Example

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.

Individual Variables.

The following table summarises the values of the individual variables for each city (each measured on its own distinct scale).

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.

Transport

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.

Other Variables

• "School Distance": Paris has notably shorter distances from each point in the city to the nearest school than does Berlin.
• "Bicycle Index": Paris has very notably better bicycle infrastructure than Berlin. This index is simply one minus the average portion of all bicycle journeys out to 5km from each point which may be taken on dedicated bicycle infrastructure. Around one quarter of all bicycle journeys in Paris may be taken along dedicated bicycle ways, compared to less than one fifth in Berlin. Moreover, comparing the maps for this variable reveal that the bicycle infrastructure in Berlin generally improves with distance away from the city centre, whereas Paris has the best bicycle infrastructure concentrated towards the centre of the city.
• "Nature Index": Access to natural spaces in the two cities is effectively the opposite of the bicycle index. Paris provides relatively little access to natural spaces for anybody not close to one of the two huge parks in the city, whereas Berlin provides an abundance of generally smaller natural spaces dispersed throughout the city. Note that natural space access includes walks alongside water bodies. Both cities include dominant rivers, yet Berlin also provides greater pedestrian access to the banks of its rivers and canals. Comparison of this layer on the maps reveals the comparably greater access in Berlin to walking paths alongside canals and rivers, whereas most of the Seine in Paris is effectively inaccessible to pedestrians.
• "Parking": Finally, both Paris and Berlin offer relatively little opportunity to park private automobiles compared with the other UTA cities, with Berlin notably less than Paris.

Relationships between variables

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:

Transport Variables

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.

Non-Transport Variables

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.

Conclusions

Lessons for Berlin

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.

Lessons for Paris

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.