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+ +The Urban Analyst platform aims to be applicable to as much of the world as +possible. To achieve this, data sources are chosen which are ideally have +global coverage, and which are not specific to any one city. The global sources used are:
+The following additional data are then required for each city:
+The last of these data requirements is the most restrictive, as most cities of +the world do not have or provide public transport data in GTFS format. There +are nevertheless thousands of cities which do provide GTFS feeds, as can be +seen for example in the GTFS feed aggregation platform +transit.land.
+ +This chapter demonstrates most of the capabilities of the Urban Analyst +platform through exploring comparisons between the +cities of Paris, France, and Berlin, Germany. It is important to remember +throughout that lower values in all UTA statistics are always better. Values +are also weighted by local population densities. This is important because, for +example, public transport systems should be constructed to offer the fastest +services to the areas where most people live. Not implementing this weighting +would, in contrast, leave measures in some form of times per unit area, so that +for example travel times from unpopulated parts of a city would be weighted +equally to times from densely populated parts. Weighting travel times, and all +other UTA variables, by population density converts them to values as +experienced on average by each person in a city.
+The comparisons in this chapter between Paris and Berlin are mostly drawn from +the "Stats" page, which provides overviews of entire cities, and comparisons +with all other UTA cities. The "Maps" page can then be used to examine the +actual spatial distributions of particular variables or relationships within +any given city.
+This comparison starts by stepping through each variable to describe kinds of +information able to be extracted, before examining pairwise relationships +between these variables, and concluding with a general summary. The Urban +Analyst platform currently measures 11 variables, +along with strengths of relationship between all paired combinations of these. +This amounts to 11 * (11 - 1) / 2 = 55 pairwise combinations. Strengths of +relationship are standardised, so are comparable throughout between all pairs +of variables, and between different cities.
+The following table summarises the values of the individual variables for each +city (each measured on its own distinct scale).
+| Variable | Berlin | Paris |
|---|---|---|
| Times (abs; min) | 40.9 | 39.5 |
| Times (rel) | 1.09 | 1.03 |
| Num. Transfers | 0.9 | 1.5 |
| Intervals (min) | 6.9 | 4.9 |
| Transport | 33.2 | 25.5 |
| Pop. Dens. | 3 | 3 |
| School Dist (m) | 338 | 186 |
| Bike Index | 0.81 | 0.76 |
| Nature Index | 0.88 | 0.93 |
| Parking | 1.32 | 1.55 |
Social disadvantage is also quantified for all cities. However, each city +calculates this in different ways, and values are not comparable between +cities. Values are nevertheless standardised for the pairwise comparisons, and +strengths of relationship described there remain valid.
+The "Transport Absolute" variable measures the absolute time (in minutes) +required to travel a distance of 10km from each point in a city, using any +combination of travel modes except private automobile, including walking, +bicycling, and any available public transport options. Travelling 10km in +Paris takes under 40 minutes on average, while equivalent journeys in +Berlin require almost 1.5 minutes more.
+The "Transport Relative" values divide the absolute travel times described above +by times for equivalent journeys with private automobile. Ratios of one imply +automobile times equal to multi-modal times; ratios of less than one imply that +multi-modal transport is faster than private automobile. Paris and Berlin both +have comparably low values for this ratio, implying relatively fast multi-modal +transport, with Paris notably faster than Berlin. This is likely influenced by +Paris's recent introduction of a uniform maximum speed limits of 30km/hour +through the city, whereas Berlin features a number of "autobahns" with much +higher speed limits.
+Note that travel times with private automobiles include estimates of times +required to park a vehicle and ultimately walk to any desired destination. +Vehicular times calculated here are thus notably longer than with most +commercial routing engines, which give vehicular travel times only, and ignore +the critical need to park a vehicle and walk to a destination.
+All individual variables also enable comparison in terms of "Variation", rather +than "Average" values. Comparing these reveals that Berlin generally has +markedly lower variation than Paris. A comparison of these statistics on the +"Maps" page reveals that this is largely because Paris is simply much larger +than Berlin, and the ranges of both absolute and relative transport times are +correspondingly greater. The fact that relative transport in Paris is still +better on average than in Berlin is thus even more impressive considering this +stark difference in scale.
+Travelling in Paris requires notably greater numbers of transfers to travel +equivalent distances than Berlin. The values in the "Number of Transfers" layer +are for journeys of 10km total distance (including walking or cycling distances +at either end). Travelling in Paris requires > 50% more transfers than journeys +in Berlin.
+The fourth transport variable, "Interval", measures the time to wait (in +minutes) until the next equivalent service. Intervals in Paris are slightly +under 5 minutes, whereas values in Berlin are just under 7 minutes.
+Finally, the "Compound Transport" variable simply multiplies absolute travel +times by intervals between services. Low values of this statistic reflect fast +and frequent transport. This statistic also indicates considerably superior +service in Paris compared with Berlin.
+This section considers relationships between each individual variable and all +other variables. All strengths of relationship shown in the "Stats" page are +assessed in standardised ways, so they may be directly compared between cities. +Moreover, the scales shown in the "Stats" page may also be directly compared. +Values of one or greater indicate very strong relationships, whereas values +less than 0.1 or so indicate weak relationships, and values less than around +0.01 should generally be interpret to indicate no relationship. Pairs of +variables with very weak or negligible strengths of relationship are generally +not interpreted in the following sub-sections.
+The following table summarises the values of the strongest pairwise +relationships for each city:
+| Variable1 | Variable2 | Berlin | Paris |
|---|---|---|---|
| Times (abs) | Bike | 1.0 | 2.0 |
| Times (abs) | Natural | -1.0 | -0.5 |
| Times (abs) | Parking | 0 | -0.15 |
| Times (abs) | Pop. Dens. | -0.15 | -0.11 |
| Times (abs) | School dist | 0.12 | 0.06 |
| Times (abs) | Transfers | -0.31 | -0.48 |
| Times (rel) | Bike | 0 | 0.16 |
| Transport | Natural | -0.22 | 2.46 |
| Transport | Parking | 1.7 | 1.9 |
| School dist | Bike | 0 | 0.4 |
| School dist | Natural | -0.12 | -0.06 |
| Social | Bike | 0.52 | -0.38 |
| Social | Natural | -0.1 | 2.0 |
| Social | Parking | 0.04 | -2.18 |
| Social | School dist | -0.05 | -0.25 |
This sub-section only considers transport times, both in absolute and relative +sense. The other transport variables, of intervals and numbers of transfers, +generally follow similar patterns and are not explicitly considered here. +Relative transport times are only very weakly related to most other variables. +In contrast, absolute transport times are strongly related to most other +variables.
+Relative transport times are negligibly associated with population densities, +while absolute times are particularly strongly and negatively correlated. These +negative relationships indicate that faster transport is associated with higher +population densities, more so in Berlin than Paris.
+Slightly weaker relationships are manifest between absolute travel times and +distances to nearest schools. Relationships in both Berlin and Paris are +positive, indicating that fast public transport is positively associated with +shorter distances to schools, with the relationship about twice as strong in +Berlin as in Paris.
+Travel times are very strongly, and positively, correlated with bicycle +infrastructure, indicating faster travel times in regions with better bicycle +infrastructure. This relationship is much stronger in Paris than in Berlin, for +reasons easy to discern by looking at the maps of Berlin for these two +variables. Bicycle infrastructure there is much better in the periphery of the +city, whereas transport times exhibit more of a systematic discrepancy between +the east (fast) and west (slow) portions of the city. In Paris, in contrast, +faster transport times and better bicycle infrastructure are both concentrated +more towards the centre of the city.
+Relationships between transport times and the index of accessibility to natural +spaces are also very strong, and negative. This means that faster transport +times are associated with lower accessibility to natural spaces, as might be +generally expected of most high-density cities. The relationship is stronger in +Berlin than Paris, indicating that faster transport times are most strongly +associated with poorer access to natural spaces there than in Paris.
+Finally, absolute transport times are slightly negatively associated with +numbers of automobile parking spaces in Paris, whereas there is no relationship +in Berlin. This negative relationship indicates that regions with faster public +transport also tend to have more automobile parking spaces, reflecting planning +decisions that associate use of public transport with the driving of private +automobiles. No such relationship appears to exist in Berlin.
+Shorter school distances are positively associated with the bicycle index in +Paris, indicating a positive association between good bicycle infrastructure +and short distances to schools. Berlin manifests no such relationship, likely +for reasons described above, that bicycle infrastructure in Berlin is generally +more peripheral than in Paris.
+Although much weaker, relationships between schools distances and the index of +accessibility to natural spaces are negative, indicating that locations closer +to schools are further from nature, and more so in Berlin than in Paris.
+Finally, the social variables are more strongly related to all other +non-transport variables in Paris than in Berlin, except for with the index of +bicycle infrastructure. This variable is more strongly, and positively, +correlated with the social indicator in Berlin than in Paris, where the +relationship is negative. The positive relationship in Berlin indicates that +the provision of bicycle infrastructure is positively associated with social +advantage, an effect again readily seen in examining the map of Berlin. In +contrast, Paris is more effective in providing bicycle infrastructure in areas +of relative social disadvantage.
+Paris also seems to be more effective in educational provision in areas of +social disadvantage, with the strong negative correlation indicating that +socially disadvantaged Parisians generally have to travel shorter distances to +schools. Although this relationship is also negative in Berlin, it is much +weaker.
+In contrast, Paris's very strong and positive relationship between social +advantage and access to natural spaces indicates the relatively far greater +difficulty experienced by less socially advantaged Parisians in accessing +natural spaces compared with equivalent inhabitants of Berlin.
+Finally, Paris manifests a very strong and negative association between social +advantage and numbers of automobile parking spaces, indicating that low social +disadvantage is strongly associated with high numbers of automobile parking +spaces, or conversely that socially disadvantaged parts of the city offer +relatively few automobile parking spaces. The relationship in Berlin is, in +contrast, slightly positive.
+Paris's transport system is considerably faster and more frequent. +Nevertheless, it also involves greater numbers of transfers, suggesting that +any attempt to improve the system in Berlin should take care to avoid +inadvertently increasing numbers of transfers.
+Berlin's average relative speed is also notably higher than Paris's, and at +1.09 likely too high to effectively discourage large numbers of people from +opting to travel via private automobile. Examination of the map of relative +travel times clearly reveals the effect of the connected ring out autobahns +encircling the city. While reducing speeds on these carriageways may not be +feasible, a uniform 30km/hour limit as introduced in Paris may nevertheless +significantly reduce this ratio, and further incentivise many more people to +opt for public transport rather than private automobile.
+Although Paris is a far larger city, its average population density is +nevertheless very similar to Berlin's. It is then even more striking that Paris +offers considerably shorter average distances to schools than Berlin. School +distances in Berlin are also only weakly correlated with social conditions, +whereas average distances to schools in Paris are shorter in less socially +advantaged areas. Both of these factors indicate a need in Berlin for more +provision of local schooling in general, and particularly in socially +disadvantaged regions, if it is to match the educational opportunities provided +in Paris.
+Paris's bicycle infrastructure is considerably better than Berlin's, and +perhaps even more importantly, becomes better towards the inner city regions. +In contrast, Berlin really only offers good bicycle infrastructure in the +relatively peripheral, and more affluent, outer regions. Berlin really needs to +proactively focus on improving bicycle infrastructure in the inner city +regions.
+Berlin is fortunately greatly enhanced by an abundance of natural space, +including access to the city's rivers and canals, and access to these natural +spaces is only weakly related to social advantage. This provides robust +evidence for Berlin to appreciate its natural spaces, and to ensure that they +remain accessible for everybody.
+Paris's transport system is notably better than Berlin's in almost all ways +except for the number of transfers necessary to travel equivalent distances. +This difference is especially notable given that Paris is much larger than +Berlin. Improvements to Paris's public transport system should focus on +decreasing numbers of transfers.
+Paris's average relative speed is very close to the "magical" value of one, at +which point private automobiles are no faster than multi-modal transport +including walking and cycling.
+Paris has done a great job of providing bicycle infrastructure in the inner +city regions, and notably of proactively enhancing or creating bicycle +infrastructure in regions of social disadvantage.
+Contrasts with Berlin nevertheless emphasise a couple of aspects which Paris +could focus on improving. The most notable of these is the index of +accessibility to natural spaces, and the relationship of this to other +variables. Paris simply has far less natural space than Berlin, and much poorer +general accessibility. Moreover, access to natural spaces is positively +associated with social advantage, so that it is relatively difficult for +socially disadvantaged Parisians to access natural spaces.
+ +This is the documentation for Urban Analyst (UA). +Urban Analyst provides open source analyses of the structure and function of +cities across the world. Each city can be viewed as an interactive +map displaying a range of properties or +variables. These include socio-demographic conditions and the structure and +function of transport systems. The platform also analyses relationships between +individual variables, such as between socio-demographic conditions and +frequency of transport services, or between distances to nearest schools and +access to natural spaces.
+UA also provides statistical comparisons +between all cities, enabling relationships between any pair of variables, such +as transport and socio-demographic disadvantage, to be compared across all UA +cities.
+Finally, UA enables cities to "learn" from one another, by visualising how the +properties of any chosen city can best be transformed to become more like the +properties of any other chosen city. Paris, for example, has better bicycle +infrastructure than Berlin, and the UA transformation algorithm can calculate +how Berlin can most easily transform its bicycle infrastructure to become more +like Paris. Values for every area in Berlin are then displayed as the +proportional increase in bicycle infrastructure which would be necessary for +the whole city to have infrastructure equivalent to Paris.
+Urban Analyst present a variety of statistics for each city +analysed, as well as relationships between these statistics. Values for each +statistic are derived at every street intersection in each city. These values +are then aggregated into the polygons shown in the "Map" +page, and across entire cities for the values +shown in the "Compare" and +"Transform" pages. Aggregations are +always weighted by local population densities, so that all UA values represent +values per person as experienced in each city. Details are provided in the +Data Sources and Software and Algorithms +chapters.
+The values presented in Urban Analyst are derived from estimates of travel +times from every point in each city to every other point using any combination +of possible modes of transport. The following table summarises numbers of +street intersections, public transport ("PT") stops, and calculations for a +selection of Urban Analyst cities.
+| city | intersections (thousands) | PT stops | PT calcs (millions) | routing calcs (billions) |
|---|---|---|---|---|
| Berlin | 360 | 9,302 | 173 | 150 |
| Hamburg | 192 | 4,585 | 42 | 56 |
| London | 506 | 20,072 | 806 | 160 |
| Paris | 313 | 29,393 | 1,728 | 124 |
One way to appreciate the scale of these calculations is through comparison +with commercial alternatives. One service, traveltime.com, charges a flat +subscription fee of €540 per month for a maximum of 60 requests per minute. +That rate would permit 31.5 million queries per year. The city of Hamburg, for +example, would then take almost 2,000 years to calculate, and would cost +€12 million. Google also offers a commercial routing service, limited to a +maximum of 500,000 queries per month, for a total price of US$2,000. At that +rate, the analyses for Hamburg would cost US$224 million.
+The results presented in Urban Analyst are simply not possible using commercial +tools, or indeed any other open source tools. These analyses truly are uniquely +powerful, and provide a depth of insight into how people move through cities +not available in any other way.
+Not directly, but feel free to open a GitHub +issue to start a discussion +about requesting full data sources.
+This documentation includes the following five chapters:
+Contributions to, or questions regarding, this documentation, are welcome at +this GitHub repository.
+ +This is the documentation for Urban Analyst (UA). +Urban Analyst provides open source analyses of the structure and function of +cities across the world. Each city can be viewed as an interactive +map displaying a range of properties or +variables. These include socio-demographic conditions and the structure and +function of transport systems. The platform also analyses relationships between +individual variables, such as between socio-demographic conditions and +frequency of transport services, or between distances to nearest schools and +access to natural spaces.
+UA also provides statistical comparisons +between all cities, enabling relationships between any pair of variables, such +as transport and socio-demographic disadvantage, to be compared across all UA +cities.
+Finally, UA enables cities to "learn" from one another, by visualising how the +properties of any chosen city can best be transformed to become more like the +properties of any other chosen city. Paris, for example, has better bicycle +infrastructure than Berlin, and the UA transformation algorithm can calculate +how Berlin can most easily transform its bicycle infrastructure to become more +like Paris. Values for every area in Berlin are then displayed as the +proportional increase in bicycle infrastructure which would be necessary for +the whole city to have infrastructure equivalent to Paris.
+Urban Analyst present a variety of statistics for each city +analysed, as well as relationships between these statistics. Values for each +statistic are derived at every street intersection in each city. These values +are then aggregated into the polygons shown in the "Map" +page, and across entire cities for the values +shown in the "Compare" and +"Transform" pages. Aggregations are +always weighted by local population densities, so that all UA values represent +values per person as experienced in each city. Details are provided in the +Data Sources and Software and Algorithms +chapters.
+The values presented in Urban Analyst are derived from estimates of travel +times from every point in each city to every other point using any combination +of possible modes of transport. The following table summarises numbers of +street intersections, public transport ("PT") stops, and calculations for a +selection of Urban Analyst cities.
+| city | intersections (thousands) | PT stops | PT calcs (millions) | routing calcs (billions) |
|---|---|---|---|---|
| Berlin | 360 | 9,302 | 173 | 150 |
| Hamburg | 192 | 4,585 | 42 | 56 |
| London | 506 | 20,072 | 806 | 160 |
| Paris | 313 | 29,393 | 1,728 | 124 |
One way to appreciate the scale of these calculations is through comparison +with commercial alternatives. One service, traveltime.com, charges a flat +subscription fee of €540 per month for a maximum of 60 requests per minute. +That rate would permit 31.5 million queries per year. The city of Hamburg, for +example, would then take almost 2,000 years to calculate, and would cost +€12 million. Google also offers a commercial routing service, limited to a +maximum of 500,000 queries per month, for a total price of US$2,000. At that +rate, the analyses for Hamburg would cost US$224 million.
+The results presented in Urban Analyst are simply not possible using commercial +tools, or indeed any other open source tools. These analyses truly are uniquely +powerful, and provide a depth of insight into how people move through cities +not available in any other way.
+Not directly, but feel free to open a GitHub +issue to start a discussion +about requesting full data sources.
+This documentation includes the following five chapters:
+Contributions to, or questions regarding, this documentation, are welcome at +this GitHub repository.
+ +This chapter provides an overview of the three main pages of the Urban Analyst +platform:
+The Summarise page is the best place to +start. The summary for any chosen city will indicate which properties of the +other pages are most important. Each of the latter three pages also includes a +pop-up "guided tour" explaining the key features. These tours will +automatically start the first time a page is opened, or can be manually opened +by clicking on the "Help" button on any of the main pages.
+The Summarise page provides an overview +of the general statistical properties of any chosen UA city. Statistics for +each chosen city are compared with those for all other cities, and textual +summaries generated for all statistics which are significantly different. +Summaries are provided not just for individual statistics, but for the +strengths of relationship between all pairs of statistics.
+The values described in this initial part of the Summarise +page indicate which statistics might be +particularly worth examining in both the +Compare and the Map + pages.
+The Summarise page also includes a +section describing the best "Target city" for each chosen city. As explained +there, the best target city is the city which is best in all the ways that the +chosen city is worse than average. This target city, and corresponding +variables described there, indicate which cities and variables might be +particularly worth examining in the Transform +page.
+The Compare page is the first and main +page of Urban Analyst, enabling the properties of all UA cities to be compared. +A pull-down panel enables each variable or "layer" to be selected. The page +then displays a graphical representation of values of the chosen layer for all +cities. As in all UA pages, lower values are generally better than higher +values. The control panel includes an "Explain Layer" button which opens a text +panel explaining details of the chosen variable.
+The control panel of the compare page +includes an option to select "paired" variables. The resultant graphs then +display the strength of relationship between any chosen pair of variables. +For example, choosing social index and the nature index will display the +strength of relationship between access to natural spaces and social +disadvantage. Both of these variables are measured such that low values are +better than high values. A positive relationship between the two would then +mean that lower social disadvantage is coupled with better access to natural +spaces, while high social disadvantage is coupled with worse access to natural +spaces. Conversely, a negative relationship would indicate that higher social +disadvantage was coupled with better access to natural resources. Or, in the +words brought up by clicking the "Explain Layer" button, "Low values indicate +that good access to natural spaces is coupled with disadvantageous social +conditions."
+The Map page shows interactive maps for each +city, with values for all UA variables displayed in small polygons. These +polygons are defined by city-specific assessments of spatial disadvantage. +Berlin, for example, regularly measures a compound index of social disadvantage +aggregated into XX polygons. The map for Berlin uses these polygons provided by +the city to aggregate all measured variables. The variables are described in a +subsequent chapter.
+As in all UA measurements, lower values of all variables are generally better +than higher values. Colour scales on all maps thus generally display lower +values in brighter, yellow colours, while higher values are displayed in +darker, blue or violet colours. The control panel includes an "Explain Layer" +button which opens a text panel explaining details of the chosen variable.
+The Transform page enables the +properties of any chosen city to be transformed to reflect equivalent +properties of some chosen "target" city. This page is best explained by an +example. Looking at the Compare page for +the "bike index" shows that Berlin has relatively poor bicycle infrastructure, +while Paris is a very good city for cyclists. The +Transform page can be used to visualise +how Berlin could best transform its current bicycle infrastructure to have an +overall distribution across the whole city equivalent to Paris. Conversely, +Paris has poorer access to natural spaces than Berlin, and the page could also +be used to examine how Paris could best transform its access to natural spaces +so that it functioned more like Berlin.
+Urban Analyst values displayed in the Map page +are aggregated from generally hundreds of thousands of individual calculations +at every street junction in each city. For the chosen variable, subsets of +these individual data points are sampled from each city, and the statistical +distribution for the chosen city is then transformed by changing each point by +the smallest amount possible so that they reflect the distribution in the +target city. These values are then aggregated into the polygons defined for the +city, to produce a visual representation of the least-cost transformation that +would be necessary for the city to have the same distribution as that of the +target city. The transformation algorithm is described in detail in the final +Software and Algorithms chapter.
+The Transform page includes an +additional button labelled Extra Layers. The transformations described above +described transforming single layers or variables. The Extra Layers panel +enables transformations not just of single chosen variables, but also of their +relationships with other variables. Examining the Compare +page, for example, shows that not only does +Paris provide poorer access to natural spaces than Berlin, but also that Berlin +has a better relationship between access to natural spaces and social +disadvantage. (This can be seen by clicking on the "Paired" layer option and +selecting those two layers.) The Extra Layers panel can be used in this case +to examine not just how Paris might best transform its access to nature to look +more like Berlin, but also how it might also improve its relationship between +access to nature and social disadvantage.
+By default, values of Extra Layers are automatically selected as those which +have better relationships in the target city. These default values will thus +change for each choice of target city and focal layer. It may be necessary to +click on the "Reset" button in the Extra Layers panel to update this default +selection after changing any of these options.
+Finally, the Transform page also has an +Output Layer option at the bottom of the control panel. This enables results +of the transformation algorithm to be displayed in one of four ways:
+This is the documentation for Urban Analyst (UA). +Urban Analyst provides open source analyses of the structure and function of +cities across the world. Each city can be viewed as an interactive +map displaying a range of properties or +variables. These include socio-demographic conditions and the structure and +function of transport systems. The platform also analyses relationships between +individual variables, such as between socio-demographic conditions and +frequency of transport services, or between distances to nearest schools and +access to natural spaces.
+UA also provides statistical comparisons +between all cities, enabling relationships between any pair of variables, such +as transport and socio-demographic disadvantage, to be compared across all UA +cities.
+Finally, UA enables cities to "learn" from one another, by visualising how the +properties of any chosen city can best be transformed to become more like the +properties of any other chosen city. Paris, for example, has better bicycle +infrastructure than Berlin, and the UA transformation algorithm can calculate +how Berlin can most easily transform its bicycle infrastructure to become more +like Paris. Values for every area in Berlin are then displayed as the +proportional increase in bicycle infrastructure which would be necessary for +the whole city to have infrastructure equivalent to Paris.
+Urban Analyst present a variety of statistics for each city +analysed, as well as relationships between these statistics. Values for each +statistic are derived at every street intersection in each city. These values +are then aggregated into the polygons shown in the "Map" +page, and across entire cities for the values +shown in the "Compare" and +"Transform" pages. Aggregations are +always weighted by local population densities, so that all UA values represent +values per person as experienced in each city. Details are provided in the +Data Sources and Software and Algorithms +chapters.
+The values presented in Urban Analyst are derived from estimates of travel +times from every point in each city to every other point using any combination +of possible modes of transport. The following table summarises numbers of +street intersections, public transport ("PT") stops, and calculations for a +selection of Urban Analyst cities.
+| city | intersections (thousands) | PT stops | PT calcs (millions) | routing calcs (billions) |
|---|---|---|---|---|
| Berlin | 360 | 9,302 | 173 | 150 |
| Hamburg | 192 | 4,585 | 42 | 56 |
| London | 506 | 20,072 | 806 | 160 |
| Paris | 313 | 29,393 | 1,728 | 124 |
One way to appreciate the scale of these calculations is through comparison +with commercial alternatives. One service, traveltime.com, charges a flat +subscription fee of €540 per month for a maximum of 60 requests per minute. +That rate would permit 31.5 million queries per year. The city of Hamburg, for +example, would then take almost 2,000 years to calculate, and would cost +€12 million. Google also offers a commercial routing service, limited to a +maximum of 500,000 queries per month, for a total price of US$2,000. At that +rate, the analyses for Hamburg would cost US$224 million.
+The results presented in Urban Analyst are simply not possible using commercial +tools, or indeed any other open source tools. These analyses truly are uniquely +powerful, and provide a depth of insight into how people move through cities +not available in any other way.
+Not directly, but feel free to open a GitHub +issue to start a discussion +about requesting full data sources.
+This documentation includes the following five chapters:
+Contributions to, or questions regarding, this documentation, are welcome at +this GitHub repository.
+This chapter provides an overview of the three main pages of the Urban Analyst +platform:
+The Summarise page is the best place to +start. The summary for any chosen city will indicate which properties of the +other pages are most important. Each of the latter three pages also includes a +pop-up "guided tour" explaining the key features. These tours will +automatically start the first time a page is opened, or can be manually opened +by clicking on the "Help" button on any of the main pages.
+The Summarise page provides an overview +of the general statistical properties of any chosen UA city. Statistics for +each chosen city are compared with those for all other cities, and textual +summaries generated for all statistics which are significantly different. +Summaries are provided not just for individual statistics, but for the +strengths of relationship between all pairs of statistics.
+The values described in this initial part of the Summarise +page indicate which statistics might be +particularly worth examining in both the +Compare and the Map + pages.
+The Summarise page also includes a +section describing the best "Target city" for each chosen city. As explained +there, the best target city is the city which is best in all the ways that the +chosen city is worse than average. This target city, and corresponding +variables described there, indicate which cities and variables might be +particularly worth examining in the Transform +page.
+The Compare page is the first and main +page of Urban Analyst, enabling the properties of all UA cities to be compared. +A pull-down panel enables each variable or "layer" to be selected. The page +then displays a graphical representation of values of the chosen layer for all +cities. As in all UA pages, lower values are generally better than higher +values. The control panel includes an "Explain Layer" button which opens a text +panel explaining details of the chosen variable.
+The control panel of the compare page +includes an option to select "paired" variables. The resultant graphs then +display the strength of relationship between any chosen pair of variables. +For example, choosing social index and the nature index will display the +strength of relationship between access to natural spaces and social +disadvantage. Both of these variables are measured such that low values are +better than high values. A positive relationship between the two would then +mean that lower social disadvantage is coupled with better access to natural +spaces, while high social disadvantage is coupled with worse access to natural +spaces. Conversely, a negative relationship would indicate that higher social +disadvantage was coupled with better access to natural resources. Or, in the +words brought up by clicking the "Explain Layer" button, "Low values indicate +that good access to natural spaces is coupled with disadvantageous social +conditions."
+The Map page shows interactive maps for each +city, with values for all UA variables displayed in small polygons. These +polygons are defined by city-specific assessments of spatial disadvantage. +Berlin, for example, regularly measures a compound index of social disadvantage +aggregated into XX polygons. The map for Berlin uses these polygons provided by +the city to aggregate all measured variables. The variables are described in a +subsequent chapter.
+As in all UA measurements, lower values of all variables are generally better +than higher values. Colour scales on all maps thus generally display lower +values in brighter, yellow colours, while higher values are displayed in +darker, blue or violet colours. The control panel includes an "Explain Layer" +button which opens a text panel explaining details of the chosen variable.
+The Transform page enables the +properties of any chosen city to be transformed to reflect equivalent +properties of some chosen "target" city. This page is best explained by an +example. Looking at the Compare page for +the "bike index" shows that Berlin has relatively poor bicycle infrastructure, +while Paris is a very good city for cyclists. The +Transform page can be used to visualise +how Berlin could best transform its current bicycle infrastructure to have an +overall distribution across the whole city equivalent to Paris. Conversely, +Paris has poorer access to natural spaces than Berlin, and the page could also +be used to examine how Paris could best transform its access to natural spaces +so that it functioned more like Berlin.
+Urban Analyst values displayed in the Map page +are aggregated from generally hundreds of thousands of individual calculations +at every street junction in each city. For the chosen variable, subsets of +these individual data points are sampled from each city, and the statistical +distribution for the chosen city is then transformed by changing each point by +the smallest amount possible so that they reflect the distribution in the +target city. These values are then aggregated into the polygons defined for the +city, to produce a visual representation of the least-cost transformation that +would be necessary for the city to have the same distribution as that of the +target city. The transformation algorithm is described in detail in the final +Software and Algorithms chapter.
+The Transform page includes an +additional button labelled Extra Layers. The transformations described above +described transforming single layers or variables. The Extra Layers panel +enables transformations not just of single chosen variables, but also of their +relationships with other variables. Examining the Compare +page, for example, shows that not only does +Paris provide poorer access to natural spaces than Berlin, but also that Berlin +has a better relationship between access to natural spaces and social +disadvantage. (This can be seen by clicking on the "Paired" layer option and +selecting those two layers.) The Extra Layers panel can be used in this case +to examine not just how Paris might best transform its access to nature to look +more like Berlin, but also how it might also improve its relationship between +access to nature and social disadvantage.
+By default, values of Extra Layers are automatically selected as those which +have better relationships in the target city. These default values will thus +change for each choice of target city and focal layer. It may be necessary to +click on the "Reset" button in the Extra Layers panel to update this default +selection after changing any of these options.
+Finally, the Transform page also has an +Output Layer option at the bottom of the control panel. This enables results +of the transformation algorithm to be displayed in one of four ways:
+This chapter demonstrates most of the capabilities of the Urban Analyst +platform through exploring comparisons between the +cities of Paris, France, and Berlin, Germany. It is important to remember +throughout that lower values in all UTA statistics are always better. Values +are also weighted by local population densities. This is important because, for +example, public transport systems should be constructed to offer the fastest +services to the areas where most people live. Not implementing this weighting +would, in contrast, leave measures in some form of times per unit area, so that +for example travel times from unpopulated parts of a city would be weighted +equally to times from densely populated parts. Weighting travel times, and all +other UTA variables, by population density converts them to values as +experienced on average by each person in a city.
+The comparisons in this chapter between Paris and Berlin are mostly drawn from +the "Stats" page, which provides overviews of entire cities, and comparisons +with all other UTA cities. The "Maps" page can then be used to examine the +actual spatial distributions of particular variables or relationships within +any given city.
+This comparison starts by stepping through each variable to describe kinds of +information able to be extracted, before examining pairwise relationships +between these variables, and concluding with a general summary. The Urban +Analyst platform currently measures 11 variables, +along with strengths of relationship between all paired combinations of these. +This amounts to 11 * (11 - 1) / 2 = 55 pairwise combinations. Strengths of +relationship are standardised, so are comparable throughout between all pairs +of variables, and between different cities.
+The following table summarises the values of the individual variables for each +city (each measured on its own distinct scale).
+| Variable | Berlin | Paris |
|---|---|---|
| Times (abs; min) | 40.9 | 39.5 |
| Times (rel) | 1.09 | 1.03 |
| Num. Transfers | 0.9 | 1.5 |
| Intervals (min) | 6.9 | 4.9 |
| Transport | 33.2 | 25.5 |
| Pop. Dens. | 3 | 3 |
| School Dist (m) | 338 | 186 |
| Bike Index | 0.81 | 0.76 |
| Nature Index | 0.88 | 0.93 |
| Parking | 1.32 | 1.55 |
Social disadvantage is also quantified for all cities. However, each city +calculates this in different ways, and values are not comparable between +cities. Values are nevertheless standardised for the pairwise comparisons, and +strengths of relationship described there remain valid.
+The "Transport Absolute" variable measures the absolute time (in minutes) +required to travel a distance of 10km from each point in a city, using any +combination of travel modes except private automobile, including walking, +bicycling, and any available public transport options. Travelling 10km in +Paris takes under 40 minutes on average, while equivalent journeys in +Berlin require almost 1.5 minutes more.
+The "Transport Relative" values divide the absolute travel times described above +by times for equivalent journeys with private automobile. Ratios of one imply +automobile times equal to multi-modal times; ratios of less than one imply that +multi-modal transport is faster than private automobile. Paris and Berlin both +have comparably low values for this ratio, implying relatively fast multi-modal +transport, with Paris notably faster than Berlin. This is likely influenced by +Paris's recent introduction of a uniform maximum speed limits of 30km/hour +through the city, whereas Berlin features a number of "autobahns" with much +higher speed limits.
+Note that travel times with private automobiles include estimates of times +required to park a vehicle and ultimately walk to any desired destination. +Vehicular times calculated here are thus notably longer than with most +commercial routing engines, which give vehicular travel times only, and ignore +the critical need to park a vehicle and walk to a destination.
+All individual variables also enable comparison in terms of "Variation", rather +than "Average" values. Comparing these reveals that Berlin generally has +markedly lower variation than Paris. A comparison of these statistics on the +"Maps" page reveals that this is largely because Paris is simply much larger +than Berlin, and the ranges of both absolute and relative transport times are +correspondingly greater. The fact that relative transport in Paris is still +better on average than in Berlin is thus even more impressive considering this +stark difference in scale.
+Travelling in Paris requires notably greater numbers of transfers to travel +equivalent distances than Berlin. The values in the "Number of Transfers" layer +are for journeys of 10km total distance (including walking or cycling distances +at either end). Travelling in Paris requires > 50% more transfers than journeys +in Berlin.
+The fourth transport variable, "Interval", measures the time to wait (in +minutes) until the next equivalent service. Intervals in Paris are slightly +under 5 minutes, whereas values in Berlin are just under 7 minutes.
+Finally, the "Compound Transport" variable simply multiplies absolute travel +times by intervals between services. Low values of this statistic reflect fast +and frequent transport. This statistic also indicates considerably superior +service in Paris compared with Berlin.
+This section considers relationships between each individual variable and all +other variables. All strengths of relationship shown in the "Stats" page are +assessed in standardised ways, so they may be directly compared between cities. +Moreover, the scales shown in the "Stats" page may also be directly compared. +Values of one or greater indicate very strong relationships, whereas values +less than 0.1 or so indicate weak relationships, and values less than around +0.01 should generally be interpret to indicate no relationship. Pairs of +variables with very weak or negligible strengths of relationship are generally +not interpreted in the following sub-sections.
+The following table summarises the values of the strongest pairwise +relationships for each city:
+| Variable1 | Variable2 | Berlin | Paris |
|---|---|---|---|
| Times (abs) | Bike | 1.0 | 2.0 |
| Times (abs) | Natural | -1.0 | -0.5 |
| Times (abs) | Parking | 0 | -0.15 |
| Times (abs) | Pop. Dens. | -0.15 | -0.11 |
| Times (abs) | School dist | 0.12 | 0.06 |
| Times (abs) | Transfers | -0.31 | -0.48 |
| Times (rel) | Bike | 0 | 0.16 |
| Transport | Natural | -0.22 | 2.46 |
| Transport | Parking | 1.7 | 1.9 |
| School dist | Bike | 0 | 0.4 |
| School dist | Natural | -0.12 | -0.06 |
| Social | Bike | 0.52 | -0.38 |
| Social | Natural | -0.1 | 2.0 |
| Social | Parking | 0.04 | -2.18 |
| Social | School dist | -0.05 | -0.25 |
This sub-section only considers transport times, both in absolute and relative +sense. The other transport variables, of intervals and numbers of transfers, +generally follow similar patterns and are not explicitly considered here. +Relative transport times are only very weakly related to most other variables. +In contrast, absolute transport times are strongly related to most other +variables.
+Relative transport times are negligibly associated with population densities, +while absolute times are particularly strongly and negatively correlated. These +negative relationships indicate that faster transport is associated with higher +population densities, more so in Berlin than Paris.
+Slightly weaker relationships are manifest between absolute travel times and +distances to nearest schools. Relationships in both Berlin and Paris are +positive, indicating that fast public transport is positively associated with +shorter distances to schools, with the relationship about twice as strong in +Berlin as in Paris.
+Travel times are very strongly, and positively, correlated with bicycle +infrastructure, indicating faster travel times in regions with better bicycle +infrastructure. This relationship is much stronger in Paris than in Berlin, for +reasons easy to discern by looking at the maps of Berlin for these two +variables. Bicycle infrastructure there is much better in the periphery of the +city, whereas transport times exhibit more of a systematic discrepancy between +the east (fast) and west (slow) portions of the city. In Paris, in contrast, +faster transport times and better bicycle infrastructure are both concentrated +more towards the centre of the city.
+Relationships between transport times and the index of accessibility to natural +spaces are also very strong, and negative. This means that faster transport +times are associated with lower accessibility to natural spaces, as might be +generally expected of most high-density cities. The relationship is stronger in +Berlin than Paris, indicating that faster transport times are most strongly +associated with poorer access to natural spaces there than in Paris.
+Finally, absolute transport times are slightly negatively associated with +numbers of automobile parking spaces in Paris, whereas there is no relationship +in Berlin. This negative relationship indicates that regions with faster public +transport also tend to have more automobile parking spaces, reflecting planning +decisions that associate use of public transport with the driving of private +automobiles. No such relationship appears to exist in Berlin.
+Shorter school distances are positively associated with the bicycle index in +Paris, indicating a positive association between good bicycle infrastructure +and short distances to schools. Berlin manifests no such relationship, likely +for reasons described above, that bicycle infrastructure in Berlin is generally +more peripheral than in Paris.
+Although much weaker, relationships between schools distances and the index of +accessibility to natural spaces are negative, indicating that locations closer +to schools are further from nature, and more so in Berlin than in Paris.
+Finally, the social variables are more strongly related to all other +non-transport variables in Paris than in Berlin, except for with the index of +bicycle infrastructure. This variable is more strongly, and positively, +correlated with the social indicator in Berlin than in Paris, where the +relationship is negative. The positive relationship in Berlin indicates that +the provision of bicycle infrastructure is positively associated with social +advantage, an effect again readily seen in examining the map of Berlin. In +contrast, Paris is more effective in providing bicycle infrastructure in areas +of relative social disadvantage.
+Paris also seems to be more effective in educational provision in areas of +social disadvantage, with the strong negative correlation indicating that +socially disadvantaged Parisians generally have to travel shorter distances to +schools. Although this relationship is also negative in Berlin, it is much +weaker.
+In contrast, Paris's very strong and positive relationship between social +advantage and access to natural spaces indicates the relatively far greater +difficulty experienced by less socially advantaged Parisians in accessing +natural spaces compared with equivalent inhabitants of Berlin.
+Finally, Paris manifests a very strong and negative association between social +advantage and numbers of automobile parking spaces, indicating that low social +disadvantage is strongly associated with high numbers of automobile parking +spaces, or conversely that socially disadvantaged parts of the city offer +relatively few automobile parking spaces. The relationship in Berlin is, in +contrast, slightly positive.
+Paris's transport system is considerably faster and more frequent. +Nevertheless, it also involves greater numbers of transfers, suggesting that +any attempt to improve the system in Berlin should take care to avoid +inadvertently increasing numbers of transfers.
+Berlin's average relative speed is also notably higher than Paris's, and at +1.09 likely too high to effectively discourage large numbers of people from +opting to travel via private automobile. Examination of the map of relative +travel times clearly reveals the effect of the connected ring out autobahns +encircling the city. While reducing speeds on these carriageways may not be +feasible, a uniform 30km/hour limit as introduced in Paris may nevertheless +significantly reduce this ratio, and further incentivise many more people to +opt for public transport rather than private automobile.
+Although Paris is a far larger city, its average population density is +nevertheless very similar to Berlin's. It is then even more striking that Paris +offers considerably shorter average distances to schools than Berlin. School +distances in Berlin are also only weakly correlated with social conditions, +whereas average distances to schools in Paris are shorter in less socially +advantaged areas. Both of these factors indicate a need in Berlin for more +provision of local schooling in general, and particularly in socially +disadvantaged regions, if it is to match the educational opportunities provided +in Paris.
+Paris's bicycle infrastructure is considerably better than Berlin's, and +perhaps even more importantly, becomes better towards the inner city regions. +In contrast, Berlin really only offers good bicycle infrastructure in the +relatively peripheral, and more affluent, outer regions. Berlin really needs to +proactively focus on improving bicycle infrastructure in the inner city +regions.
+Berlin is fortunately greatly enhanced by an abundance of natural space, +including access to the city's rivers and canals, and access to these natural +spaces is only weakly related to social advantage. This provides robust +evidence for Berlin to appreciate its natural spaces, and to ensure that they +remain accessible for everybody.
+Paris's transport system is notably better than Berlin's in almost all ways +except for the number of transfers necessary to travel equivalent distances. +This difference is especially notable given that Paris is much larger than +Berlin. Improvements to Paris's public transport system should focus on +decreasing numbers of transfers.
+Paris's average relative speed is very close to the "magical" value of one, at +which point private automobiles are no faster than multi-modal transport +including walking and cycling.
+Paris has done a great job of providing bicycle infrastructure in the inner +city regions, and notably of proactively enhancing or creating bicycle +infrastructure in regions of social disadvantage.
+Contrasts with Berlin nevertheless emphasise a couple of aspects which Paris +could focus on improving. The most notable of these is the index of +accessibility to natural spaces, and the relationship of this to other +variables. Paris simply has far less natural space than Berlin, and much poorer +general accessibility. Moreover, access to natural spaces is positively +associated with social advantage, so that it is relatively difficult for +socially disadvantaged Parisians to access natural spaces.
+All UA variables are measured such that lower or negative values are good, +whereas higher values are bad. (Exceptions are neutral variables such as +population density which are nevertheless straightforward to interpret.) For +example, unemployment or transport times are both better when values are lower. +Indices of bicycle infrastructure and access to natural spaces are also +transformed so that lower values indicate better or more of either. In these +cases, the transformations are simply one minus the respective proportions of +journeys out to fixed distance travelled along bicycle infrastructure, or +through or alongside natural spaces. Values of 0 then reflect 100% of all +journeys spent on bicycle infrastructure or in natural spaces, while values of +1 would represent complete absence of either bicycle infrastructure or natural +spaces.
+Variables are measured for every way, path, or street intersection within each +city. Values for the maps are aggregated within polygons defined by the +Socio-demographic variables described in the following sub-section, while +values within the statistics page are aggregated across entire cities. Unless +explicitly described otherwise, values of all variables are weighted by +population density. This means that, for example, distances to nearest schools +represent average distances that each person must travel to get to school. Full +descriptions of the calculation of all variables are given in the Software +and Algorithms chapter.
+The extent and structure of each city is defined by its "socio-demographic +variable," or "social variable" for short. These are taken from open-source +datasets provided by the cities as a series of geographic areas, defined as +polygonal shapes, and some corresponding measure of socio-demographic +disadvantage. The cities themselves decide the resolution and extent of these +polygonal data. These polygons then define the extent and shape of cities +analysed in Urban Analyst, and the individual polygons into which the map data +are aggregated.
+Values of these socio-demographic variables are the only aspect that differs +between different cities. One of the simplest versions is unemployment rate, +generally measured either as a percentage (0-100), or a proportion (0-1). The +UA platform selects the most representative measure of general social +disadvantage provided by each city, and defaults to rates of unemployment only +where no more comprehensive of integrative measures are openly provided by +cities.
+Urban Analyst provides highly detailed statistics on transport systems. Many of +these are derived from estimates of times required to travel fixed distances of +10km. This value is chosen to capture the general efficiency of public +transport systems. Shorter distances do not sufficiently capture the influence +of transport modes such as express or long-distance train services, while +longer distances unfairly penalise smaller cities in which most journeys are +only of shorter distances.
+Values at this distance of 10km are obtained by following these three steps, +taking the example statistic of travel times:
+Values shown in the maps are aggregated within each polygon of a chosen city, +while values shown in the statistics page are aggregated over entire cities.
+The remainder of this section describes the five travel variables:
+Urban Analyst enables comparisons of travel times between two primary modes of +transport:
+Private Automobile. Travel times with private automobile are used as a +benchmark for measures of travel time using other modes. UA generates +realistic estimates of private automobile travel times through scaling to +empirically observed data on actual vehicular travel times. (Calibration +procedures are implemented and documented in this GitHub +repository.) Importantly, UA +includes an additional, unique aspect of automobile travel times not +quantified in any other equivalent system, through an algorithm to accurately +estimate the likely time required to park a private vehicle, and then to walk +to a desired destination. These parking times are crucial, as direct travel +times to many inner-city destinations do not provide realistic estimates of +actual journey times to locations where it may be impossible to actually park +a private automobile.
+Multi-Modal Transport. UA's "multi-modal travel times" represent fastest +possible times taken for journeys from every single point in a city to travel +10km using any combination of transport modes excluding private automobile. +The primary modes considered are walking, bicycling, and all available modes +of public transport within each city. Where it is faster to cycle 10km than +to take public transport (such as from locations with very poor public +transport connections, or on the top of long hills where downhill cycling may +actually be faster), these times will represent the single mode of cycling +only, but multi-modal times will generally reflect fastest times formed by +combining multiple modes of transport.
+Travel times measured these two ways are then combined to generate the +following two primary travel time statistics:
+Absolute travel times as the multi-modal travel times; that is, using any +mode except private automobile.
+Relative travel times as the ratios of absolute travel times compared with +equivalent travel times with private automobile. Relative travel times of +less than one indicate that multi-modal transport is faster than equivalent +transport with private automobile, while values greater than one indicate +that private automobile transport is faster.
+In addition to travel times, UA also includes the following two additional +statistics quantifying other aspects of public transport systems. Both are +measured for every point of origin with a city, with final values again derived +by following the steps described above to obtain average values of each for all +trips of 10km distance. Numbers of transfers are thus the average number +required for all journeys of 10km, while intervals are the required waiting +times for the next equivalent journey out to that distance.
+Intervals to Next Service are measured in minutes. For each point of origin +in a city, this statistic measures the waiting time necessary before +departing to each destination within a city on the service after the one +corresponding to the fastest journey. This delayed service may not be fast as +the original, or it may even be faster in some cases, as the UA algorithms +also prioritise connections with the fewest possible transfers. It can happen +that subsequent services are actually faster, yet involve additional +transfers not required in the originally identified "fastest" service.
+Numbers of Transfers measure the number of transfers necessary for a +minimal-transfer journey out to a distance of 10km. These +minimal-transfer journeys are selected to allow for journeys slightly +slower than absolute fastest journeys (generally by up to 5 minutes) if they +involve fewer transfers.
+All three of the statistics described above - travel times, intervals, and +numbers of transfers - are measured such that lower values are more desirable. +Travel times are then directly multiplied by (a logarithmically-transformed +version of) intervals between services to generate a "compound travel +statistic". Low values of this statistic only arise in locations which have +fast travel times and short intervals between services. Low values may +accordingly always be interpreted as indicating overall good transport +services. In contrast, high values may arise through various combinations of +variables, from extremely high values of one single variable, to less extreme +combinations of the two variables. It is thus generally not possible to +directly discern reasons for high values of this compound travel statistic. +Urban Analyst nevertheless provides direct insight into all individual values, +as well as all pairwise combinations of values, permitting indirect insight.
+Population density values are taken directly from the European Union Global +Human Settlement Layer data, +aggregated into polygons for maps, or across entire cities for statistics.
+Distances to nearest schools are measured in kilometres, as shortest walking +distances from each point to the nearest school. These are network distances, +and not simple straight line distances. A single value is ascribed to each +point within a city, and all points aggregated after weighting by local +population densities.
+The bicycle infrastructure index is derived from a measure of the proportion of +all possible journeys from each point out to a fixed distance of five +kilometres that travel along dedicated bicycle infrastructure. To conform with +all other UA variables, the index is one minus this proportion, so that low +values reflect high proportions of bicycle infrastructure. Values of zero would +then reflect all journeys taken along dedicated bicycle paths, while values of +one would mean a complete absence of dedicated bicycle infrastructure.
+Travel is calculated using a bicycle-specific algorithm that only extends along +ways unsuitable for bicycle travel where no alternatives exist. The weighting +scheme used adds total distances for all portions of travel along designated +cycleways that are separated from vehicular traffic. Portions of trips +extending along other types of ways are added with "half weightings" so, for +example, one kilometre along these types is equivalent to two kilometres on +dedicated bicycle ways. These "half-weight" ways include residential or +"living" streets, unpaved tracks, and bicycle lanes directly alongside +automobile lanes. A third category of ways are weighted at one-quarter, +including footpaths and general pedestrian areas which permit bicycle travel. +The precise weighting scheme can be viewed in this source code +file.
+The weighted sums of all distances along these types of ways traversed out to +five kilometres from any given point are then divided by the sum of all +distances travelled regardless of way type to give a ratio between zero and +one. This bicycle infrastructure index is then one minus this value.
+Natural space accessibility is measured in a similar way to the bicycle +infrastructure variable, except it quantifies proportions of walking distances +out to maximal distances of two kilometres that traverse natural spaces. This +provides a more realistic measure of natural space than simple aggregations of +areas, because it measures the ability of people to directly walk from every +point in a city through or alongside nearby natural spaces.
+Moreover, aggregate metrics do not generally capture the ability of people to +actually access natural spaces. A park may, for example, have restricted or +even private access. This would count as a natural space in a simply aggregate +metric, yet not in UA because access restrictions are taken into account in the +routing algorithms.
+The algorithm also measures lengths of ways walked adjacent to water - +so-called "blue space", providing a comprehensive metric of the actual ability +to access natural spaces from every point in a city. A natural space index of +zero would represent an entire city of natural space, with no built structures +at all, while a value of one would represent a complete absence of natural +spaces.
+The parking index is the ratio of numbers of nearby parking spaces to total +volumes of nearby buildings. The parking statistic is calculated for each point +by adding all nearby parking spaces with a weighting scheme that decreases +exponentially with distance, so that nearby parking spaces count more than +parking spaces that are farther away. Building volumes are also aggregated +using an identical weighting scheme. The parking index at each point is then +the ratio of the sum of distance-weighted numbers of parking spaces to the sum +of distance-weighted total building volumes.
+All publicly accessible parking spaces are counted, including on-street +parking, open parking lots, and multi-level parking garages. Building volumes +are aggregated regardless of type or purpose.
+The Urban Analyst platform aims to be applicable to as much of the world as +possible. To achieve this, data sources are chosen which are ideally have +global coverage, and which are not specific to any one city. The global sources used are:
+The following additional data are then required for each city:
+The last of these data requirements is the most restrictive, as most cities of +the world do not have or provide public transport data in GTFS format. There +are nevertheless thousands of cities which do provide GTFS feeds, as can be +seen for example in the GTFS feed aggregation platform +transit.land.
+The primary software used to generate the results presented on Urban Analyst are:
+osmdata for accessing and processing
+data from Open Street Map.
dodgr for general network routing queries.
gtfsrouter for public transport routine queries.
m4ra for multi-modal routing queries
uaengine for additional
+algorithms combining aspects of these other routing algorithms for tasks
+specific to Urban Analyst.
All of this software was primarily developed by the same team responsible for +Urban Analyst itself.
+(... coming soon)
+ +The primary software used to generate the results presented on Urban Analyst are:
+osmdata for accessing and processing
+data from Open Street Map.
dodgr for general network routing queries.
gtfsrouter for public transport routine queries.
m4ra for multi-modal routing queries
uaengine for additional
+algorithms combining aspects of these other routing algorithms for tasks
+specific to Urban Analyst.
All of this software was primarily developed by the same team responsible for +Urban Analyst itself.
+(... coming soon)
+ +All UA variables are measured such that lower or negative values are good, +whereas higher values are bad. (Exceptions are neutral variables such as +population density which are nevertheless straightforward to interpret.) For +example, unemployment or transport times are both better when values are lower. +Indices of bicycle infrastructure and access to natural spaces are also +transformed so that lower values indicate better or more of either. In these +cases, the transformations are simply one minus the respective proportions of +journeys out to fixed distance travelled along bicycle infrastructure, or +through or alongside natural spaces. Values of 0 then reflect 100% of all +journeys spent on bicycle infrastructure or in natural spaces, while values of +1 would represent complete absence of either bicycle infrastructure or natural +spaces.
+Variables are measured for every way, path, or street intersection within each +city. Values for the maps are aggregated within polygons defined by the +Socio-demographic variables described in the following sub-section, while +values within the statistics page are aggregated across entire cities. Unless +explicitly described otherwise, values of all variables are weighted by +population density. This means that, for example, distances to nearest schools +represent average distances that each person must travel to get to school. Full +descriptions of the calculation of all variables are given in the Software +and Algorithms chapter.
+The extent and structure of each city is defined by its "socio-demographic +variable," or "social variable" for short. These are taken from open-source +datasets provided by the cities as a series of geographic areas, defined as +polygonal shapes, and some corresponding measure of socio-demographic +disadvantage. The cities themselves decide the resolution and extent of these +polygonal data. These polygons then define the extent and shape of cities +analysed in Urban Analyst, and the individual polygons into which the map data +are aggregated.
+Values of these socio-demographic variables are the only aspect that differs +between different cities. One of the simplest versions is unemployment rate, +generally measured either as a percentage (0-100), or a proportion (0-1). The +UA platform selects the most representative measure of general social +disadvantage provided by each city, and defaults to rates of unemployment only +where no more comprehensive of integrative measures are openly provided by +cities.
+Urban Analyst provides highly detailed statistics on transport systems. Many of +these are derived from estimates of times required to travel fixed distances of +10km. This value is chosen to capture the general efficiency of public +transport systems. Shorter distances do not sufficiently capture the influence +of transport modes such as express or long-distance train services, while +longer distances unfairly penalise smaller cities in which most journeys are +only of shorter distances.
+Values at this distance of 10km are obtained by following these three steps, +taking the example statistic of travel times:
+Values shown in the maps are aggregated within each polygon of a chosen city, +while values shown in the statistics page are aggregated over entire cities.
+The remainder of this section describes the five travel variables:
+Urban Analyst enables comparisons of travel times between two primary modes of +transport:
+Private Automobile. Travel times with private automobile are used as a +benchmark for measures of travel time using other modes. UA generates +realistic estimates of private automobile travel times through scaling to +empirically observed data on actual vehicular travel times. (Calibration +procedures are implemented and documented in this GitHub +repository.) Importantly, UA +includes an additional, unique aspect of automobile travel times not +quantified in any other equivalent system, through an algorithm to accurately +estimate the likely time required to park a private vehicle, and then to walk +to a desired destination. These parking times are crucial, as direct travel +times to many inner-city destinations do not provide realistic estimates of +actual journey times to locations where it may be impossible to actually park +a private automobile.
+Multi-Modal Transport. UA's "multi-modal travel times" represent fastest +possible times taken for journeys from every single point in a city to travel +10km using any combination of transport modes excluding private automobile. +The primary modes considered are walking, bicycling, and all available modes +of public transport within each city. Where it is faster to cycle 10km than +to take public transport (such as from locations with very poor public +transport connections, or on the top of long hills where downhill cycling may +actually be faster), these times will represent the single mode of cycling +only, but multi-modal times will generally reflect fastest times formed by +combining multiple modes of transport.
+Travel times measured these two ways are then combined to generate the +following two primary travel time statistics:
+Absolute travel times as the multi-modal travel times; that is, using any +mode except private automobile.
+Relative travel times as the ratios of absolute travel times compared with +equivalent travel times with private automobile. Relative travel times of +less than one indicate that multi-modal transport is faster than equivalent +transport with private automobile, while values greater than one indicate +that private automobile transport is faster.
+In addition to travel times, UA also includes the following two additional +statistics quantifying other aspects of public transport systems. Both are +measured for every point of origin with a city, with final values again derived +by following the steps described above to obtain average values of each for all +trips of 10km distance. Numbers of transfers are thus the average number +required for all journeys of 10km, while intervals are the required waiting +times for the next equivalent journey out to that distance.
+Intervals to Next Service are measured in minutes. For each point of origin +in a city, this statistic measures the waiting time necessary before +departing to each destination within a city on the service after the one +corresponding to the fastest journey. This delayed service may not be fast as +the original, or it may even be faster in some cases, as the UA algorithms +also prioritise connections with the fewest possible transfers. It can happen +that subsequent services are actually faster, yet involve additional +transfers not required in the originally identified "fastest" service.
+Numbers of Transfers measure the number of transfers necessary for a +minimal-transfer journey out to a distance of 10km. These +minimal-transfer journeys are selected to allow for journeys slightly +slower than absolute fastest journeys (generally by up to 5 minutes) if they +involve fewer transfers.
+All three of the statistics described above - travel times, intervals, and +numbers of transfers - are measured such that lower values are more desirable. +Travel times are then directly multiplied by (a logarithmically-transformed +version of) intervals between services to generate a "compound travel +statistic". Low values of this statistic only arise in locations which have +fast travel times and short intervals between services. Low values may +accordingly always be interpreted as indicating overall good transport +services. In contrast, high values may arise through various combinations of +variables, from extremely high values of one single variable, to less extreme +combinations of the two variables. It is thus generally not possible to +directly discern reasons for high values of this compound travel statistic. +Urban Analyst nevertheless provides direct insight into all individual values, +as well as all pairwise combinations of values, permitting indirect insight.
+Population density values are taken directly from the European Union Global +Human Settlement Layer data, +aggregated into polygons for maps, or across entire cities for statistics.
+Distances to nearest schools are measured in kilometres, as shortest walking +distances from each point to the nearest school. These are network distances, +and not simple straight line distances. A single value is ascribed to each +point within a city, and all points aggregated after weighting by local +population densities.
+The bicycle infrastructure index is derived from a measure of the proportion of +all possible journeys from each point out to a fixed distance of five +kilometres that travel along dedicated bicycle infrastructure. To conform with +all other UA variables, the index is one minus this proportion, so that low +values reflect high proportions of bicycle infrastructure. Values of zero would +then reflect all journeys taken along dedicated bicycle paths, while values of +one would mean a complete absence of dedicated bicycle infrastructure.
+Travel is calculated using a bicycle-specific algorithm that only extends along +ways unsuitable for bicycle travel where no alternatives exist. The weighting +scheme used adds total distances for all portions of travel along designated +cycleways that are separated from vehicular traffic. Portions of trips +extending along other types of ways are added with "half weightings" so, for +example, one kilometre along these types is equivalent to two kilometres on +dedicated bicycle ways. These "half-weight" ways include residential or +"living" streets, unpaved tracks, and bicycle lanes directly alongside +automobile lanes. A third category of ways are weighted at one-quarter, +including footpaths and general pedestrian areas which permit bicycle travel. +The precise weighting scheme can be viewed in this source code +file.
+The weighted sums of all distances along these types of ways traversed out to +five kilometres from any given point are then divided by the sum of all +distances travelled regardless of way type to give a ratio between zero and +one. This bicycle infrastructure index is then one minus this value.
+Natural space accessibility is measured in a similar way to the bicycle +infrastructure variable, except it quantifies proportions of walking distances +out to maximal distances of two kilometres that traverse natural spaces. This +provides a more realistic measure of natural space than simple aggregations of +areas, because it measures the ability of people to directly walk from every +point in a city through or alongside nearby natural spaces.
+Moreover, aggregate metrics do not generally capture the ability of people to +actually access natural spaces. A park may, for example, have restricted or +even private access. This would count as a natural space in a simply aggregate +metric, yet not in UA because access restrictions are taken into account in the +routing algorithms.
+The algorithm also measures lengths of ways walked adjacent to water - +so-called "blue space", providing a comprehensive metric of the actual ability +to access natural spaces from every point in a city. A natural space index of +zero would represent an entire city of natural space, with no built structures +at all, while a value of one would represent a complete absence of natural +spaces.
+The parking index is the ratio of numbers of nearby parking spaces to total +volumes of nearby buildings. The parking statistic is calculated for each point +by adding all nearby parking spaces with a weighting scheme that decreases +exponentially with distance, so that nearby parking spaces count more than +parking spaces that are farther away. Building volumes are also aggregated +using an identical weighting scheme. The parking index at each point is then +the ratio of the sum of distance-weighted numbers of parking spaces to the sum +of distance-weighted total building volumes.
+All publicly accessible parking spaces are counted, including on-street +parking, open parking lots, and multi-level parking garages. Building volumes +are aggregated regardless of type or purpose.
+ +