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spatial patterns

How does accessibility re-frame our projects?

Tatiana Peralta Quiros's picture
The increasing availability of standardized transport data and computing power is allowing us to understand the spatial and network impacts of different transportation projects or policies. In January, we officially introduced the OpenTripPlannerAnalyst (OTPA) Accessibility Tool. This open-source web-based tool allows us to combine the spatial distribution of the city (for example, jobs or schools), the transportation network and an individual’s travel behavior to calculate the ease with which an individual can access opportunities.

Using the OTPA Accessibility tool, we are unlocking the potential of these data sets and analysis techniques for modeling block-level accessibility. This tool allows anyone to model the interplay of transportation and land use in a city, and the ability to design transportation services that more accurately address citizens’ needs – for instance, tailored services connecting the poor or the bottom 40 percent to strategic places of interest.

In just a couple of months, we have begun to explore the different uses of the tool, and how it can be utilized in an operational context to inform our projects.
 
Employment Accessibility Changes in Lima,
Metro Line 2. TTL: Georges Darido

Comparing transportation scenarios
The most obvious use of the tool is to compare the accessibility impacts of different transportation networks. The tool allows users to upload different transportation scenarios, and compare how the access to jobs changes in the different parts of the city. In Lima, Peru, we were able to compare the employment accessibility changes that were produced by adding a new metro line. It also helped us understand the network and connectivity impacts of the projects, rather than relying on only travel times.

Understanding spatial form
However, the tool’s uses are not limited to comparing transport scenarios. Combining the tool with earth observation data to identify the location of slums and social housing, we are to explore the spatial form of a city and the accessibility opportunities that are provided to a city’s most vulnerable population.  We did so in Buenos Aires, Argentina, were we combined LandScan data and outputs from the tool to understand the employment accessibility options available to the city’s poorest population groups.

Bright Lights, Big Cities

Mark Roberts's picture

Delhi night-time lights data imageIf you visit the National Museum of Natural History in Washington, D.C., one of the exhibits you’ll come across is a map of the Earth, which shows lights detected by satellites at night. With even a cursory look, it’s clear the lights pick out spatial patterns of urban and economic development.  Look at the USA, and you see the coasts are brightly lit, whereas the country’s interior is much less so.  Look at the Korea peninsula and you see that whilst South Korea is almost ablaze with light, the North is noteworthy for its almost complete absence of light.

The potential ability of night-time lights imagery to detect spatial patterns of urban and economic development has been known in the remote sensing community since the late 1970s.  However, it has only recently been brought to the attention of economists following a paper by Vernon Henderson, Adam Storeygard and David Weil entitled “Measuring Economic Growth from Outer Space.”  This paper alerted economists to the strong correlation between a country’s rate of GDP growth and the growth in intensity of its night-time lights, and the fact that the lights represent (for economists) a relatively untapped dataset with global coverage and a time-series dating back more than twenty years.