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UrbanSim2: Simulating the Connected Metropolis

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now we are ready to begin with Fletcher off t by photon direct Urban seem to stimulating the connected metropolis the gates and sort of but that that shares in and so reframed the conversational that and so on specifically I wanna go script a little bit too frame what we do in the context of what already happen in the session so doesn't seem like it comes out of left field and because in fact we do exactly the same thing that the previous session was talking about at the core of what we do we
have statistical models where we're trying to predict prices or people live and we use variables they're very similar to the statistics that human trellis transit was talking about so something population reachable within 15 minutes 20 minutes 30 minutes on average income all sorts of statistics the mine omega GIS but using distances along the network rather than within shapes n l i adjusting of interesting there actually people behind these open projects there's a history here so of interplanetary came from Brendan Martin Anderson and it's written in Java and so on that's been going on for 3 or 4 years there's actually another 1 of these called Open Source Routing machine which I'm came out of a university in Germany and dentist fluxons person who started that project any awards format box and we've actually we've we've been working with for 3
years back before you work for my box when he's just a grad student and I have been in
integrating our code with open source routing machine for years and have finally released a a couple weeks ago and is now fully accessible in Python even though open source routing machine is written in C + + so we'll come back to that but I just wanted to frame the discussion so whom I I am a PhD in city planning have and the newly minted Pht I got my diploma in the mail on Thursday if you have a high and yet very exciting I'm glad to be done on a portion is being recorded some advisers probably watch on my research was relating what squared and I is and sentences go and it's a boiler religions and they're not going to read my dissertation that when I found relationship on people a our increasingly preferring walking in their homes I'm I'm now Chief Data Scientist at Cumberland cults intercity and out of Berkeley and I actually and and and for the next that I used to own a house a book from here so I'm happy to be back on hunter of the outline of my talk I'll I'll tell you what Robinson is give you a case study of an application of urban sound discuss our open-source stack of urban centers open source is BSD-licensed and we are open source stack I live and breathe our stack of and I will get to the point which is we have pivots and technologies I hope out of urban sum in a way that is accessible to other sort of PPI data programmers of some general functionality that you might find interesting and I'll I'll talk about that and also it's a sort of calling this term or data science that is not really a term for this right now but there's a lot of stuff going on around the science that is specific to behavior in cities and I would like to create a community around that sort of thing the and I don't expect a lot have time but if we have time there's a demo at the end but it's all get out of the examples and demos that sort of thing interest is not just for food and clothing anymore I keep all my favorite resources on the internet site through visual very well organized you can follow along everything on that side is you and I will answer questions on any of that do free ask on something haven't talked about it and so what of the same and reason
is can be thought of as retirement planning for cities so 30 years from now most cities perhaps Detroit and Cleveland accepted expected to grow by vast amounts over the next 3 years and are we going to be able to help all those people all is jobs what are the impacts going to be on transit on
traffic that how can we cities going to function when they're twice as large as they are now and how do we keep them functioning at as high a level they are now or improve their function so urban centers a set of statistical models to forecast changes in and population employment and the built environment over a 30 year period and more specifically it is an
agent-based simulation of the regional real estate markets so what that means is there we we capture individual
households individual jobs and the decisions that individual households make decisions that employers make the decisions that landowners make in each of those things are desired objects that are allowed to make their own decisions and the behavior sort of emerges out of that on going so
primarily regional Governments right our very large cities were planning for the future of their areas of that's been around for 15 years it was originally written job in 2000 and was implemented in Python in 2006 we recently implemented it and this year on new scientific Python tools specifically pandas but when I 1st started using pen that does anyone know pandas there so not not everyone to pain is the data analysis library in Python you can now do close to well of all say everything I need to do would have needed to do and ah I cannot do in Python path so also assesses the school analysis is now available in and panders I can do a database like things group by those aggregations filter of evaluations of all that sort of stuff is now available to us and Python from and is gaining in popularity all the time now it's up to 120 thousand downloads a month when I 1st started using it was the 10 thousand elements amount or something like that I and some is created by Paul Adela's my batteries chair of the city planning department at Berkeley of this is a slide I from a theoretical perspective cities really complicated and urban sound has the goal of modelling all of those behaviors so there are long-term choices on where to live and locate short term for Americans spend your days their developers to build things I'm going to trust you guys and I'm going to talk about book more about how it's actually implemented than what is from a theoretical perspective I think I think you can do it was stress so I'm essentially is a set of statistical models the run 1 after the other and at the core of it is these network-based variable computations there are similar to what they were discussing the last session and then we use those as variables to understand how people behave there is a residential side there's commercial side on the residential side you might be very familiar with it because you might have bought a house and you made tradeoffs on what you were valuing and buying a house at the sum of money for another bathroom for a larger lots of 2 to the good schools that sort of thing all those are trade that you can capture a statistical model home values that would be the residential price model the location choice model as where you chose to live and work trading often we're choosing to live the transition model is increases and changes in demographics of people in different cities so your population might double and the body which might get younger in much people might get married later all sorts of interesting demographic things happen that a foreigner happening now and having all the time there's another set of models that are on the commercial side I won't go into that too much and then there are real estate developers to make choices about when to build new buildings that and are largely informed by the decisions that we all make so when we drive up prices democracy that and they start building more things yeah urban is a scenario planning tool what that means is I have this feature of with this set of policies I this feature with this other set of policies I'm going to create some outcomes which
1 do I like better the inputs are things like and subsidies and Portland we have an urban growth boundary the incentives to live in more dense locations layer of road networks and transit networks their changing the orange line is going into Portland and 2017 I think sentiment of
their own and their road pricing on the United States but in other places and the parking policies of things like parking pricing is now a being used in some and then you get allergies triple-bottom-line metrics economic environmental and social equity considerations and
we we've done full full scale simulations in Paris this was for a billion dollar investment in transit and and planned area and in our home turf in the sense kobe area and very absolutely interesting projects and spend a couple of years putting the other simulation took it for public comment and then the tea party when not fast was absolutely it would I encourage you to go on youtube and look up when they it's a party and see some of the comments that came back the absolutely fascinating and I'll leave it at that time and so the Bay area of case study was when I worked on personally and I won't go into much detail these are my slides these are the regional planning agencies begin from the the results that we produce for them a largely the regional task is to reduce greenhouse gasses they're going to do that by making more dense built environments and the goal is to put 80 that 80 % of the runs on fiber those of the implications there and then you get out a ton of more specific numbers things like mode share alike population growth like the prices the increase and so forth FIL free proves this more in more detail and I go into the stuff in detail just noted there there 5 columns there were 5 scenarios that were taken to the public sort of a new
project to transit priority on a social equity and they they sought
public comments and then went with some the preferred scenario so that's that's
the reason I but so I hope that gives you a flavor of what is obviously a 3 difficult to sort of an talk about it but what I really wanna talk about as our experience in implementing of urban some within this open source community and talk about our open-source stack and this is my image of all time because pandas is the library that I use every day and my line of work and uh this is appended taking over the Empire State Building which was the cover of the economist make a statement on the Chinese economy taking over the American economy I wish it were higher repair resolution have never seen in a screenshot there was kind of resolution bright and that sort of symbolic of my life I don't know if this is true I hope that it's true I noticed in my anyone programs in England at sides pipeline and a program it's even performance I've been programming in Python since 2002 but when we had a writer and the is the pressure and the world has changed since then by that has an incredible set of back libraries and incredibly active community I am went to Python that when the programmer without it of the tenders was a game-changer for us from led to us reimplement urban sound from what's as do all the stuff there would be an honor of the assassin SPSS stay all the statistical libraries now I can you always things antennas and the scikit-learn that's models is that there are libraries in Python and this has been a complete game changer for us the 2006 version Robinson was 150 thousand lines of code in the current version
is 5 thousand lines of code and and that's not an exaggeration and you can look at the old 1 the new 1 and through encounter and all
of this makes it so much easier to maintain a lot easier to explain the clients all that sort of thing and what I find most fascinating is that the people who programs that the urban some from 2006 were writing Pandas and they didn't know and so they had a right 80 % of what pen as does for us today and they we tried the pivotal out to a larger community and then never quite got the traction and then was making came along the pen isn't in 2010 and just change the landscape and so this is largely argument to if you have something that is generally flexible engage with the larger community in order to get that traction or else you crude will die is essentially what it comes down to you so we actually had to repel all the things that were so close dependence and put pen to then because that's where the world was going and I by admission is before like we are wed to our open source stack we we sell Python antennas and all these things as a competitive advantage for our company and I believe that it is and we also teach it as part of endowments implementation of our open source stack is our lifeblood any up we employed someone on that dataset given that share at this point he does something called Software Carpentry which you can hire him and all of his people at work with him to come teach depend methodology for your organization of python has a
huge ecosystem of tools in that with all the on the notebooks the testing the documentation and then we we often use a lot of Web tools to you very interactive communication of our results and and get have has changed our our world as well so nothing new there everyone I hope is using get over something very similar to it but I will say we've had great success with our clients using get out and they are not programmers and most the time I'm speaking to a very different audience than your eyes and of In our clients love the fact that what we do is completely open to them that we we can solve for the American track for them and they get to see what we're doing on an almost daily basis and know that their money is being most will used OK so the Urban data science torture it would be the set of things that um we have put it out
of urban sound that might be interesting to you guys I hope and 1 of those is what we call pandanus so this is a neologism for panels network analyst it there's a lot of the things that has these network
analyst would do but was the very pandas like API so if you happen be familiar with pandas you should be very familiar with the API for this and what it does is it does this sort of travel shared the things that we've been discussing in the session and this is uh and a network or a buffer query from from the red point this is the street OpenStreetMap network from downtown sentences go and you have its origin point which is the red points and then you have some distance and you got along the network and you touch all the the nodes we get to in a certain amount of time really can change that time so any sort of generalized impedance and it's quite flexible and and you what happens within the buffer so things they're further way might help less they have weights they're not 1 . 0 all the time and on so again some the population around this point and it is very smooth surface so totally different from GIS as shapes and reader in 1 Shaper in the other shape and then 1 of the really big and the other one's me 1 small you have these very smooth surfaces that are defined by being able to model in the network then for example so a lot of where I came from is doing things like what score if you know what's words accessibilities nearby and this weighted combination of these and so this is
essentially what score using OpenStreetMap points of interest and combined to show workplaces a high accessible you accessibility so these these very orange spots are on high accessibility and the green spot low accessibility don't recognize this as a sense of scale and and every street intersection gets its own create an therefore a value of what's being aggregated around it and you get this very nice smooth services and learning this code as points and often and or alternatively on we also
have this is not open source but it is
free where you can take the form of an analysis with colors on parcels and extra them into 3 D and you can do this for the whole region so this is 1 of those on network accessibility maps and I believe this is sushi accessibility was always my theory about so and these are the predominantly agent neighborhoods of services go from and so we call this Non-CC but I'm you can you can actually color of all parcels and a very large area very quickly because using open GL instead of just roster rising everything so this is called a geochemist and you can go to our website downloaded music and then you don't have to use the network stuff with with geochemists anything that you can put a parcel you cannot seem geochemist and then you just hit the button and its 3 D yeah I then we have a workflow tool and
which allows it to if you have if you happen to have a point intended as a workflow where you're on steps 1 after the
other and you configure each of the steps and you wanna look at the tables in the maps we we have a tool that nothing is specific to a ransom in this case so we we hope that will be useful to anyone who has that's overflow this is configuring models is of running the workbook the batch jobs it can take hours on a server and checking on the status and that sort of thing so these tables this is just dating and so all of the base year 5 Table there available through vendors and this is using leaflet for maps using Desiri things and what zones on and then our ultimate goal is to be build work cities so that you can visualize the the future of the city in 3 D as buildings so and this is actually not on the source project by one-to-one screen shut up there on this 3 buildings I I don't think we have time for the actual your still have
well and this is what it looks like an online and you live demo but this is an
IPython notebook with as converted slides and so you get really work out of the list if I follow the landscape code and you initializing preprocessed America just grab units edges and weights which in this case is the instruments just distances then you do things like point point of interest Greece which would be so I'm a got 1 category in the restaurants I got these restaurants from
OpenStreetMap by way and I'm going to do the nearest points of interest of for those restaurants and grab the 10 closest and then I can actually is not problem and that
big enough that's that's not the best and all these things come out as dot maps and and they think they actually look pretty nice but I'm so this would be the the distance to the nearest restaurant this
is the distance to the fitness astronomy conceded to career and then this is the
distance to the 10th nearest restaurant so this is 1 way of looking at accessibility there are very few places in san cisco that so the don't have accessibility to 10 restaurants only those places the orange which is essentially my mental map of services go and I'm totally out punch line and I don't know indigenous earlier each of these queries runs in less than a 2nd I'm walking skill queries for a whole region less than a 2nd no problem for the other 45 minutes sort of regional-scale query since I think I'm we we run what things go crazy need actually drag the slider back and forth and we visualize it in real time so like 20 20 ms never deal the it and the thing is this is sentences go I can't even show all the data there were agreeing that's my biggest problem is displayed not the analysis I'm not talking about 1 2nd for sentences go I'm talking about 1 2nd for the bay area so 226 thousand nodes this is a 10th of that so all of the barrier and about a 2nd and is a general
flexible aggregation tool as well not just the nearest thing but this would some restaurants than 500 meters and you these very local scale and metrics
if I go up to a thousand meters at its smoother together 2 thousand years it is quite
serious and 3 thousand this is that's the surface there's not about so if you want the local scale if you want the regional scale you should and you can pick and choose to use the models you can convey and people always within and just emphasize like what I'm really
doing is this much data and it looks like that which is not pretty something that was only make this
look better I'm I'm game and right now we use geochemists this but like do in the book any questions yes for the and I don't know about
you but I don't know what that is going to get up such simplicity yeah Saul of course that yeah the model itself and some of the you use use about Python statistical libraries to run the
simulation shirt carried can you talk 20 seconds and some of the statistical method to use in shares so I'm I mean we we use a linear regression on log-transformed outcome variables for home
prices and then we most of what we do is the discrete choice theory so we had to come up with some new methods to do the choices among very large choice sets and we we choose amongst the large population of say 200 thousand vacant units and I in a region so like I have this probability distribution over the unit thousand units and so we we compute all that with in Python and we had to write that support that we had the right ourselves that wasn't widely available so we could we come from a discrete choice background everything we do is discrete everything is at the in this creature yeah there is a happened like track totally so the questions about agitation modeling and we II give presentations on this for like land use real estate modeling and agitation is a big deal it's actually required by federal law that every MPO has these very large-scale transportation models and they came up to me afterward these presentations and so Archimedes same thing for us so working on right now have yeah at pleasure time again is so he talked about last 1 2nd NASA calculations also view your sources OpenStreetMap data track so can you talk a little bit about what you do to take you know say giant done from OpenStreetMap you gigs engaged some data in that form to give in the form that you can then use calculations should finish the sets and so I don't know if you've been following the vendors folks but they're trying to that it depends methodology and to geospatial technology I have no idea whether or not you're giving a presentation right now but it's it's saying it's should be a bigger deal than it is and 1 of the developers has been working on OpenStreetMap import you can now just give a bounding box and get and deal constant stream of data within the bounding box using stupendous but is not yet a routable network and we're working on that it's you step there will be done soonish certainly doable just as a substitute to stay out of like PostgreSQL database and just do everything in Python the very question but yeah no for the what's actually like much interest has 3 much everything and during toolkit is 1 might where they call these things from will but
there's like open borders on very like multiple things going on nobody is interested in this case I that I'm I'm not embarrassed if I
it it how it I hope that's true that's true th stands for patterns in his data that have happened that I make this is available and have a if go for different i . com is like a list of papers and you know this I'm a big fan of the Web like
everything should be and get on tumbler and get dangerous like in my life's pretty public at this point just poke around at and if you want to know about the toolkit perhaps the best place to start is simplicity icon slashed toolkit yeah back in Colorado time in your life you know see what was back of the time there of letting the original C plus possible the implantation of of of the the simulation software of natural 0 was trying to do a transportation simulator some always doing it format show here in Portland a wondering if there's any connection between this and that are if you know anything about that I don't think this is a direct connection between the 2 and that there is a long history of these overlapping Urban modelling teams in academics and and that's the thing different regions have a different lineage so that it's quite interesting but I don't know the details on that hi I just wonder whether with their network modeling side that they're the guys looking at terms man that the rich you invited looks at like network acts sugars so on network X and her about 4 years ago my advisor
said we need to bring in our text and surgical again and it's it's predominantly doing like graph matching like a graph that looks like this also looks like this and how similar they are and was really not well suited for accessibility queries and the other big thing everybody's doing is routing and that's a big deal like that there are a lot of really good reasons for that but no and actually transform routing into this analysis engine for network analysis and so there was a gap I've never text definitely didn't cover for us the M. OK yeah
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Metadaten

Formale Metadaten

Titel UrbanSim2: Simulating the Connected Metropolis
Serientitel FOSS4G 2014 Portland
Autor Foti, Fletcher
Waddell, Paul
Lizenz CC-Namensnennung 3.0 Deutschland:
Sie dürfen das Werk bzw. den Inhalt zu jedem legalen Zweck nutzen, verändern und in unveränderter oder veränderter Form vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen.
DOI 10.5446/31624
Herausgeber FOSS4G, Open Source Geospatial Foundation (OSGeo)
Erscheinungsjahr 2014
Sprache Englisch
Produzent FOSS4G
Open Source Geospatial Foundation (OSGeo)
Produktionsjahr 2014
Produktionsort Portland, Oregon, United States of America

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Abstract <style type="text/css"><!--td br --></style>UrbanSim is an open source software platform for agent-based geospatial simulation, focusing on the spatial dynamics of urban development. å Since its creation UrbanSim has been used in the official planningå processes for at least a dozen regional governments which were usedå to help allocate billions of dollars in regional investments in transportationå infrastructure.UrbanSim was first conceptualized in the late 1990's and implementedå using the Java programming language. The technology landscape forå scientific computing changed dramatically after that, and by 2005å UrbanSim was converted to Python, making heavy use of Numpy to vectorizeå calculations. By 2014, it became clear that UrbanSim should be reimplementedå again to take advantage of significant advances in the libraries availableå for scientific Python. The new version of UrbanSim, called UrbanSim2,å makes extensive use of community-supported scientific Python librarieså to reduce the amount of domain-specific customized code to a minimum.UrbanSim is an excellent case study for the power of leveraging thework of the scientific programming community as scaffolding for adomain-specific application, as opposed to building an extensive customizedå solution in each domain. Additionally, the open and participatoryå nature inherent in nearly all of the open source projects describedå here has been particularly embraced by governments, who are oftenå reticent to support large commercial institutions and balkanized andå private data formats and software tools.<style type="text/css"><!--td br -->UrbanSim is an open source software platform for agent-based geospatialå simulation, focusing on the spatial dynamics of urban development. å Since its creation UrbanSim has been used in the official planningå processes for at least a dozen regional governments which were usedå to help allocate billions of dollars in regional investments in transportationå infrastructure.UrbanSim was first conceptualized in the late 1990's and implementedå using the Java programming language. The technology landscape forå scientific computing changed dramatically after that, and by 2005å UrbanSim was converted to Python, making heavy use of Numpy to vectorizeå calculations. By 2014, it became clear that UrbanSim should be reimplementedå again to take advantage of significant advances in the libraries availableå for scientific Python. The new version of UrbanSim, called UrbanSim2,å makes extensive use of community-supported scientific Python librarieså to reduce the amount of domain-specific customized code to a minimum.UrbanSim is an excellent case study for the power of leveraging thework of the scientific programming community as scaffolding for adomain-specific application, as opposed to building an extensive customizedå solution in each domain. Additionally, the open and participatoryå nature inherent in nearly all of the open source projects describedå here has been particularly embraced by governments, who are oftenå reticent to support large commercial institutions and balkanized andå private data formats and software tools.--></style>
Schlagwörter UrbanSim
urban planning
simulation

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