it does not scale well to (especially) large data sets because of the repeated processes of fitting and then comparing multiple regression surfaces. A solution is to make use of developing grid infrastructures, such as that provided by the National Grid Service (NGS) in the UK, treating GWR as an “embarrassing parallel” problem and building on existing software platforms to provide a bridge between an open source implementation of GWR (in R) and the grid system. To demonstrate the approach, we apply it to a case study of participation in Higher Education, using GWR to detect spatial variation in social, cultural and demographic indicators of participation.
---
paper_title: Anomaly detection: A survey

paper_content:

Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.
---
paper_title: Machine Learning for Spatial Environmental Data: Theory, Applications, and Software

paper_content:

PREFACE LEARNING FROM GEOSPATIAL DATA Problems and important concepts of machine learning Machine learning algorithms for geospatial data Contents of the book Software description Short review of the literature EXPLORATORY SPATIAL DATA ANALYSIS PRESENTATION OF DATA AND CASE STUDIES Exploratory spatial data analysis Data pre-processing Spatial correlations: Variography Presentation of data k-Nearest neighbours algorithm: a benchmark model for regression and classification Conclusions to chapter GEOSTATISTICS Spatial predictions Geostatistical conditional simulations Spatial classification Software Conclusions ARTIFICIAL NEURAL NETWORKS Introduction Radial basis function neural networks General regression neural networks Probabilistic neural networks Self-organising maps Gaussian mixture models and mixture density network Conclusions SUPPORT VECTOR MACHINES AND KERNEL METHODS Introduction to statistical learning theory Support vector classification Spatial data classification with SVM Support vector regression Advanced topics in kernel methods REFERENCES INDEX
---
paper_title: Specification and estimation of spatial panel data models

paper_content:

This article provides a survey of the specification and estimation of spatial panel data models. These models include spatial error autocorrelation, or the specification is extended with a spatially lagged dependent variable. In particular, the author focuses on the specification and estimation of four panel data models commonly used in applied research: the fixed effects model, the random effects model, the fixed coefficients model, and the random coefficients model. The survey discusses the asymptotic properties of the estimators and provides guidance with respect to the estimation procedures, which should be useful for practitioners.
---
paper_title: Creating the UK National Statistics 2001 output area classification

paper_content:

Summary. The paper describes the creation of the Office for National Statistics 2001 output area classification, which was created in collaboration with the authors.The classification places each 2001 census output area into one of seven clusters based on the socio-economic attributes of the residents of each area. The classification uses cluster analysis to reduce 41 census variables to a single socio-economic indicator.The classification was made available with a host of supporting and descriptive information as a National Statistic via National Statistics on line. The classification forms part of a suite of area classifications that were produced by the Office for National Statistics from 2001 census data. Classifications of local authorities, statistical wards and health areas are also available.
---
paper_title: Mining association rules between sets of items in large databases

paper_content:

We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer management and novel estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company, which shows the effectiveness of the algorithm.
---
paper_title: Head/Tail Breaks: A New Classification Scheme for Data with a Heavy-Tailed Distribution

paper_content:

This paper introduces a new classification scheme - head/tail breaks - in order to find groupings or hierarchy for data with a heavy-tailed distribution. The heavy-tailed distributions are heavily ...
---
paper_title: Geospatial analysis requires a different way of thinking : the problem of spatial heterogeneity

paper_content:

Geospatial analysis is very much dominated by a Gaussian way of thinking, which assumes that things in the world can be characterized by a well-defined mean, i.e., things are more or less similar in size. However, this assumption is not always valid. In fact, many things in the world lack a well-defined mean, and therefore there are far more small things than large ones. This paper attempts to argue that geospatial analysis requires a different way of thinking—a Paretian way of thinking that underlies skewed distribution such as power laws, Pareto and lognormal distributions. I review two properties of spatial dependence and spatial heterogeneity, and point out that the notion of spatial heterogeneity in current spatial statistics is only used to characterize local variance of spatial dependence. I subsequently argue for a broad perspective on spatial heterogeneity, and suggest it be formulated as a scaling law. I further discuss the implications of Paretian thinking and the scaling law for better understanding of geographic forms and processes, in particular while facing massive amounts of social media data. In the spirit of Paretian thinking, geospatial analysis should seek to simulate geographic events and phenomena from the bottom up rather than correlations as guided by Gaussian thinking.
---
paper_title: Head/tail breaks for visualization of city structure and dynamics

paper_content:

The things surrounding us vary dramatically, which implies that there are far more small things than large ones, e.g., far more small cities than large ones in the world. This dramatic variation is ...
---
paper_title: The Fractal Geometry of Nature

paper_content:

"...a blend of erudition (fascinating and sometimes obscure historical minutiae abound), popularization (mathematical rigor is relegated to appendices) and exposition (the reader need have little knowledge of the fields involved) ...and the illustrations include many superb examples of computer graphics that are works of art in their own right." Nature
---
paper_title: Ht-Index for Quantifying the Fractal or Scaling Structure of Geographic Features

paper_content:

Although geographic features, such as mountains and coastlines, are fractal, some studies have claimed that the fractal property is not universal. This claim, which is false, is mainly attributed to the strict definition of fractal dimension as a measure or index for characterizing the complexity of fractals. In this paper, we propose an alternative, the ht-index, to quantify the fractal or scaling structure of geographic features. A geographic feature has ht-index h if the pattern of far more small things than large ones recurs (h-1) times at different scales. The higher the ht-index, the more complex the geographic feature. We conduct three case studies to illustrate how the computed ht-indices capture the complexity of different geographic features. We further discuss how the ht-index is complementary to fractal dimension, and elaborate on a dynamic view behind the ht-index that enables better understanding of geographic forms and processes.
---
paper_title: The Misbehavior of Markets: A Fractal View of Risk, Ruin, and Reward

paper_content:

This international bestseller, which foreshadowed a market crash, explains why it could happen again if we don’t act now. Fractal geometry is the mathematics of roughness: how to reduce the outline of a jagged leaf or static in a computer connection to a few simple mathematical properties. With his fractal tools, Mandelbrot has got to the bottom of how financial markets really work. He finds they have a shifting sense of time and wild behaviour that makes them volatile, dangerous - and beautiful. In his models, the complex gyrations of the FTSE 100 and exchange rates can be reduced to straightforward formulae that yield a much more accurate description of the risks involved.
---
paper_title: Fractal Cities: A Geometry of Form and Function

paper_content:

Planners and architects will find that many aspects of fractal geometry covered in this book are relevant to their own interests.
---
paper_title: Nonlinear Black-Box Models in System Identification: Mathematical Foundations

paper_content:

We discuss several aspects of the mathematical foundations of the nonlinear black-box identification problem. We shall see that the quality of the identification procedure is always a result of a certain trade-off between the expressive power of the model we try to identify (the larger the number of parameters used to describe the model, the more flexible is the approximation), and the stochastic error (which is proportional to the number of parameters). A consequence of this trade-off is the simple fact that a good approximation technique can be the basis of a good identification algorithm. From this point of view, we consider different approximation methods, and pay special attention to spatially adaptive approximants. We introduce wavelet and ‘neuron’ approximations, and show that they are spatially adaptive. Then we apply the acquired approximation experience to estimation problems. Finally, we consider some implications of these theoretical developments for the practically implemented versions of the ‘spatially adaptive’ algorithms.
---
paper_title: Exploring the efficiency of users' visual analytics strategies based on sequence analysis of eye movement recordings

paper_content:

Visual analytics is often based on the intuition that highly interactive and dynamic depictions of complex and multivariate databases amplify human capabilities for inference and decision-making, as they facilitate cognitive tasks such as pattern recognition, association, and analytical reasoning (Thomas and Cook 2005). But how do we know whether visual analytics really works? This article offers a generic evaluation approach combining theory-and data-driven methods based on sequence similarity analysis. The approach systematically studies users' visual interaction strategies when using highly interactive interfaces. We specifically ask whether the efficiency (i.e., speed) of users can be characterized by specific display interaction event sequences, and whether studying user strategies could be employed to improve the (interaction) design of the dynamic displays. We showcase our approach using a very large, fine-grained spatiotemporal dataset of eye movement recordings collected during a controlled human subject experiment with dynamic visual analytics displays. With this methodological approach based on empirical evidence, we hope to contribute to a deeper understanding of how people make inferences and decisions with highly interactive visualization tools and complex displays.
---
paper_title: Cognitive And Usability Issues In Geovisualization

paper_content:

We provide a research agenda for the International Cartographic Association's Commission on Visualization and Virtual Environment Working Group on Cognitive and Usability Issues in Geovisualization. Developments in hardware and software have led to (and will continue to stimulate) numerous novel methods for visualizing geospatial data. It is our belief that these novel methods will be of little use if they are not developed within a theoretical cognitive framework and iteratively tested using usability engineering principles. We argue that cognitive and usability issues should be considered in the context of six major research themes: 1) geospatial virtual environments (GeoVEs); 2) dynamic representations (including animated and interactive maps); 3) metaphors and schemata in user interface design; 4) individual and group differences; 5) collaborative geovisualization; and 6) evaluating the effectiveness of geovisualization methods. A key point underlying our use of theoretical cognitive principles is tha...
---
paper_title: Design and Implementation of a Model, Web-based, GIS- Enabled Cancer Atlas

paper_content:

AbstractThe design and development of a highly interactive web-based, GIS-enabled atlas is reported. The atlas is a prototype, designed as a model for implementation of atlases to support government cancer-control activities. This model integrates symbolisation and design principles from print cartography, interaction strategies from exploratory geovisualisation, and web-map/web-feature service advances from GIS. The atlas has been implemented using a client-server architecture. It makes use of two open-source GIS tools, PostGIS (as the system database) and GeoServer (to connect the database to the client mapping application). The client mapping application has been built in Macromedia Flash. The entire client-server architecture is described, then direct primary emphasis is focused on the client mapping application. For this component of the system, the interface design strategy is detailed, the approach taken to implement this strategy in Flash is documented, and the mechanisms developed to build dynami...
---
paper_title: Geospatial analysis requires a different way of thinking : the problem of spatial heterogeneity

paper_content:

Geospatial analysis is very much dominated by a Gaussian way of thinking, which assumes that things in the world can be characterized by a well-defined mean, i.e., things are more or less similar in size. However, this assumption is not always valid. In fact, many things in the world lack a well-defined mean, and therefore there are far more small things than large ones. This paper attempts to argue that geospatial analysis requires a different way of thinking—a Paretian way of thinking that underlies skewed distribution such as power laws, Pareto and lognormal distributions. I review two properties of spatial dependence and spatial heterogeneity, and point out that the notion of spatial heterogeneity in current spatial statistics is only used to characterize local variance of spatial dependence. I subsequently argue for a broad perspective on spatial heterogeneity, and suggest it be formulated as a scaling law. I further discuss the implications of Paretian thinking and the scaling law for better understanding of geographic forms and processes, in particular while facing massive amounts of social media data. In the spirit of Paretian thinking, geospatial analysis should seek to simulate geographic events and phenomena from the bottom up rather than correlations as guided by Gaussian thinking.
---
paper_title: Grid-enabling Geographically Weighted Regression: A Case Study of Participation in Higher Education in England

paper_content:

Geographically Weighted Regression (GWR) is a method of spatial statistical analysis used to explore geographical differences in the effect of one or more predictor variables upon a response variable. However, as a form of local analysis, it does not scale well to (especially) large data sets because of the repeated processes of fitting and then comparing multiple regression surfaces. A solution is to make use of developing grid infrastructures, such as that provided by the National Grid Service (NGS) in the UK, treating GWR as an “embarrassing parallel” problem and building on existing software platforms to provide a bridge between an open source implementation of GWR (in R) and the grid system. To demonstrate the approach, we apply it to a case study of participation in Higher Education, using GWR to detect spatial variation in social, cultural and demographic indicators of participation.
---
paper_title: The Geotemporal Demographics of Twitter Usage

paper_content:

This paper presents a preliminary empirical evaluation of the strategic importance of infusing Twitter social media data into classifications of small areas, as a way of moving beyond the nighttime residential geographies of conventional geodemographic classifications. We attempt an empirically based critique of the merits and drawbacks of the use of social media data, in which the value of high spatial and temporal granularity of revealed activity patterns is contrasted with the paucity of individual attribute information. We apply new and novel methods to enrich the profiles of Twitter users in order to generalize about activity patterns in London, our case-study city. More insidious problems in the use of social media data arise from the as-yet-unknown sources and operation of bias in their user bases. Our contribution is to begin to identify and assess the biases inherent in social media usage in social research, and use these to evaluate their deployment in research applications.
---
paper_title: An Empirically-Derived Taxonomy of Interaction Primitives for Interactive Cartography and Geovisualization

paper_content:

Proposals to establish a 'science of interaction' have been forwarded from Information Visualization and Visual Analytics, as well as Cartography, Geovisualization, and GIScience. This paper reports on two studies to contribute to this call for an interaction science, with the goal of developing a functional taxonomy of interaction primitives for map-based visualization. A semi-structured interview study first was conducted with 21 expert interactive map users to understand the way in which map-based visualizations currently are employed. The interviews were transcribed and coded to identify statements representative of either the task the user wished to accomplish (i.e., objective primitives) or the interactive functionality included in the visualization to achieve this task (i.e., operator primitives). A card sorting study then was conducted with 15 expert interactive map designers to organize these example statements into logical structures based on their experience translating client requests into interaction designs. Example statements were supplemented with primitive definitions in the literature and were separated into two sorting exercises: objectives and operators. The objective sort suggested five objectives that increase in cognitive sophistication (identify, compare, rank, associate, & delineate), but exhibited a large amount of variation across participants due to consideration of broader user goals (procure, predict, & prescribe) and interaction operands (space-alone, attributes-in-space, & space-in-time; elementary & general). The operator sort suggested five enabling operators (import, export, save, edit, & annotate) and twelve work operators (reexpress, arrange, sequence, resymbolize, overlay, pan, zoom, reproject, search, filter, retrieve, & calculate). This taxonomy offers an empirically-derived and ecologically-valid structure to inform future research and design on interaction.
---
paper_title: A general-purpose parallel raster processing programming library test application using a geographic cellular automata model

paper_content:

A general-purpose parallel raster processing programming library (pRPL) was developed and applied to speed up a commonly used cellular automaton model with known tractability limitations. The library is suitable for use by geographic information scientists with basic programming skills, but who lack knowledge and experience of parallel computing and programming. pRPL is a general-purpose programming library that provides generic support for raster processing, including local-scope, neighborhood-scope, regional-scope, and global-scope algorithms as long as they are parallelizable. The library also supports multilayer algorithms. Besides the standard data domain decomposition methods, pRPL provides a spatially adaptive quad-tree-based decomposition to produce more evenly distributed workloads among processors. Data parallelism and task parallelism are supported, with both static and dynamic load-balancing. By grouping processors, pRPL also supports data-task hybrid parallelism, i.e., data parallelism within a processor group and task parallelism among processor groups. pSLEUTH, a parallel version of a well-known cellular automata model for simulating urban land-use change (SLEUTH), was developed to demonstrate full utilization of the advanced features of pRPL. Experiments with real-world data sets were conducted and the performance of pSLEUTH measured. We conclude not only that pRPL greatly reduces the development complexity of implementing a parallel raster-processing algorithm, it also greatly reduces the computing time of computationally intensive raster-processing algorithms, as demonstrated with pSLEUTH.
---
paper_title: Customizing Computational Methods for Visual Analytics with Big Data

paper_content:

The volume of available data has been growing exponentially, increasing data problem's complexity and obscurity. In response, visual analytics (VA) has gained attention, yet its solutions haven't scaled well for big data. Computational methods can improve VA's scalability by giving users compact, meaningful information about the input data. However, the significant computation time these methods require hinders real-time interactive visualization of big data. By addressing crucial discrepancies between these methods and VA regarding precision and convergence, researchers have proposed ways to customize them for VA. These approaches, which include low-precision computation and iteration-level interactive visualization, ensure real-time interactive VA for big data.
---
paper_title: CloST: a hadoop-based storage system for big spatio-temporal data analytics

paper_content:

During the past decade, various GPS-equipped devices have generated a tremendous amount of data with time and location information, which we refer to as big spatio-temporal data. In this paper, we present the design and implementation of CloST, a scalable big spatio-temporal data storage system to support data analytics using Hadoop. The main objective of CloST is to avoid scan the whole dataset when a spatio-temporal range is given. To this end, we propose a novel data model which has special treatments on three core attributes including an object id, a location and a time. Based on this data model, CloST hierarchically partitions data using all core attributes which enables efficient parallel processing of spatio-temporal range scans. According to the data characteristics, we devise a compact storage structure which reduces the storage size by an order of magnitude. In addition, we proposes scalable bulk loading algorithms capable of incrementally adding new data into the system. We conduct our experiments using a very large GPS log dataset and the results show that CloST has fast data loading speed, desirable scalability in query processing, as well as high data compression ratio.
---
paper_title: A task analysis approach to the visualization of geographic data

paper_content:

Over the last decade two trends have been apparent; the first an increasing complexity and volume of environmental data and the second a rapid development of computer technology. The first trend would be overwhelming if it were not for the second but this convergence of the need for information tools and the potential to provide them has also dramatically increased the need to provide the human user of such systems with intuitive software support. ::: Scientific visualization is an emerging discipline which holds considerable promise for geographers examining diverse, n-dimensional data sets. Incorporating scientific visualization systems (SVSs) into the set of tools available to geographers requires a thoughtful approach to visual design and software requirements. Traditional approaches to requirements focus on functions or data, often leaving the human user to deal with cumbersome, ineffective software. As visual displays are of optimal support only when they directly support geographer's tasks, a task analysis methodology was developed and tested in this research with the objective of producing more intuitive designs and more user-oriented visualization software requirements. ::: The task analysis methodology developed and modified through this research was based on an examination of the fundamental concepts of task analysis, work on data classification in cartography and visualization, and research on visual operators or visual primitives. An initial task model was tested by the author and two independent designers in seven real-world cases with scientists from the U.S. Geological Survey and the Colorado State Drought Assessment Group. ::: The research produced two results. First, a final task analysis methodology comprising a task structure, task model, design model, and a list of primary visual operators. Second, a set of software requirements was developed through the implementation of designs created from the task analyses. The conclusions drawn from the research indicate that visualization techniques can indeed be of assistance to geographers but that the functions of generic SVSs must be tailored and enhanced, as indicated by the requirements list, for their full value to be realized.
---
paper_title: A parallel computing approach to fast geostatistical areal interpolation

paper_content:

Areal interpolation is the procedure of using known attribute values at a set of (source) areal units to predict unknown attribute values at another set of (target) units. Geostatistical areal interpolation employs spatial prediction algorithms, that is, variants of Kriging, which explicitly incorporate spatial autocorrelation and scale differences between source and target units in the interpolation endeavor. When all the available source measurements are used for interpolation, that is, when a global search neighborhood is adopted, geostatistical areal interpolation is extremely computationally intensive. Interpolation in this case requires huge memory space and massive computing power, even with the dramatic improvement introduced by the spectral algorithms developed by Kyriakidis et al. (2005. Improving spatial data interoperability using geostatistical support-to-support interpolation. In: Proceedings of geoComputation. Ann Arbor, MI: University of Michigan) and Liu et al. (2006. Calculation of average covariance using fast Fourier transform (FFT). Menlo Park, CA: Stanford Center for Reservoir Forecasting, Petroleum Engineering Department, Stanford University) based on the fast Fourier transform (FFT). In this study, a parallel FFT-based geostatistical areal interpolation algorithm was developed to tackle the computational challenge of such problems. The algorithm includes three parallel processes: (1) the computation of source-to-source and source-to-target covariance matrices by means of FFT; (2) the QR factorization of the source-to-source covariance matrix; and (3) the computation of source-to-target weights via Kriging, and the subsequent computation of predicted attribute values for the target supports. Experiments with real-world datasets (i.e., predicting population densities of watersheds from population densities of counties in the Eastern Time Zone and in the continental United States) showed that the parallel algorithm drastically reduced the computing time to a practical length that is feasible for actual spatial analysis applications, and achieved fairly high speed-ups and efficiencies. Experiments also showed the algorithm scaled reasonably well as the number of processors increased and as the problem size increased.
---
paper_title: The next generation of atlas user interfaces – A user study with “Digital Natives”

paper_content:

Atlases are one of the most complex geovisualization environments as they are very information-rich. Within these environments, a well-designed user interface is essential to explore the variety of atlas maps and media. Involving technology-affine digital natives in the interface design process seems self-evident to provide appealing and intuitively usable atlases in the future. In our study, we presented secondary school students (n = 110, age 14–15 years) with five graphical user interface (GUI) mock-ups varying in layout density and tool arrangement. Each alternative design embodies a GUI concept inspired by an existing Web atlas or a popular website. The students have completed five tasks in these atlas interfaces that represent typical use cases for thematic navigation, spatial orientation and information queries. We collected performance and preference metrics for each layout, i.e., the time to solve a task (efficiency), whether students found the correct answers (effectiveness), and their ratings of each layout for “attractiveness”. To complete the analysis, we also conducted a mouse click analysis. Results indicate that atlas interfaces with a medium layout density are strongly preferred by the tested participants, and through inferential statistics, by digital natives in general. These medium density layouts also perform significantly better; i.e., they have lower average times, lower number of clicks and a higher percentage of successfully completed tasks. Based on the interpretation of the results of this study, general and practical guidelines for future atlas user interfaces are derived.
---
paper_title: Visualizing and quantifying the movement of vegetative drought using remote-sensing data and GIS

paper_content:

Remote-sensing-based drought monitoring methods provide fast and useful information for a sustainable management strategy of drought impact over a region. Common pixel-based monitoring methods are limited in the analysis of the dynamics of this impact at regional scale. For instance, these hardly allow us to quantify the movement of drought in space and time and to compare drought with rainfall deficits without losing the variability of these events within a region. This study proposed an object-based approach that allowed us to visualize and quantify the spatio-temporal movement of drought impact on vegetation, called vegetative drought, in a region. The GIS software Dynomap was used to extract and track objects. Measures of distance and angle were used for determining the speed and direction of vegetative drought and rainfall deficit objects, calculated from the National Oceanic and Atmospheric Administration's (NOAA's) normalized difference vegetation index and rainfall estimates data. The methods were applied to the two rainy seasons during the drought year 1999 in East Africa. Results showed that vegetative drought objects moved into the southwestern direction at an average angle of −138.5° during the first season and −144.5° during the second season. The speed of objects varied between 38 km dekad−1 and 185 km dekad−1 during the first season and between 33 km dekad−1 and 144 km dekad−1 during the second season, reflecting the rate of spread between dekads. Vegetative drought objects close to rainfall deficit objects showed similar trajectories and sometimes regions overlapped. This indicated that the two events are related. We conclude that a spatiotemporal relationship existed between the two types of events and that this could be quantified.
---
paper_title: High-performance computing and geography: developments, issues, and case studies

paper_content:

In this paper we outline some of the results that were obtained by the application of a Cray T3D parallel supercomputer to human geography problems. We emphasise the fundamental importance of high-performance computing (HPC) as a future relevant paradigm for doing geography. We offer an introduction to recent developments and illustrate how new computational intelligence technologies can start to be used to make use of opportunities created by data riches from geographic information systems, artificial intelligence tools, and HPC in geography.
---
paper_title: Big Data:A Revolution that will Transform How we Live, Work and Think

paper_content:

In the first chapter, "Now" the authors explain the concept of "Big data". Big Data refers to capability to collect large amount of information, analyze it in real time, and draw thoughtfully astonishing conclusions. The authors present case studies of how Farecast, Google and Amazon are doing this, to impart better understanding of the concepts.
---
paper_title: Discovering data quality rules

paper_content:

Dirty data is a serious problem for businesses leading to incorrect decision making, inefficient daily operations, and ultimately wasting both time and money. Dirty data often arises when domain constraints and business rules, meant to preserve data consistency and accuracy, are enforced incompletely or not at all in application code. ::: ::: In this work, we propose a new data-driven tool that can be used within an organization's data quality management process to suggest possible rules, and to identify conformant and non-conformant records. Data quality rules are known to be contextual, so we focus on the discovery of context-dependent rules. Specifically, we search for conditional functional dependencies (CFDs), that is, functional dependencies that hold only over a portion of the data. The output of our tool is a set of functional dependencies together with the context in which they hold (for example, a rule that states for CS graduate courses, the course number and term functionally determines the room and instructor). Since the input to our tool will likely be a dirty database, we also search for CFDs that almost hold. We return these rules together with the non-conformant records (as these are potentially dirty records). ::: ::: We present effective algorithms for discovering CFDs and dirty values in a data instance. Our discovery algorithm searches for minimal CFDs among the data values and prunes redundant candidates. No universal objective measures of data quality or data quality rules are known. Hence, to avoid returning an unnecessarily large number of CFDs and only those that are most interesting, we evaluate a set of interest metrics and present comparative results using real datasets. We also present an experimental study showing the scalability of our techniques.
---
paper_title: Data quality: The other face of Big Data

paper_content:

In our Big Data era, data is being generated, collected and analyzed at an unprecedented scale, and data-driven decision making is sweeping through all aspects of society. Recent studies have shown that poor quality data is prevalent in large databases and on the Web. Since poor quality data can have serious consequences on the results of data analyses, the importance of veracity, the fourth ‘V’ of big data is increasingly being recognized. In this tutorial, we highlight the substantial challenges that the first three ‘V’s, volume, velocity and variety, bring to dealing with veracity in big data. Due to the sheer volume and velocity of data, one needs to understand and (possibly) repair erroneous data in a scalable and timely manner. With the variety of data, often from a diversity of sources, data quality rules cannot be specified a priori; one needs to let the “data to speak for itself” in order to discover the semantics of the data. This tutorial presents recent results that are relevant to big data quality management, focusing on the two major dimensions of (i) discovering quality issues from the data itself, and (ii) trading-off accuracy vs efficiency, and identifies a range of open problems for the community.
---
paper_title: Urban social vulnerability assessment with physical proxies and spatial metrics derived from air- and spaceborne imagery and GIS data

paper_content:

Risk management in urban planning is of increasing importance to mitigate the growing amount of damage and the increasing number of casualties caused by natural disasters. Risk assessment to support management requires knowledge about present and future hazards, elements at risk and different types of vulnerability. This article deals with the assessment of social vulnerability (SV). In the past this has frequently been neglected due to lack of data and assessment difficulties. Existing approaches for SV assessment, primarily based on community-based methods or on census data, have limited efficiency and transferability. In this article a new method based on contextual analysis of image and GIS data is presented. An approach based on proxy variables that were derived from high-resolution optical and laser scanning data was applied, in combination with elevation information and existing hazard data. Object-oriented image analysis was applied for the definition and estimation of those variables, focusing on SV indicators with physical characteristics. A reference Social Vulnerability Index (SVI) was created from census data available for the study area on a neighbourhood level and tested for parts of Tegucigalpa, Honduras. For the evaluation of the proxy-variables, a stepwise regression model to select the best explanatory variables for changes in the SVI was applied. Eight out of 47 variables explained almost 60% of the variance, whereby the slope position and the proportion of built-up area in a neighbourhood were found to be the most valuable proxies. This work shows that contextual segmentation-based analysis of geospatial data can substantially aid in SV assessment and, when combined with field-based information, leads to optimization in terms of assessment frequency and cost.
---

Title: Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges
Section 1: Introduction
Description 1: Briefly introduce the topic of geospatial big data, its significance, origins, and the unique characteristics that define big data.

Section 2: Quality Assessment
Description 2: Discuss the challenges of geospatial data quality, evaluation methods, and the particular issues such as uncertainty, asynchronicity, and incompleteness.

Section 3: Data Modelling and Structuring
Description 3: Describe the various spatial data models suitable for geospatial big data and discuss the efficiency of these models in handling large datasets.

Section 4: Functional programming for big data streams
Description 4: Explain the role and advantages of functional programming in the context of geospatial big data streams, including aspects like data races and parallel computing.

Section 5: Geospatial big data analytics
Description 5: Review the techniques and methods used for spatial data mining, including statistical and machine learning techniques, and identify their scalability with big data.

Section 6: Data Visualization and Visual Analytics
Description 6: Examine the importance and challenges of data visualization in geospatial big data, and discuss novel approaches and tools for visual analytics.

Section 7: Data Mining and Knowledge Discovery
Description 7: Explore traditional and modern methods for knowledge discovery from geospatial big data, including data mining techniques and the implications of fractals.

Section 8: Challenges, Open Issues and Future Directions
Description 8: Outline the key challenges, open issues, and future research directions in geospatial big data handling, as identified in the survey.

Section 9: Conclusions
Description 9: Summarize the findings of the review, highlight the main points, and provide final thoughts on the future of geospatial big data handling.