Data collection

This dissertation relies on a sample of 82 BRT stations that were selected in Bogota (10), Sao Paulo Metropolitan Area (12), Curitiba (16), Goiania (11), Ciudad de Guatemala (10), Quito (12) and Guayaquil (11) for data collection in 2012 through a study funded by the Lincoln Institute for Land Policy (Rodriguez & Vergel-Tovar, 2014). In 2014, additional data were collected. A total of 39 additional BRT stations were visited by the author and examined (21 in Bogota and 18 in Quito), resulting in a total of 120 stations, excluding one BRT Terminal from Ciudad de Guatemala which did not have population data available. The sample of 120 BRT stations in seven cities was selected in consultation with local transportation and city planners based on the following criteria: i) primary data collection could be conducted with several

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fieldwork visits; ii) BRT stations are representative of other stations in the BRT system, ii) BRT terminals represent different types of this large type stations in the system; iii) secondary built environment data was available. The location of the BRT stations was established by using Google Earth and then exported to a geographic information system (GIS). Two buffer areas were determined using GIS, a buffer of 250 meters was determined for single BRT stations and a buffer of 500 meters was determined for BRT terminals. The buffer areas defines the unit of analysis for the study, an area of 0.2 square kilometers around a sample of simple BRT stations and an area of 0.79 square kilometers around a sample of BRT terminals. A preliminary station area map was developed for each BRT station studied in which blocks and segments were identified. A block consists on a polygon with a group of land parcels or public spaces and determined by segments. A segment is the side of each block that goes from one intersection or corner to another as part of continuum of constructions, vacant land or public spaces.

The number of BRT stations, blocks and segments studied per city are shown in Table 13. The number of segments per station is higher in Guayaquil and Ciudad de Guatemala. The number of blocks per station is also higher in these two cities.

Table 13 Built environment data collected round BRT stations (terminals and single stations) in seven cities

City Built Environment Data

BRT stations

studied Segments Seg/Station Blocks Blocks/Station

Bogota 31 3,362 108.45 948 30.58

Sao Paulo ABD-Corridor 12 1,317 109.75 371 30.92

Curitiba 16 1,638 102.38 457 28.56 Goiânia 11 1,308 118.91 390 35.45 Ciudad de Guatemala 9 1,230 136.67 348 38.67 Quito 30 3,117 103.90 810 27.00 Guayaquil 11 1,585 144.09 459 41.73 Total 120 13,557 3,783

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Studies looking at built environment impacts of BRT systems have been conducted with the definition of different catchment areas. Studies examining the impacts of the BRT system in Bogota on property values have identified catchment areas based on walking distance to BRT stations. The study of property values within a 10 minute walking from the BRT system in Bogota defined a buffer area of 822 meters (Munoz-Raskin, 2010). There are other studies conducted in Bogota looking at the impacts on property values which defined buffer areas from the trunk BRT corridor from 500 meters to 1 kilometer (Rodriguez & Mojica, 2009; Rodriguez & Targa, 2004).

As it was mentioned previously, there are two different catchment areas in this study. For single BRT stations the buffer area is 250 meters due to BRT stations are usually located every 500 meters along BRT corridors. The buffer area for single BRT stations seeks to capture built environment attributes that are within the catchment area of the BRT station studied avoiding an overlap with the nearest single BRT station. For BRT terminals, the buffer area is 500 meters considering that these transportation hubs are considerable larger in size.

Previous research conducted with the aim to predict ridership at the station level and test the influence of different catchment areas around transit stops has found that different catchment areas have a little influence on the explanatory power of the model (Cervero & Guerra, 2013). This dissertation assumes that every BRT user boarding at a given station will have had to experience the area closest to the station. If it is impossible to cross a busy street to enter the station, then ridership is to be affected. Conversely, although still important, a busy street ½ km away from the station may not have such a notorious impact on ridership. In this way, focusing on the built environment of the area closest to the station is a desirable strategy. In data-rich contexts, examining a broader buffer is desirable. However, the larger catchment area for BRT

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terminals responds to previous research suggesting catchment areas varied by station type (Jiang et al., 2012).

All built environment data around the sample of BRT stations were collected by the author by walking all street segments within a buffer of each station in six cities. The data collection from nine BRT stations in Ciudad de Guatemala was conducted by personnel trained by the author as part of a previous research project. The author conducted fieldwork visits to all BRT stations studied and their respective blocks and segments identified previously. The fieldwork allowed the author and trained personnel to identify more segments and update the shape of blocks not capture by Google Earth or included in the preliminary GIS shape files. The built environment data was collected by adjusting the Pedestrian Environment Data Scan (PEDS) scan audit tool designed to collect built environment data to assess the level of pedestrian

friendliness, mainly in North America ("Planning and Physical Activity," 2007). The audit form was design for previous research in order to collect data about pedestrian environment, land use and land development intensity, public spaces and housing characteristics (Rodriguez & Vergel- Tovar, 2014). Data was collected at three levels.

At the BRT station level, BRT ridership data was provided by the Transportation Authorities in each city. In the case of Sao Paulo, ridership data were only available for BRT terminals. These terminals are main transportation hubs along the ABD Corridor providing access to passengers to the BRT corridor as well as to conventional buses. With the support of the Lee Schipper Memorial Scholarship and EMBARQ, ridership data were collected during three working days in October of 2014 at seven BRT stations from the ABD Corridor in Sao Paulo Metropolitan Area. In the case of Goiania, ridership data were available from the State Government of Goiás from a recent demand study along the BRT corridor (Eixo Anhanguera)

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("Estudos de demanda e oferta VLT Anhanguera," 2012). Census data was provided by local governments and governmental agencies and then it was calculated within BRT stations buffer areas by capturing the overlap with blocks identified for this research. Distance to the CBD was determined by calculations using GIS the distance of each BRT station to the closest activity node within each city.

At the block level, data collected were: a) facilities (presence of big-box developments for private vehicle or pedestrian access, schools, hospitals, temples, libraries, market squares, fair-exhibition, sports, recreational, others); b) public spaces (presence of green areas, parks, squares, pocket squares, boulevards, pedestrian bridges, bike-paths, street vendors, others).

At the segment level, data collected were: type of street (3 lanes, 2 lanes or pedestrian); land use (institutional, industrial, exclusively commercial, commercial and other land use, single residential, multifamily residential, industrial/commercial, commercial/residential, vacant/not developed, open green area); building heights (1 floor, 2-3 floors, 4-5 floors, more than 5 floors, none); urban density, assessment of built-up density along the segment (low, medium, high); consolidation level, assessment of presence of urban infrastructure (low, medium, high); construction condition, assessment relative to years of construction and condition of

surroundings buildings (low, medium, high); affordable housing (presence of social interest housing or slums); BRT corridor, in case the segment is facing the BRT corridor; parking (on- street, off-street).