Good afternoon everybody. Hope you're all well. So prior to Nigel's piece on spatial ecology and COVID-19, which uses census data, I'm going to give you a very quick whistle stop tour of UK census data. I'm the head of the aggregate data unit within the UK data service and I've been involved with census data since 1995, so quite a long time. I did a placement year from university, the University of Manchester in the in the computing centre and then I'm glad to say that they took me back on and I've been working on census data ever since. So if we could have the next slide please. So we're going to start with a quick quiz. So in 2011, how many people in England and Wales identified their religion as Jedi? And if we get the next slide, we should have a way of recording this. So if you go to www.menti.com, use the code 4-8-6-8-4-6-0-0, we should get some answers. Okay, so we have a look at how you've got on with that. So census data is classic aggregate data. It's counts of things, people aggregated to a geography. Amazingly, the majority of people got that right. It is 6,632 and all of these people live amongst us. Next slide please. So the question on Jedi is probably not particularly useful to ourselves unless you're planning on founding a Jedi church. But what if the above diagram was your local area and the numbers represented households where you can identify single person households, people living alone? And then if you can add in the characteristics that the person living alone was over the age of 60, using that information, we can build a picture of where in the country we could target resources for looking at vulnerable people who might be shielding from COVID-19. Next slide please. So the census population in the UK is held every 10 years. The first modern census in the UK was in 1841 and the data that the census agencies hold is held completely anonymous and secure for 100 years and they are extremely rigorous in that security. We have digitized census from 1971. It says 1961 ongoing. That data is almost ready for release. We will have that as soon as it's available. Is this the 2021 census, the last ever census? They say it's going to be but then they said that the 2011 census was the last one that it was going to be. So we don't know. I personally think that there will be a 2021 census. Next slide please. We talk about a UK census but there is no such thing as a UK census. We have different nations, different governments and each of those governments drives the ideas for the census. In England and Wales we have the ONS. In Scotland we have NRS and in Northern Ireland we have NISRA. They all have different but similar geographies. They ask different questions at times. They look for different outputs from the census. However there is a lot of harmonization that goes on. The census agencies talk to each other and they do plan to have harmonization across most subjects. Next slide please. So the 2021 census for England and Wales held on the 21st of March. They've had a 97% response rate for that census which is unprecedented. I guess it shows that the online website they built for recording that was actually very good. They have this completion time for each census with just 23 minutes. And what we've got going on now, several months after the census, we've got a census coverage survey going on. So they're looking to see how well it was covered, what sorts of people responded and who didn't. We have got a census quality survey going on so they will randomly go and talk to people to see how they felt about filling in the census and also so we can cross-reference that and see if what they vote actually tell us with what the questioner can find out. And then we've also, we've just closed the, sorry, the ONS has just closed its output consultation. Next slide please. So a very similar thing going on in Northern Ireland. I think statistical disclosure control. They are still currently working on the best methods for ensuring that the data when it is released is not disclosed if you can't find individuals out from that. And the next one please. Scotland, Scotland haven't yet had their census that'll be taken place on the 1st of March. They suspended their census due to Covid. They have had a very successful rehearsal and the rehearsal of the data processing. And there's a lot of work going on to ensure that people are not counted twice or they're not counted all. So if they were in England and they moved to Scotland, they don't want to be on both censuses. If they were in Scotland, in Scotland in last year, well, yesterday when they moved to England tomorrow, they won't be on any census. So they want to make sure that people are counted somewhere. The next steps, lots of seeds, sorry, lots of seeds. So they're going to compile 3% of the census wasn't completed online. So that's a very small number, but it's still a lot of paper. So they're going to compile all the paper census returns. They're going to clean the data. They will, for instance, remove duplicate outputs. It does happen that, you know, someone in the house returns, completes a survey, sends it in, and then someone does it online. They're going to complete the census. So they want to have 100% coverage. So they're going to fill in the gaps where there's missed or invalid or inconsistent responses and estimate those who haven't responded using statistical methods. We're going to cross validate. So we'll be using alternative administrative data to QA the responses. Consultation. So they've consulted with local authorities, charities, community groups, commerce and ourselves on what we'd like to appear in, how we'd like the outputs to appear. And there's been a lot of work going on on confidentiality. So they're working tirelessly to protect anonymity. Next slide, please. Oh, sorry, no, we'll stay on that slide, sorry. So the outputs themselves should start to appear a year after census date. We haven't got a definite date on that. The first sets of figures will be headline figures. After that, we will get the data in tranches with the simplest data first with more complex, multivariate data towards the end. They're looking at a year to two years to get all the data out. So the data will appear in ready made tables, those are traditional census tables, tables that flex, so tables that you can tweak slightly, and also the ability to build your own tables. So you'll be able to specify the variables you want on that. And then it'll analyze the census database, make sure that none of the data you want is disclosed, if it is, it'll blur that data, and then it'll output that to you. They're also going to be linked to administrative data as well to fill in gaps or add extra value to the census outputs. Next slide, please. So what can census data tell us? All sorts. I'm not going to go through that list, but massive amounts of information. It's gold standard social survey data. It covers 100% of the population. No other social survey comes even close to that. It's for a single point in time. So it starts out extremely useful. After 10 years, you've got to start to think about how populations and lives have changed. Those categories of census data, they can be broken down into subclasses. They can be mixed with other topics, and it comes in a number. We have a number of different census products. So we have the aggregate data. We've got geographical boundary data so you can make maps. We've got flow data so we can look at where, how people travel to work and where they travel to work. We can look at how people are migrating within the UK and from the UK to abroad and vice versa. We've got micro data as well so we can start to look at individuals and anonymized individuals as well. Then we've got some derived data as well. So we've got some deprivation data as well, for instance, and spatio-temporal data. So things like in the past we've had POP247 which is looked at daytime and nighttime populations. Next slide, please. So in 2021 the census changed. It changes every time we do it, but in 2021 we've got new questions on gender. Northern Ireland didn't ask the gender question. So this is a voluntary question about how you identify your gender, as opposed to the sex that was registered at birth. Next slide, please. We had a question on sexuality in all the nations. Again, a voluntary question.  Next slide, please. In England, Wales, and Scotland we had a question on veteran status. So this is people who were in the armed forces but are no longer there. There are separate questions on armed forces personnel. This wasn't asked in Northern Ireland because it's still a sensitive topic. Next slide, please. We've got a new question on health conditions in Scotland and in Northern Ireland. So in England and Wales they just asked, do you have a long-term limiting illness and left it at that? Northern Ireland Scotland, they're asking what that illness is for Northern Ireland. It's a tick list in Scotland that's a writing. So that might be very telling with long COVID, for instance. So we look forward to seeing the results from that. Next slide, please. What the census can't tell us is there's no information on wealth income. So we derive our deprivation data using information like overcrowding, access to cars and vans, and socioeconomic grouping. There's no personal identification. They use data blurring and obfuscation to make sure that hopefully individuals cannot. Well, as far as I know, no individual has ever been identified from census samples. So in theory, if you're a hit man and your target is of Chinese origin living in Belfast but following the Sikh religion, it might be possible that you are the only person in your particular output area. So what we will do is they will add a random number to that one, two or three. And they might also swap your records with another similar type of output area. Next slide, please. Census geography in this country is a little bit of a mess. In fact, geography in this country is a little bit of a mess. Because we have four different nations and four different governments, we have four different sets of geographies. They all share the same building block, which is the output area, which is about, in England and Wales, about 125 households. However, in Scotland, it's about 50 households. So there is some difference there. From the output area, oh, the output area is a built after the census has been taken, because they are socioeconomically heterogeneous. So they're designed to have a similar population. Designed to not spill over obvious geographical impediments, so motorways, for instance, rivers, etc. They are aligned to local authority boundaries. So they don't split over, they don't split over into different local authority areas. From the output area, we create a set of geographies called super output areas, lower super output or middle super output areas in England and Wales, data zones and intermediate geographies in Scotland and in Northern Ireland, they just have lower super output areas. These geographies are supposed to be little changing over the years, so that you can use them for comparison with older censuses. None of those geographies particularly relate to anything real. No one gives their own criteria or their address. No one sends their local taxes to a data zone. So we also have on top of those, we have real geographies of regions, counties, local authorities, wards and electoral divisions, which have different names in all of the different nations. So there are no regions and counties and local authorities in Scotland. They have council areas. There is no postcard geography in the census outputs. There is an interface that we've created for matching and converting between geographies, and that is called Geocomvert, the address is there. I think we might talk a bit about that later. Okay, next slide please. So the boundary data, the data that we can use to create maps, we have all that at the place called UK Borders at Edinburgh, which is part of the UK Data Service. We've got data all the way back to 1981 in mapping for ship, file, KML, CSV, all the mostly most used mapping formats. One of our older interfaces, Casweb actually has 2001 and 1991 data boundaries bundled together. So if you get data out of there, you can actually have it in a mapping format all together, and if you don't have to do the work of joining your mapping data together. Next slide please. We have a number of different interfaces to the census record data, which has come about because we've been given money at different times to create new interfaces for new censors. Infuse for data from 2001 to 2011, and that includes a combined UK data set. Casweb for older census data, and Decan, it's currently only 2011, but we are loading 2001 into it at the moment. That's for bulk downloads of whole tables for whole nations, geographies, or whatever you want. That's what if you're doing bulk analysis. Next slide please. Deprivation data. There aren't any questions on income or wealth, so we use data from room occupancy, home ownership, tenancy, car availability, employment status, etc. to create a number of recipes, car stairs, town's end, and the IOD. They all use slightly different recipes for creating these indices. We have all that within geoconvert, so you can add deprivation data to your census data if you wish, or you can add it to postcode geography as well. I think that's all I've got to say about deprivation data. If you want to match geographies, so it tends to be that if you're taking social survey data or you're asking questionnaires of people, you're asking for postcodes. If you then want to match postcodes to census geographies, we have a tool called geoconvert, which uses while mail address points within geographies to calculate an area of your population and then proportion those that are up to larger geographies. You have to think about what you're attempting to do. Trying to aggregate data up to very high levels, there'll be very large errors that can happen. Also, postcodes change a lot, and we do have information in there about when postcodes start and when postcodes terminate. The royal mail re-uses postcode, so you just have to be careful when you're matching it to have a look and think, oh, there's an Aberdeen postcode here, but it's coming out for a Portsmouth census geography. We've got lots of documentation on geoconvert about how we can use that. Next slide, please. I think I've already mentioned actually deacon, bulk data. We've created it around census themes, very used to search, and has lots of metadata in there as well, and as we say, it's expanding all the time. I think, yeah, if we have any questions. So, what we're going to move on to is the ecological analysis. In starting to talk about this, I'm aware that people may be at very different stages in terms of understanding the ways that these methods, what underpins these methods. I'm going to go through some things which some of you may find easy and some of you may find quite complex, so I'm happy to answer questions as we go along, specifically about the contents of this, but there'll be an opportunity at the end to wrap up as well. So, if I don't get to your question during the session, we'll cover it at the end of explaining this before we get into the practical work. So, first of all, spatial data is a particular form of data, and it has, when you look at the files you've got, you have a number of different elements of those files. So, I'm just going to talk about the key things. So, the first is the projection, the second is shape, and then the kind of data we attach to places. So, first of all, the challenge we have is we're taking something round the earth and translating it into something flat. And what that means is, depending on where you look at it from, look at the earth from, you get very different pictures. So, we tend to look at maps of Norway from above somewhere around the equator. So, Norway looks quite small, but if we move round our point of view to above Norway, it would suddenly become quite, it would become much larger. So, the projection system is one where we derive our view, and the data we're using, the spatial data we're using, derives that view from something generated by ONS, which allows us to see the UK more or less from above it when we map it. The second thing that's there is our shape files, which are the way that we create boundaries on a map. So, in effect, it's a series of dots, and we go through joining up those dots to create spatial areas. And then the final thing we're going to cover is data types. So, an aero unit is a polygon, a shape, and they're given codes and names within data. So, when you download data from census aggregate data, you will have codes that attach it to a particular geometry. So, in this example, we're looking at lower layer super output areas. We've got some data attached to them. And when we create a shape file, we can attach to that the values associated with each aero unit. The second type of data is point data, and this is used to generate points on a map. So, the two examples there are actually tram stops in north Manchester. So, we can create an absolute place where those are and locate them on maps as well. So, those are the two types of data we use. If you are mapping individuals, you could similarly put that data onto point data. We've talked a little bit, Dave talked a little bit about output area geography, which we're going to focus on. But in the example here, I just want to show the benefits of the output area geography or the differences between output area geography. So, in the map at the top, we can see greater Manchester. And this is showing the proportion of households in the private rented sector. So, we can see a proportion of around 30% in Manchester, which is the long thin local authority area. When we move down to the next layer of geography, which is a middle level super output area, there's 57 areas and we can begin to see patterns of concentration. The darker part and the lower part of the diagram is around the university, which is where there's lots of student accommodation and student halls. And the part further off is near the city centre, where, again, there's quite high concentration, quite high density. As we move down to lower layer super output areas, we can see this becoming more granular. And the values now go between 5% and 90%. And finally, in output areas, we can see there's 1530 and you can get some real levels of detail. So, we've got some areas with no nobody living in the private rented sector up to 97%. So, that's something about the geographies available and that would, using these techniques, you'd have to choose your geography depending on what you are linking to. So, in terms of COVID data, COVID case rates are published by Public Health England. There's a consolidated publication as well as the daily ones which you may see in the news. There's a consolidated publication of all the results, positive results from the last week released each week. And that's held at mid-layer super output area. So, that's the data we're going to be using because we're constrained by where the level of COVID cases are. Vaccination data isn't available at that level so I couldn't include that within the data source. And we're using Greater Manchester. You could cover this across England, across a region, et cetera, but Greater Manchester seems like about a reasonable size to give an example of how to use this data. One of the things I provided is you've got two sets of shapefiles and I wanted to show you the difference between them. So, on the left, we have a physical map of data which is showing the COVID cases per 100,000 population at MSOA level in the first week of October 2021. So, as you can see that gives you a kind of picture which tells you something about where COVID cases are. But if we look on the right, the boundaries have been resized to reflect the population density. So, those where the population density is higher have become much larger. So, you can see that Manchester has grown significantly in that map and some of the outer areas have shrunk quite a lot. So, if you pick out parts of that map, you'll be able to see both of those. Now, they both have their purposes. So, I've given you both options. So, if you're writing for a policy audience or a more populist audience, then the physical geography probably works best. But if you're trying to show the extent, I suppose, of whatever variable you're mapping, then the cartogram has some definite advantages. So, I'm going to now move on from those mapping basics into some of the principles of spatial analysis. So, I'm going to go through these in a bit of detail. There's more on the slides and I suspect this is the part where some people might want to stop and ask questions. But in terms of these principles, these are what you'll be applying in the workshops. So, the first thing is what's called the modifiable aerial unit problem. And what this is is that depending on where you join boundaries, you will get different values. So, in the example here, you can see we've got a square with nine aerial units. So, the right of that, we shifted that over to three units. Below that, we've got a different set of boundaries and the final option is another set of boundaries. And if we look at what that creates, the red dots are showing where there's what look like errors between the spaces. So, in terms of the boundaries, you're seeing quite different results. This particularly happens. I suppose this randomness is one of the things that output areas were designed to try and overcome in terms of grouping areas of similar characteristics. But one note of caution here is though that's true of the output area, it's not true of those things that are built together because those are built continuously. So, we may well introduce error by going up from output area to lower layer and mid-layer, super output areas. So, that's what's called the modifiable aerial unit problem. It's something you might need to consider when looking at any analysis that uses spatial techniques. The next thing is spatial dependence. So, if you think about where you live, sorry, I'll just answer Paul's question here, which is asking if is it correct to assume that any physical representation of a place is at least slightly distorted. Yes, that's true just by the perspective that's taken on it. So, we can get close to representations we think are okay, but the representation we use for Greater Manchester is not taken from above Greater Manchester. It's a UK-wide one. So, spatial dependence is where there's a relationship between things. I suppose if you think about the towns or cities or villages or countryside you live in, you would probably think of certain places as having characteristics that were similar. So, if you know Manchester, you would know that there are different parts of Manchester and some are regarded as affluent, not many of them. Some are regarded as places where there's lots of students. Some are regarded as places where there's quite high rates of poverty. What that suggests is that within those areas there is a kind of clustering effect. So, that's what spatial dependence is. So, if places around you are like the place you're in, then there is a level of spatial dependence. The second kind of concept is heterogeneity. So, this is something I think that geographers in London and people doing spatial analysis in London find quite a lot that what you're seeing is very different areas, very close together. So, it's a kind of issue that will affect any calculations you do. So, just to give you an example of spatial dependence, on the left-hand side we have again a set of units, aerial units, with rates of burglary. So, the lightest shading or low rates, the medium shading are medium rates of the high shading. The darker shading is high rates. But if we look at income per household, we might expect there to be some correlation. So, if you look at number six, then there are high rates of burglary and income per household is high. So, why isn't that happening in number 16? So, one explanation might be that the higher rates of burglary surround a cluster of low rates of income. That's not definitive but it's something that you might wish to explore looking at that breakdown. Whereas on this pattern, which is fertility levels and female labour force participation, there doesn't really seem to be a correlation at all. There are some patterns there, but there's enough going on that suggests, so one in eight for example, where there's high rates of fertility correspond to low rates of female labour force participation in one and high rates in eight. Now, this might be because we haven't included something that would help us to explain female labour force participation, or it might be there's just a factor that isn't affected by where you are. So, moving on from there, what we try and detect is whether there's autocorrelation. So, whether there's clustering of high or low values or negative clustering of neighbours with high and low values like a chessboard pattern. And the effect of that autocorrelation in the error term in regression is to make estimates of the t-test values unreliable. So, positive spatial autocorrelation will increase the value of the R-square statistic and negative spatial autocorrelation will deflate it. So, those are the kind of problems that come from a non-random distribution that is caused by spatial autocorrelation. So, in order to kind of work out whether there is spatial autocorrelation, we need to generate some kind of value for an area, an area or unit, and its neighbours. And there are different ways of doing this. So, one is based on areas that are next to each other. So, contiguity. One is based on distance. So, you take the centroids of the polygons, the shapes, and calculate a distance. So, if you say within 10 miles of this space, of this shape, then all of the units will be regarded as neighbours. And there's a set of kind of work-throughs that go through that detect which neighbour, which aerial unit should have which particular neighbour. Or you can limit the number of nearest neighbours. Now, I think just to say here, I tended, I've used this technique for a number of years. I tend not to use distance because it does cause issues with more densely populated areas. So, I tend to use contiguity. And for the workshop, we will use a contiguity measure. So, looking at those, there are two commonly used ones. So, on the left we can see a first order rook. So, this is based on chest playing. A rook can move up and down and sideways. So, what you do is take the area units above, below, to the left and to the right. And the second is the first order queen, which takes all touching units. So, it takes all of those that are adjacent to the unit you're looking at. Those, and you could also limit the number of neighbours. So, it will cut them down by some calculation. So, that's the first order. If you were looking at large geographies with lots of units, you can also go up to second order, which would move these out another area. So, it would go on the rook side, go up one, left one more, left one more, right one more, and down one more. And with the queen, it would move out another block. So, having generated that, we can then look at spatial order correlation. So, I put the formula there. I'm not going to go through it. But Moranzai was developed. And what it returns is a value between 1 and minus 1. And it's a diagnostic that tells you it's a spatial order correlation. So, 1 is complete spatial order correlation. My positive order correlation and minus 1 is negative order correlation. A value of 0 or close to 0 would imply there's no order correlation. And what we get is a plot. So, in effect, what this plot is doing is taking that value for the aerial unit and the average value for the weighted areas. So, in this example, this is a queen's contiguity first-level weighting. So, it's taking the average or the mean of the variable across those units and then plotting them against each other. So, what this is suggesting is that similar places are grouped together. So, the direction is a positive space, suggesting positive spatial order correlation. The next thing we can do is look at how this is happening in each aerial unit. So, again, a formula on the local indicators of spatial association, which are used to generate leaser maps. So, let's have a look at them. So, what this is showing is on the left-hand side is a map of areas. I haven't put the variable on here, which is a bit poor on my part. So, this is a physical map of Greater Manchester, and it's showing the types of areas that are conjugated together. So, those areas that are red are high values of this variable, surrounded by high values in the neighbouring areas. So, you can see the crosses of red around the outside to the north, and in part of the other boroughs there. And the blue is low surrounded by low. So, this is where there are low values of the indicator surrounded by them. I think this was an indicator I generated for the training programme, which is based on a hard-working index. So, there are a few light blue areas, which are low values surrounded by high values, and there's a few pink ones, which are high values surrounded by low values. So, that's the map on the left, the Leesa map. Quite commonly, that's produced with an indicator statistical significance. So, the darker the green shading, the more statistically significant the values are. These can be generated, they're quite often published together, and the colours are a kind of convention that is used in producing and talking about these maps. So, when we move on from there to multivariate analysis, we have a basic linear regression model, which is a constant coefficient for each of the variables selected, and an error term. When we think about spatial articulation, we think that the error term may vary between the spatial units. So, in the example here, survival probabilities in the event of a heart attack are likely to depend on the distance to the hospital. We could also take into account the severity of the heart attack. You could also let the intercept vary in the model. So, what we get to is, after including all the variables that we think are meaningful, we may still get a significant result in the Moran's eye. So, that's the global measure. And what might be causing that is spatially correlated variables that are omitted, spatially correlated errors in variable measurement, and things we haven't considered, interactions we haven't considered. And to address this, we can use two models. We can use a spatial lag model, which takes account of positive of spatial dependency, or a spatial error model, which takes account of spatial heterogeneity. So, in thinking about spatial lag, we use a lag value of the dependent variable. So, we take the dependent variable we're looking at, and we look at the lag value. That's the mean value of that area and the ones around here, in the case of a queen's order. And the result that comes out is a parameter called row.  And in terms of spatial error, the results are put in a term called lambda. And those are both estimated by maximum likelihood. So, in terms of deciding whether there's spatial autocorrelation, you've got some diagnostics. In deciding which approach to use, you can do this either through theoretical considerations or exploratory data analysis. Within the regression model in Geoda, this is available also at least in R and Stata. There are diagnostic tests that give the Lagrange modifier and robust Lagrange modifier, which identify whether there's lag and or error. So, if we assume that there is no spatial autocorrelation, we would use a standard regression model. So, that is the process we'll go through later on. We then look at the multiplier lag being significant or the error being significant. So, if one is significant and the other isn't, it would point us to one of the models. If both tests are significant, we can use the robust statistics. And again, choose one or the other. There are some cases where both will be significant, in which case you may well end up presenting your three sets of results. So, your standard regression model, spatial lag model, and spatial error model. So, we're going to move on to the workshop in a bit. We're going to have some questions. But what we'll be doing is using Geoda to load the data, explore some of the descriptive statistics available within Geoda, produce weights, and then explore the kind of clustering and sparsity using Lisa maps, exploring spatial autocorrelation, and then moving on to regression. Now, a note of caution here. I suppose in designing this workshop, we're thinking that the 2021 census is going to give us some really good data to be able to explore elements of this. But in developing this, I've used the 2011 data. So, quite a lot of that data may well have changed. So, the nature of places, the kind of people that live there, et cetera, may well have changed. And in testing these results, a number of the coefficients don't give you significant results. So, what we're doing in the workshop is really exploring. Now, the two I've given you, the two examples I've given you in the workbook, do produce some significant results and do have something to discuss. But I think it's fair to say that I would assume that using 2021 data when it's available is much more likely to predict levels of COVID cases. The other thing to say is that the case data that we're using comes from 13 months. So, from October 2020 through to October 2021. And the way that COVID has changed over that time has been quite significant. So, there have been what are commonly called hotspots. So, I was teaching at another university in October 2020. And the areas where the students lived became a particular hotspot of that time. The cases went through the roof. And I think that movement of students to university was quite significant in many places last October. Whereas, in different times, it will be related to different reasons. So, the early cases were very much associated with health staff. And if we isolate where health staff live, we might get some indicators of that as a part. Okay. It doesn't look at this stage like we have any questions.  Because what I'm going to do next is to move on to quickly demonstrating Geoda. So, okay. So, this is the interface. Open this. So, this won't be sized. Geoda, if your site isn't the best, is a strange menu. But to start with, it has a menu across the top, a number of icons which relates that menu. So, the first thing I'm going to do is to open a shape file. So, I go to file, open shape file. The workbook that you should have access to, and we'll drop the link again in chat, will take you through these steps yourself. So, I'm going to open the cartogram of the MSOAs in Greater Manchester. So, you can see this is now a map of Greater Manchester. I've just highlighted one of them. So, what this is doing on the left-hand side, it tells you how many units there are, 346 MSOAs. And as you can see, it's a cartogram. So, the areas have been leveled out in terms of size. So, the next thing I'm going to do is just explore some of that data. So, I can go in and look, for example, at a histogram, which will give me the frequency distribution. And it comes up with all the variables. So, let's just pick May 21. And that shows me the distribution. So, we can see that there's a few areas with quite high rates, but there's a lot with quite low rates in May 21. I can't see a map right now, Nigel. Sorry if I'm misunderstanding, but I can just see the menu up close. Can you see the histogram? It says histogram Crate May 21, but then it's just a menu below it. Right, okay. I'll go back and write. Now, can you see my screen now? Yeah, I can see the map and it's a bit more zoomed out as well, which is good. We can see it really clearly. Okay. So, I'm going to have to flip between the menu and so on. So, the next thing I did was then go in to explore and look at histogram. And I picked the rate for May. And what that gave me was another pop-up window. And I'll enlarge that a bit. Can you see that? Yeah. Okay. So, what, as I was saying, that's showing a number of areas, more than 200, with low numbers of cases, and just a few with quite high numbers of cases. So, there's one over here with 570, I think, if I'm reading that right. Now, I'm going to shut a couple of these windows down just to go to the next one. Okay. So, back into my GEODA menu, the next area of exploring you can look at is a box plot. So, that is similar. It gives you a distribution. I'll do it again for the same value. And it gives a simple box plot of one variable. So, you can't put multiple variables in here, just one. So, it gives you a way of exploring the data to see how it's distributed in terms of values. And you can see the outliers at the top. I'll close that again. You can compare two variables. So, if I was to take that rate in May and compare that to the proportion of students, that's if there's any relationship. And it's suggesting there's no relationship in May 21 case rates with the proportion of students in an area. But if I was to do that with the October 20 case rates, the picture is quite different. So, if you can see those two scatterblocks, you can see in October 2020, there was an issue associated with students. So, those areas with high proportions of students appear to have high levels of cases, whereas there's apparently no relationship by the time we come to May 2021. Okay. So, I'm going to close down those two scatterblocks. So, that is probably about it for the other plots there. You can explore them. The next stage would be to go into tools and to generate your weights. Now, I've already done that, but I will show you the screen just to show you how you do it. So, you create it, you add in an ID variable, and then you select what type you want. So, if I click on Queen Contiguity, I'm going with those right next to it, create it, and I can save that as a file. And now, I can use that when I move on to looking at the next stage. So, that's a one-off stage. So, now I can move into these, which are the diagnostics. So, I can look at the Miranzi, which we talked about. So, this will plot a variable and say, is there any relationship with places around it? So, let's pick a different one. Let's go for January 2021. And this is plotting the rate in January 2021 in an area with the weight surrounding it. So, we put in a Queen's weight, Queen's Contiguity weight. So, this is saying there is some positive auto correlation between these, and it's giving a Miranzi value of 0.22 something. So, I should make that a bit bigger again. So, that's plotting those variables. So, back into Geoda, if I go into space again, I can also look at the local area. So, if I look at the local Miranzi for the same area, it gives me a set of options. So, I can produce the significance map. That was the green one to show statistical significance, the cluster map, and the Miranzi got the scatter plot. So, if I just click on OK with that, I get three different maps. So, let's have a look at them each in turn. So, three different figures. So, this is the clustering of COVID-19 cases in January 2021. So, we can see some red places where there seem to be quite high rates of clusters, some blue places where there's quite low rates, and then the light blue where there's low surrounded by high, and the pink where there's high surrounded by low. And a large part of the map is not significant. And we could look at the statistical significance of those as well. So, you could present those two maps together, potentially, if you're explaining what you've done. And last I've got the Miranzi, which we saw before. So, we can explore those different variables that we might think about in a model, using those different techniques. Finally, we could look at methods, and where we get here is the regression. So, I'm going to pick a different time. So, the first thing I do, can you see this, by the way? The regression screen. Yeah. So, on the left, I've got a set of the variables. So, if I pick a dependent variable of January 21, which we were just looking at, and I'm going to try using the concentration of people who work in health, education, and emergency services. So, at the moment, I've got no weights fail. So, if I click that on, I get the weights file. And I'm running a, first of all, a standard regression. So, this just one's a classic regression, and produces an output file with those coefficients. So, this is telling me that up here, that this is not explaining an awful lot of variance, 2%, that there's a positive effect for health, but it's not statistically significant. There's a negative effect for education, it's not statistically significant. And there's a negative effect for the emergency services, which is statistically significant. Now, thinking about what these coefficients mean, what they mean is that for each unit increase in the value of this coefficient, the effect on the COVID case rate is minus 4279 in this case. So, for each percentage increase in the proportion of blue like people living in a neighborhood, there's a reduction of 42.7 or 42.8 in the case levels. Okay, so that's the first part of the model. Now, the second part of the model here gives you the diagnostics. So, what this is saying is the lag model is statistically significant. So, there is a spatial lag effect, and it's also saying the error model is statistically significant, but neither the robust tests are significant. So, what we can do with this is to go back to the regression model that we've got here, and we could try the spatial lag effect and see what happens. And we get another regression report for this. And if we look at the results, we've got some changes here. So, the results include introducing spatial lag, leaves health very significant, sorry, very similar results, but not statistically significant. It shows that education is now statistically significant and has a negative effect. And those working in emergency services is statistically significant, but the effect has reduced. So, I'm just going to park that one over here and try spatial error and just see what happens with that. So, what this has done has changed those results again, and it's given you a lambda effect, but it's saying the only significant effect is education, people working in education. Now, I kept this fairly simple. I think if I was thinking about this, I might be thinking about how I could isolate health staff who are at the front line from people who are managing etc. So, I might be using the NSSEC social classes to try and isolate people in particular occupations. So, the census gives a broad occupational space of health. It gives three or four different categories, and I've combined them here. So, in effect, what I've done is run through the ways that we'll be using geodas.


In this video, we'll be looking at how we can use Kohlrausch's law to calculate the limiting molar conductivity of weak electrolytes. Now this plot here is a variation of molar conductivity with concentration and we have seen these curves before. The green one is the variation of molar conductivity for a weak electrolyte and this white line is the variation of molar conductivity for a strong electrolyte. And in one of the earlier videos, we have discussed how to find the limiting molar conductivity or the molar conductivity at infinite dilution for a strong electrolyte. If you don't remember, I'll just quickly remind you. So what we did was we extrapolated this curve to the point where it cut the y-axis. So here if I extend this line, it intersects the y-axis at this point and this value will be the limiting molar conductivity or the molar conductivity at infinite dilution for a strong electrolyte. So here it was only a matter of extending this line and seeing where it intersects the y-axis. But can we carry out the same exercise for the weak electrolyte? So if we look at this green line, as we go to smaller and smaller concentrations like say at this point, a curve approaches the axis but we don't know where it will intersect the axis. Like if we extend the axis as well, we can see that the green curve is getting closer and closer to the axis but we cannot say for sure where it will intersect the axis. So in such a case, when we want to find out the limiting molar conductivity of the weak electrolyte, we cannot do that by just extrapolating this curve. And in such situations, we can use Kohl-Rosch's law of independent migration to determine the limiting molar conductivity of a weak electrolyte. And one more thing. Now because of my bad drawing skills, it may look like this green line will intersect this white line but we know that that will not happen because earlier we had derived the equation for this curve which was a hyperbola and we know that this hyperbola will be an asymptote to this line. So it will never intersect this line but it will keep getting closer to this line as we go to smaller concentration values. And the other thing is, let's say you really want to find out where this is going to intersect and to do that, you measure the conductivity at smaller and smaller values of concentration. So when you are at these very small values of concentration, you run into experimental limitations. Like for example, we know that the least count of a regular scale or ruler is 1 mm. So we cannot measure any length which is less than 1 mm using that scale. So just like that, when we try to measure the concentrations at very small values and the corresponding conductivities which are very large, we could hit these experimental constraints. And again, one way to get around this would be to use Kohl-Rosch's law of independent migration. So let's see what that is. Now when Kohl-Rosch was looking at the data for molar conductivities of different electrolytes, a pattern was observed. So let's say if you have two electrolytes, one in the form of NaX and one in the form of KX, where X can be any anion. So basically we have the same anion, but different cations for both of these electrolytes. Now for these, if we look at the data for the limiting molar conductivities of these electrolytes for different choices of X, we see a pattern. So let's say first we take the case where X is Cl negative and we calculate this value, that is the limiting molar conductivity of NaCl minus the limiting molar conductivity of KCl or the difference between these two values when X is chloride ion. So this is the first case. Now similarly, we calculate this difference again, but this time let's say we take this X to be the bromide ion or Br negative. And again, we calculate the limiting molar conductivity for NaBr minus the limiting molar conductivity for KBr. And let's say we take one more case, which is the similar exercise carried out for an iodide ion. So we have the limiting molar conductivity of NaI minus the limiting molar conductivity of Ki. Now what we see is in all these cases, these values come out to be approximately equal. What this could mean is that the value of this expression seems to be independent of the choice of the anion because this difference turns out to be approximately equal irrespective of which anion we choose. And a similar trend is also observed when we take the same cation, but we use different anions. So based on these observations, we can make some calculations. Let's see what those are. So if we go back to the examples that we used because we noticed that the difference seemed to be independent of the choice of anion or the cation. Let's take a simple assumption. Let's say that the limiting molar conductivity of NaX will be equal to the sum of the molar conductivity of the individual ions. So in this case, the limiting molar conductivity of NaX will be equal to the limiting molar conductivity of the Na plus ion plus the limiting molar conductivity of the X negative anion. And we can write a similar expression for this KX as well. So these lowercase lambdas are the limiting molar conductivities of the individual ions. And now if we look at the difference between the limiting molar conductivity of NaX and that of KX, we can see that this value is common to both of them. So if we subtract this value from this value, the limiting molar conductivity of the anion gets cancelled out. So we can write lambda naught m of NaX minus lambda naught m of KX will be equal to this value minus this value. So we have this expression and we can see that the lambda naught X negative gets cancelled here. And so this difference is equal to the difference of the molar conductivities of the individual ions. And this explains why we got similar values for different choices of X. So let's say if X is chlorine, we know that the limiting molar conductivity of NaCl will be equal to the sum of the limiting molar conductivities of the Na plus ion and that of the Cl minus ion. But what if we were looking at something like MgCl2? In that case, how can we write this expression? So it was observed that in such cases, we can write it as lambda naught for the Mg2 plus ion and two times the lambda naught for chlorine negative ion. So we have multiplied the limiting molar conductivity of this ion by the number of such ions when MgCl2 dissociates. So based on this, we can write a generalized expression for the limiting molar conductivity of any electrolyte as equal to the sum of the limiting molar conductivity of the individual ions multiplied by the number of cations or the number of anions. So this is the generalized expression of Kohlrausch's law of independent migration. Now let's see how we can use this expression to find out the limiting molar conductivity of a weak electrolyte. Let's say we want to find the limiting molar conductivity of CH3COOH and we are given these three values which are the limiting molar conductivity values of CH3COONA, HCl and NACL. And if you notice CH3COOH is a weak electrolyte and all these three are relatively strong electrolytes. So we can calculate this value by applying Kohlrausch's law of independent migration on this data which is given. So if you want you can pause the video and give it a try before we continue. Okay. So let's see how we can find out this value. Now the first thing is that since we are given these three values let's apply Kohlrausch's law for CH3COONA, HCl and NACL which will look something like this. So we know that the limiting molar conductivity of CH3COONA will be the sum of the individual limiting molar conductivities of CH3COO negative and NA plus and similarly for NACL it will be the sum of the limiting molar conductivities of NA plus and CL minus. And similarly for HCl it will be the sum of the limiting molar conductivities of H plus and CL negative. And I have labeled these equations as 1, 2 and 3. So now looking at this let's say we subtract equation 2 from equation 1. So in this case we get 1 minus 2 and this lambda naught NA plus is common to both of them. So this will get subtracted out and we get lambda naught CH3COO negative minus lambda naught of CL negative. Now what if we add this value to equation number 3 here? So if we do that we have this 1 minus 2 added to equation number 3 and again here we have this positive value and this negative value which will get cancelled out. So when we add equation 3 to this equation on the right hand side we only have lambda naught CH3COO negative plus lambda naught H plus and if we look at this value we just now saw how from Kohlrausch's law this value that is the addition of the individual molar conductivities of these ions will be equal to the molar conductivity of CH3COH and so because we know the values of equation 1, 2 and 3 that is these values the left hand side values we can simply substitute them and based on these values we can find out the limiting molar conductivity of CH3COH which is a v-character light.


Greetings friends. Welcome to CTUCC Conference Cast for December 19, 2013, the regular podcast of the Connecticut Conference of the United Church of Christ. Whoever you are and wherever you may be on life's journey at this very moment, you are welcome here. We begin this week's conference cast with this meditation from the Reverend Dr. Michael Seba, Southwest Regional Minister. In his Gospel, Matthew tells the story of Jesus' birth by focusing on Mary and Joseph.  It takes a dream of an angel to persuade him to stand with her and be parent to the boy called Immanuel, or God with us. Luke owns Christmas. Go to any Christmas pageant, listen to any Carol, watch any movie about Jesus' birth. All you get is Luke, embellished by over 2,000 years of storytelling, the journey to Bethlehem, no room at the inn, sheltering in the stable with farm animals, the baby in a manger, shepherds in the fields, the heavenly hosts singing glory. Of course Matthew owns Epiphany, but that's a different feast. In contrast, Matthew's birth narrative is sparse. You couldn't make a Christmas pageant out of it if you tried. But there is one absolutely beautiful artistic rendering of Matthew's story of Jesus' birth. It's in the film The Gospel According to Saint Matthew, produced by Pierre Paolo Pasolini in 1964. I invite you to watch the whole film online on YouTube, or to watch from minute 3 to 6 minutes and 45 seconds to see how Pasolini dramatizes the story. At the beginning of the scene, Mary stands outside her house. As the camera pans her body, we see that she is obviously pregnant. As she stares at Joseph, her face reveals uncertainty and a bit of fear, but also displays a conviction that she has not done anything wrong. Joseph appears to be holding in his anger at this discovery. His lips look like they are about to move, but he cannot form the words. Eventually he turns and walks away. As he leaves, some other women join Mary in the background, as if in solidarity with her. Throughout the scene, no words are spoken between Mary and Joseph. Joseph walks to the town square. As adults do business and children play, he lies down and falls asleep. The angel who appears to Joseph is portrayed by a woman. Nice touch. Her message is both an explanation and a command. I sense she would not respond favorably to any objections Joseph might raise. Joseph knows what he must do. He hurries back to Mary's house. She is still waiting outside. The camera focuses on Mary's face. As she realizes that Joseph will fulfill his commitment to the marriage, we see her begin to smile. He will all be okay. Matthew describes Joseph as a righteous man. For Joseph, this meant following the commandments of God as he understood them. But Mary's experience and the witness of the angel offer him a different perspective. God is able to do things outside the limits of the laws that God has created. When this happens, we need to be open to possibilities that may challenge our settled opinions about what is right and what is wrong. The angel tells Joseph that the mission of Jesus is to save his people from their sins. One of the ways we are saved from the consequences of sin is through reconciliation with God and with one another. Matthew's birth narrative is a story of reconciliation. Joseph is reconciled with Mary and with God when he accepts that God is capable of righteousness beyond the conventional notions of right and wrong. The first act of salvation achieved through Jesus, even before his birth, is the reconciliation of his parents. This reconciliation makes it possible for them to complete the marriage commitment they made to each other. The presence of Emmanuel, God with us, enables us to restore, renew, refresh, and maintain relationships that are strained. Do we need to ask or offer forgiveness? Do we need to be reconciled with anyone? Do we need to look beyond rules that only serve to exclude? Do we need to be more open to a God who is free to act in ways that seem to defy conventional wisdom? As the angel told Joseph, let's not be afraid to do what God calls us to do. Here is a prayer for this week. Reconciling God, however much we try to put you in a box, you will not stay there. Help us, as the body of Christ, truly be a healing presence in and for the world. In Jesus' name, amen. In the news this week, Conference Minister the Reverend Kent Solati offered Christmas greetings yesterday in a short video released through our YouTube channel. Please do view it in its entirety, but here are some highlights. The little babe in the manger who's crying, no crying he makes, I'm not sure that's the Jesus that I'm preparing for. Do you think Jesus may have alerted his parents that he was hungry with a cry? I also think that Jesus grows up and becomes someone who doesn't fulfill the expectations of Messiah the way that the culture did. He wasn't a military leader, he didn't lead by conquest, he was born to unmarried parents who were dirt poor, and he came to literally turn the world upside down. I also love music, and so the traditions of playing, my Christmas or my Advent preparations will not be complete unless I've listened to Handel's soulful Messiah, which was produced about 20 years or so ago, which is a very different rendering than the classic traditional Messiah, and I have to listen to that several times before I feel ready for Christmas. And what is your wish for this Christmas season? My wish is really a very simple one, and one that is not original, but I do really believe in my heart of hearts that this vision of Isaiah, where we would beat our swords into plowshares would come true, that the ways of the world, which includes some very violent reactions in our culture, that we would learn that is not the way, that the Prince of Peace has come to bring God's shalom. It's my prayer and my wish this Christmas that that message would touch more people through our lives, through the way we live, and through the changes that we make. My Christmas wish as well is that we would recognize that we need one another, that there is an interdependence that exists on this earth, that human beings need each other, we need the created order, and we need to make a difference in this world. We need a new zeal for the gospel and God's extravagant love. On behalf of the staff of the Connecticut Conference of the United Church of Christ, I would like to wish each one of you a very blessed Christmas. This is a season where we proclaim that God's love comes to us in a baby, in a vulnerable way. And so it is a joy for us to be part of this family, this season, this holy time. We wish God's extravagant love and blessings to be with you and yours in this Christmas season. God bless you. Last Saturday morning at 9 o'clock, falling snow outside the windows did not keep many members and friends of the New Town Congregational Church UCC away. As they gathered for a special service of remembrance, one year after violence took the lives of a gunman, his mother, six educators at Sandy Hook Elementary School, and 20 children. New Town's faith communities each held their own services. But at the Congregational Church, Senior Pastor the Reverend Matthew Kreben included other leaders in the morning's prayers, including Conference Minister the Reverend Kent Salati, Ramayesh Aul Praaver of Congregation Adath Israel, the Reverend Samuel Saylor of Blackwell AME Zion Church in Hartford, the Reverend Henry Brown of Hartford's Mothers United Against Violence, Retired Associate Pastor the Reverend Janice Tolukian, and Alyssa DeWolf, the Church's Minister of Faith Formation. Even in the midst of darkness, we pray that your light will continue to guide us on our way, Kreben said, and your promise of peace will continue to call to us and summon us out of our fears and our apathy and our despair and help us to find life and to find it abundantly in and through you, you who are our light and our hope. The final print issue of contact will arrive in mailboxes this week, and the online version is posted to our website at ctucc.org. Our theme this issue is telling the story, and so we have stories about the importance of storytelling, about coming to faith, about ways to make stories into reality, and about new media with which to tell stories. Also on our website this week, you'll find a story and photos of the Victorian Christmas held at Westfield Congregational Church UCC in Danielson. The last of those services is this coming Sunday evening at 7 p.m. There are lots of new stories from the recent contact, of course, and the full video of Kent Solati's Christmas greetings. You'll find them all along with the latest headlines at ctucc.org slash news. This is the season of the Friends of the Conference annual appeal. The conference is an extension of your local church ministry, enabling us to reach out in love to each other and to those in need. That is why we ask you to give generously, even sacrificially, to our Friends of the Conference campaign, so we can be in ministry together, transforming lives in the name of Christ. To be part of the new things we imagine God doing through us, visit us at ctucc.org slash donate. In the new year, Stepping Stones returns on January 14th, with past them the football, the care and support of volunteers in Southington. Basics of Christian Education, a new program that will enable participants to launch an effective faith formation ministry in their local church, runs three Saturdays, beginning January 18th. Hartford Seminary and Asylum Hill Congregational Church have come together to offer the Bible and the Quran on January 29th in Hartford. Comfort food for the journey to a day retreat for clergy women will be February 4th in Hartford. Mark your calendars now with the first of March, when we hold Super Saturday, a day of workshops, fellowship and celebration for church members and leaders, featuring a keynote address from author Diana Butler-Bass. We're holding this in concert with the Massachusetts Conference this year and will be in Ludlow, Massachusetts. Registration is open for the New England UCC Women's Celebration to be held March 28th through 30th in Portland, Maine. There's a discount available for those who registered before January 15th, so get that done right now. Learn more at UCCWomenCelebration.org and you can always learn more about what's coming up in the Connecticut Conference by visiting us at ctucc.org slash events. We're closing today with another special prayer offered to us by the young people who participated in last spring's maple sugaring retreat weekend at Silver Lake Conference Center. With the fresh snow of two recent storms gleaming around us, we give thanks for this season. God, your presence engulfs me. I hear you in the stream babbling beside me. I see your touch in the bark of the trees. The snow sparkles like a diamond. I am one with the trees, the cool air, the crunchy snow that blankets the ground. I see my reflection in the river and I see you standing next to me. Everything was created and is living because of you. When I think I am alone, I am really at God's side. Amen. And that brings this conference cast to a close. Thanks to Michael Siebel for his reflection and GarageBand for our music. Special thanks today to David Jarvis for his contribution of our Christmas music. Primary funding for conference cast comes from your congregation's gifts to our church's wider mission, basic support, changing lives through the United Church of Christ. This is Eric Anderson, the Minister of Communications and Technology for the Connecticut Conference of the United Church of Christ, praying that your days this week may be filled with the presence, the guidance and the grace of God. This is Eric Anderson again with a program note. With Christmas coming next Wednesday, I plan to release conference cast early in the day on Tuesday, Christmas Eve, if all goes well. The following week I will be on vacation. So the first conference cast of 2014 will be posted on January 9th. And may God bless you all.


To download KeePass XC, go to this website, keepassxc.org, and make sure to download the proper version according to your operating system. KeePass XC works on Windows, macOS, and all Linux-based operating systems. Once you download KeePass XC, you will see something like this that invites you to create a new database. That is a file in which you will be storing and protecting your passwords. They will be stored in an encrypted form. When you choose to create a new database, you will be invited to name your database. You can choose to call it passwords, or you can choose to call it something less obvious, like flowers or family pictures. Also, make sure to put your database somewhere where you can easily find it. For the purposes of this demonstration, we are going to put the database on our desktop. Now, you are invited to create something called the master key. It is in a way a password of all passwords. It is the key to your database, so make sure to make it long, complex, hard to guess, but also memorable, because you will have to remember it. Once you do that, you are given access to your database. This is where you are going to generate and store all of your passwords. Let's see how to do it using one example. Let's generate our first password. To do so, we need to click on that little golden key with a green arrow. We can then enter a title for the password, a username, and ask KeyPass to generate a very strong and long and complex password for us. To do so, click on the black dice button as shown on the screen. This will allow you to control the length of the generated password and whether it contains caps or special characters and so on. Once you are satisfied with your generated password, click on the Apply button. Once you click OK, you are done and your password is ready to be used. You can repeat the same operation to save more passwords on your database. Let's now see how we can use our newly generated password on a popular webmail service like Gmail. To do so, simply go to Gmail and trade your username. Then go back to KeyPass and simply copy your password by highlighting the account that you want to use. Either right click and copy your password or hit Ctrl C and then go back to Gmail and simply paste your password. And you are done. Thank you for watching.


As we talked about in a previous module, nonlinear causality is a form of causation where cause and effect can flow in a bidirectional fashion between two or more elements and where a single effect may have multiple causes or vice versa. The essential characteristic of nonlinear causality is the idea of feedback that an effect can create a cause but equally this cause can then feedback to create an effect in the first system. Nonlinear causality can be contrasted with linear causality where the direction of an effect flows in a unidirection. Nonlinear causality leads to a number of important outcomes that are not possible when considering more simple circumstances involving linear causality. Nonlinear causality can lead to self-reinforcing or self-amplifying processes through feedback loops thus allowing for disproportionality between initial cause and final effect. The second important outcome to nonlinear causality is the bidirectional flow of causation between the macro and micro levels within a system thus enabling downward causation as well as upward causation. Thirdly it can allow for reverse causation in time that is to say set future goals can feedback to affect current events. Likewise nonlinear causality implies the action of many variables in creating a cause or vice versa which can lead to equifinality the idea that some end effect may be created or reached through a number of different pathways. Finally in contrast to linear causality that creates a deterministic vision of the world nonlinear causality may lead to the indetermination of outcomes. Feedback processes are a central characteristic of nonlinear causation. Events do not happen in isolation but instead feedback to affect their source. Thus nonlinear causality can have the characteristic of being self-perpetuating or self-reinforcing. For example when we look at some relationship dynamic between a parent and child we might note that the child is uninterested in the parents because her parents showed no interest in her but equally her parents showed no interest in her because she appeared uninterested in the first place. This is a self-reinforcing loop enabled by nonlinear causality. Likewise the formation of hurricanes, financial crisis, animal stampede or the development of new cultures would be other examples of processes driven by self-reinforcing cause and effect. Whereas linear causation is defined by a degree of proportionality between a given cause and its effect. Nonlinear causality driven by this feedback can enable a disproportionality between cause and effect what is called the butterfly effect. Because of feedback loops small events can get compounded through each causal feedback iteration enabling a rapid change in proportionality between the inputs and outputs to the system. The results of this can be widely divergent outcomes to some situation depending on small changes in input values what is called sensitivity to initial conditions. Whereas linear causation is predicated on being able to isolate a single or small amount of variables causing a given effect. In searching for nonlinear causation we do not try to reduce the number of reasons for a given effect or vice versa the number of effects deriving from a cause. For example if we took the linear reductionist paradigm and asked the question why does an airplane fly this approach would try to reduce the cause to a limited number of direct physical interactions in which case the answer would be traced back to the dynamics of the airflow around the wing as described by a few variables within the Navier-Stokes equations of fluid dynamics. Inversely a nonlinear causal description may involve many different explanations to this question. We might say the flight is caused by the pilot directing the plane to its destination or the flight is caused by the fact that it was chartered to fly at that particular time or that it is flying because the company can make a profit from putting on that flight. Without any of these factors the flight would not be happening. When we take this non-reductive more holistic interpretation to causality it's possible to see that some effects are the product of an almost infinite number of interacting factors and it stops making sense to ask about a single direct effect. Instead the language switches to that of emergence asking how many different factors interact in a specific fashion to create a given outcome. With emergent phenomena creating higher level patterns that then feed back to exert an effect on the lower level more elementary parts. Whereas linear causality implies that causation flows from the bottom up but not in both directions. Nonlinear causality and the idea of emergence enable the interpretation of events as both upwardly and downwardly caused with causation flowing bi-directionally from the micro to the macro and back again. To illustrate this we might think about a polar bear and ask why is the polar bear colored white? The reason that it is white one might say is because of its genotype which is a bottom-up answer. But if we then ask why are its genes as such we would discover that they are a product of evolution which is selected for the color best suited to that environment. The polar environment is white so the genes have produced a white bear that have been selected for that environment. If the bear was in the Canadian forest it would be a brown bear instead. Thus we see downward causation acting with the cause coming from the environment to affect the state of the individual. Likewise the specific atomic nuclear interaction in the interior of the star at any given time is determined by where the reaction is taking place within the overall star. Thus the overall structure of the system is affecting the specific phenomena in a downward fashion. But again the micro level atomic interactions affect the whole creating a nonlinear causal relationship between the system's micro and macro levels with feedback between them. These patterns have downward causal efficacy in that they can affect which causal powers of their constituent elements are activated and this has significant implications for our conception of determinism. Both top-down and bottom-up causation can occur at the same time. Whereas linear causality and upward causality lead to the vision of a deterministic world. Nonlinear causality leads to a greater capacity for indeterminism. Indeterminism is the concept that events are not caused or not wholly caused deterministically by prior events. With linear causality a past cause creates a current effect in a single direct fashion. With nonlinear causality a cause may create an effect but because of the top-down bottom-up bidirectional flow to causality the context and conditions of this low level interaction are conditioned by higher level phenomena. Meaning the overall outcome is a product of this more complex interaction between the lower level cause and effect interaction and the upper level organization that sets the context for that interaction. Thus enabling a much greater opportunity for indeterminism. The philosopher Robert van Gullig describes this phenomena as such. A given physical constituent may have causal powers but only some subset of them will be activated in a given situation. The larger context i.e. the pattern of which it is a part may affect which of its causal powers get activated. Thus the whole is not any simple function of its parts since the whole at least partially determines what contributions are made by its parts. Thus with nonlinear causality the cause of events are not directly determined by preceding events but more emerge out of the bidirectional exchange between the conditions set by the overall system and the local level interactions. With nonlinear causality a single cause may have many effects such as a nerve cell sending out many impulses or inversely many causes may have a single effect such as a hurricane being a product of temperature pressure humidity etc. This leads to the idea of equifinality which is the principle that in open systems a given end state can be reached by many potential means. The term and concept are derived from Hans Dreisch the development biologist and later applied by Ludwig von Bertelenfie the founder of general systems theory. Some systems have more than one pathway or process for achieving a given goal. This increases the likelihood that the system will achieve its goal under various environments and circumstances. If one subsystem is damaged or if environmental circumstances change significantly the presence of multiple mechanisms or pathways thereby increases the likelihood that the varying condition can be adapted to or overcome. For example the human immune system has both a pre-existing component and induced antibody component to respond to foreign invaders. Some organisms have multiple pathways and use them in different environmental circumstances. These redundant systems accomplish the same end but do so with more than one similar or equivalent channel. Our conception of time is closely connected to our understanding of causality. A linear conception of causality leads to a unidirectionality to cause an effect over time and this is a fundamental component in structuring our understanding of past present and future. When considering only matter and energy within a purely physical system this unidirectionality to causality with respect to time may hold. However when we introduce information into the model it now becomes possible for future events to feedback to affect current events and thus enable reverse causation. Information encoding and processing is an essential feature of biological systems that differentiates them from purely physical systems and allows this departure from deterministic physical causality. Many entities have control systems that enable them to process information. Examples include animals, humans, social institutions and various kinds of technology. With a control system the structure and initial conditions may not matter. What matters is the goal. Future goals can determine current actions. Here we can note how causality is reversing as it goes from some projected future event back to affect the present state. The human body is acting out this process virtually every moving second in that we typically formulate some goal before initiating any action such as desiring the future state of eating a meal before acting out the process of cooking him. The physical structure and initial conditions determine the outcomes as governed by equations. Past effects cause current events going forward but with the control system the information defines a systems desired behavior or response and thus cause and effects are contingent on higher level information whose cause is derived from some future projection.


Starting up the engine. So my name is Tony Alatello. And I'm actually used to work on Blender development earlier back in, I don't know, five or more years ago, and started originally with the Blender game engine. And it's always fun here to see a lot of nice usage of that. But nowadays, we have a different project. It's a real extension. And we are using Ogre because we do real-time networked stuff. And here's the application. Here's one scene running in Ogre. And this is the, which is mentioned also in the text. It's modeled after, like, inspired by a real-life location in the United States. There's a place called Chesapeake Bay, which is quite famous for the nature there. There's, like, a lot of interesting animals, the Maryland blue crab and big birds and fishies and all. And then this beautiful white-tailed deer. So this is kind of a demo we made of this new platform. So all this stuff is with Creative Commons license and an open-source project on its own. And it's all made in Blender, the modeling and the animations. And then there's some games and scripting. Scripting, there's some, like, walk-path stuff that some of the animals walk around and so forth. But I'm also using this same environment to show my slides. So this whole real-extend project is, like, there's this word called the 3D Internet. And it's kind of an own thing. But it's sort of the background of the project anyway, that there's some people who have this idea that the Internet could be, like, a thing, like this kind of a virtual world where there's places that you can visit. And it's a lot all inspired by Second Life. And that's also where the history of the real-extend project goes to. This guy, Juha Holko, his avatar in real-extend is like a Finnish businessman. And after doing an earlier company, he discovered Second Life in 2007 or something and thought that it would be interesting to have this technology that you can just make spaces and have people visit them and meet other people there. But he didn't like the idea that there's one company that controls it all and that you have to pay money to get servers and stuff. So he started this open source project and gathered some companies. And I'm from this play-sign company.  And we are interested in this because we have been making game prototypes with Ogre already earlier, just single-player demos. But we want to make those games multiplayer. And that's what this platform gives us, that it's easy to make multiplayer games and other applications. So a little bit about the stuff that has been done. We made this kind of a project with schools back home. It has been in pilot use by both primary school and even 10-year-old kids and then up to high school, where you don't have to be a 3D modeler. You can just add your own images and sounds and text and stuff easily in a 3D environment and make galleries and exhibitions and presentations. And this system and the tools and this environment is on a public server on the net. So you can just visit it from the relaxedin.org. But so basically, our main thesis is that for people who know OpenSim and Second Life and that there's this kind of fixed model that what the world is like, that there's land and you walk on it and all that. But this system that we have now is totally modular and customizable so you can make any kind of application. I've been using this word application and I'm a bit scared that does that mean anything for Blender people, but I was really happy today to see that Japanese presentation about the school scheduling information because that was like a perfect example of an application. It was not a world or something like that and it was not a game but just an application that's using this technology. So we think of this as an application platform for all kinds of applications and games and multiplayer games, our one application. So here's a little diagram that I drew this morning that for technical folks shows a bit what we have. So we call this a Tundra software development kit and the stuff in purple is what is always there and like the core and the library dependencies that we always need and we try to keep that quite small. But then it's a modular system. So if you want to just, for example, make a game in C++ you can just skip all the extra stuff that you don't like but and write your own modular module using the core services and do that. Or otherwise you can just use the, like how it is by default and for example, make all sorts of functionality in JavaScript. Like for example, this slideshow thing is just a JavaScript thing and there's a couple of other examples. Here's a little beginning of a game that some French folks made that guy had used Flash earlier and he just made a test if he can make something like 3D and he managed. So this is just like a physics thing that you can, the goal is to try to like shoot with a cannon and try to reload the cannon and then shoot again and you get points when the boxes drop from the platform but you only have three shots. And here's another little game prototype by our company. The idea is that you visit these planets and for example on this planet you encounter these kind of enemies and it's like a fast action game. You see the game character and when you're actually playing then like this kind of controls that you just move the target where your guy goes and it's like rotating the planet and it's just a little prototype that we had made earlier using Ogre and have been now porting to real extent because it's planned as a multiplayer game and we get the multiplayer stuff with this easily. Okay, well I guess I have to demo the networking stuff so I'll try to do that quick so I get to show the planter stuff also. So I'm now starting another instance of the application so what is shown there is actually now running as a server in server mode so other clients can connect to it and this is what our client startup looks like let's collect two things. It actually works also as a web browser so it now just went to our website and from there you can log in to the public server but I can also just log in to the server that I'm running here which is showing on the background and I think it's connecting now so now in this client mode I get this custom user interface that's made for this place that I can use to start the games and stuff and if we look here, here on the server the guy should be visible here as well, there he is. So now if I position the camera correctly and switch to the client, oops, yeah now you can see a little guy move on the server on the server as well and these things here I can start to use to start the game so well I guess I showed here is the little guy on the server if nobody noticed and one of the games is where you have to fly as an osprey, like this kind of eagle-like thing and you have to try to catch fish by diving with the thing and then there's another game where you swim as a little anchovy fish and you can actually collaborate because this bird eats the bigger fishes that eat the little fishes that your friend perhaps is so that's how you can, it's like a three-step food chain thing and yeah if we look here and lucky enough we can see the bird flying somewhere, but okay now I go to the blender, blender part just a sec, killing these, don't you love those dolls? Okay so here's blender and well you could use the osprey so here's the bird that you saw in the game world and now I have this add-on install so there's this extra button called ogre I just enable that, I get these extra buttons this world thing, I can just click this and what it does now is that it exports the selected objects to the ogre format and then it launches the thunder system so we have it here now and then we can use the normal blender authoring stuff to do things for example I can just add a add a hemilamp and set the color for it and so forth, let's see if it's already by default yeah now I got some light there so the idea is that we can use blender for the authoring and we don't have to redo blender but we can still use this inherently networked and game suitable powerful engine and this stuff, the blender in the collection is really new, one of the new things there is now that it's quite handy to preview animations because you can just select the animation in blender and then it shows in the ogre, is that the lighting animation, yeah and yeah I can just also just move the object in blender and it moves in this game server and I can add new objects, so if I for example add a susanne here, somewhere, I think I need to select this both, now also, yeah susanne also appeared there now and then I can use, that was the lamp I can use blender to play susanne where I want and so forth but also this tundra application itself is quite, oh which button did I press so I don't see anything myself, is that the maximize thing oh doesn't matter, it has a quite nice editor where you can see the, like all the data and edit and the idea is that, for example here we see the susanne I think, that all the data, like we have this automatically generated user interface for manipulating all the scene data and the trick is that if you make a game with custom data, this editor and everything works for your data types also, your custom components and all that data is automatically synchronized over the net, so if you for example make a door and want the door to be so that you can lock it and then if you want some client side functionality for example that when the mouse cursor hovers over an object, depending whether the object is locked or not, the cursor is different. To do that sensibly, every client needs to know whether the door is locked or not and that's just automatic here, you just create the door component and put an attribute there that locked and then you just modify that one value and it's synchronized to all the participants and yeah, there's a lot of stuff I could show, I'm sorry I lost time because of technical stuff and the schedule and all, but that was the very basics basics anyway and yeah, we are using this to do projects for customers in several companies and well, all the companies want to grow as well so we are open for proposals if somebody wants some multiplayer game or whatever but it's also all there just free to use as is or as basis for your own engine or whatever. So yeah, if there's time, I'm happy, if there's questions or something, there's quite a lot in the blender user interface and there's nice demo videos of that now, how you can configure normal mapping and all sorts of stuff too. So if you just search for Blender 2.Ogr in YouTube, there's some pretty cool stuff these aren't, I think the newest ones but anyhow and yep, we also have a WebSockets client, perhaps I end by, oh, I can't see, sorry, I have to type here because I can't see them. So yeah, if somebody doesn't know what WebSockets and WebGL is, it means that there's now a new browsers, a new Firefox and a new Chrome, there's this functionality that, it went to a different screen. So what we see here is that on this right side here, I have this native client, which I was using, but on the left side, there's just normal Chrome but also this web client thing in Chrome is connected with WebSockets to the same server, so the movement is synchronized. So actually with this system, if I'm connected also from Blender to the same server, you can move objects in Blender and see them move in a web browser and the rendering here is just WebGL so there's no plugins or anything, it's just a JavaScript engine. So we are actually quite happy to have these both, that we have the powerful native auger thing that can render at least three or even six viewports for caves, like some people have built these caves now where you get really immersion and so you get all this power and you can use Kinect input and all that, but then for people who just need to see something and who don't want to install anything and so forth, then this is kind of like a light client with just a browser-based, but yeah, thanks for the interest. Sorry for the delay. Yeah, any questions? Yeah, we don't currently do any synchronization among like multiple blenders. It's because we are mostly using Blender just when authoring, when making a scene and tweaking materials and so we're mostly using it for this preview thing, but it could be used also to synchronize multiple blenders and I was myself using the verse thing earlier that and a little bit involved in the development then and but now we're actually using much simpler techniques and actually something that Theo has done earlier between Blender and real extent, we just send like Python pickles so it's easy to do anything and then for these like other clients, it's also kind of a simpler protocol. Yeah, it's an interesting idea, but we are not doing it. No, yeah, we actually use the same code base both for servers and clients. So even just on a server, there's also ogre running, but it's just not doing any rendering. It's like a headless ogre, but that's one I think big advantage that we have compared to this Second Life and Open Simulator thing because there's this one company that's making the open source client, but then there's a separate server project and they don't share any code. It's much easier to add new features when there's a single code basin and it's just an issue of a matter of configuration whether you are a client or server and perhaps we do some peer to peer stuff later on, but yeah. If somebody wants to try the application, I also have the installers for Linux and Mac and Windows on my memory stick so it's easy to test if it works on your computer and there's public servers online and so forth so yeah, you can test stuff. Yes, we use a bullet for physics so same as Blender and GameKit so no it's like the same code but so to author, you just do it like in Blender normally you put the rigid body properties and those are exported to Ogre then and they work the same there because it's the same code but it's just using the same library. So we are expanded and open-sync? Yeah, Modrex was what we had earlier because when we didn't have our own server stuff yet, we had this add-on for OpenSim Modrex which allowed using meshes and doing some custom game stuff and so forth so yeah, that's what Modrex is but currently we are not using OpenSim at all anymore usually because it's really complex with all the different servers and all the databases and this stuff is much simpler you don't have to do any configuration you just run it and it works and it's smaller and faster and reliable because OpenSim developers themselves say that it's in alpha still and we do commercial products so we don't use alpha software we use stable material stuff so currently the Modrex is not developed anymore but if OpenSim develops in the future it's perhaps we again use it somehow but now we use this simpler stuff Yes, Safari has WebSockets and WebGL at least in the nightly builds and I'm really looking forward to WebGL and WebSockets coming to iOS next summer or sometime like that because then we can run in these devices as well and it's cool that the game kit has been already running on Android so it's the same ogre that we use so we wanna run on Android as well we should have clicked the wrong thing Yeah, it's okay, go away Yep, that's good, thanks