Copyright
by
Aaron James Nichols
2015
The Report Committee for Aaron James Nichols
Certifies that this is the approved version of the following report:
Measuring the Relationship between Environmental and Demographic
Characteristics and Checkouts at Bike Sharing Stations in Texas Cities
APPROVED BY
SUPERVISING COMMITTEE:
Supervisor:
Junfeng Jiao
Kristen Camareno
Measuring the Relationship between Environmental and Demographic
Characteristics and Checkouts at Bike Sharing Stations in Texas Cities
by
Aaron James Nichols, BA
Report
Presented to the Faculty of the Graduate School of
The University of Texas at Austin
in Partial Fulfillment
of the Requirements
for the Degree of
Master of Science in Community and Regional Planning
The University of Texas at Austin
May, 2015
Abstract
Measuring the Relationship between Environmental and Demographic
Characteristics and Checkouts at Bike Sharing Stations in Texas Cities
Aaron Nichols, MSCRP
The University of Texas at Austin, 2015
Supervisor: Junfeng Jiao
This study uses a geospatial and statistical analysis to quantify environmental and
demographic characteristics within quarter mile and half mile service areas of bike sharing
stations and then measure the relationship between these characteristics and checkouts at
bike sharing stations in four Texas cities. Ten multiple regression analyses were done to
measure the significance of groups of these characteristics when predicting checkouts. It
was found that, for the most part, individual environmental and demographic
characteristics are not strongly correlated with checkouts at bike sharing stations. When
using multiple characteristics in the multiple regression analyses there were large variations
in the R2 values between the different cities and geographic levels. While groups of
environmental and demographic characteristics can be used to predict checkouts with
varying success, depending on the city, there are other variables that should be accounted
for when attempting to predict checkouts at bike sharing stations.
iv
Table of Contents
List of Tables ........................................................................................................ vii
Introduction ..............................................................................................................1
Literature Review.....................................................................................................2
Methods....................................................................................................................6
Geospatial Analysis ........................................................................................6
Austin ..............................................................................................................8
Fort Worth.......................................................................................................8
Houston ...........................................................................................................8
San Antonio ....................................................................................................9
Correlation Analysis .......................................................................................9
Statistical Analysis ..........................................................................................9
Results ....................................................................................................................10
Correllation Analyses....................................................................................10
Ausitn ...................................................................................................10
Quarter Mile ................................................................................10
Half Mile .....................................................................................11
Fort Worth ............................................................................................11
Quarter Mile ................................................................................11
Half Mile .....................................................................................12
Houston ................................................................................................12
Quarter Mile ................................................................................12
Half Mile .....................................................................................13
San Antonio .........................................................................................13
Quarter Mile ................................................................................13
Half Mile .....................................................................................13
Regression Analyses .....................................................................................14
Austin ...................................................................................................14
Quarter Mile ................................................................................14
v
Half Mile .....................................................................................15
Fort Worth ............................................................................................15
Quarter Mile ................................................................................15
Half Mile .....................................................................................15
Houston ................................................................................................16
Quarter Mile ................................................................................16
Half Mile .....................................................................................16
San Antonio .........................................................................................16
Quarter Mile ................................................................................16
Half Mile .....................................................................................17
All Systems ..........................................................................................17
Quarter Mile ................................................................................17
Half Mile .....................................................................................18
Discussion ..............................................................................................................19
Correllation Analysis ....................................................................................19
Regression Analysis ......................................................................................20
Conclusions and Limitations..................................................................................24
Appendix ................................................................................................................26
References ..............................................................................................................48
vi
List of Tables
Table 1:
Environmental and Demographic Characteristics in Austin’s Bike
Sharing system (Quarter Mile Service Area) .............................. 26-28
Table 2:
Environmental and Demographic Characteristics in Austin’s Bike
Sharing System (Half Mile Service Area) .................................. 29-31
Table 3:
Environmental and Demographic Characteristics in Fort Worth’s Bike
Sharing system (Quarter Mile Service Area) .............................. 32-33
Table 4:
Environmental and Demographic Characteristics in Fort Worth’s Bike
Sharing System (Half Mile Service Area) .................................. 34-35
Table 5:
Environmental and Demographic Characteristics in Houston’s Bike
Sharing System (Quarter Mile Service Area) ............................. 36-37
Table 6:
Environmental and Demographic Characteristics in Houston’s Bike
Sharing System (Half Mile Service Area) .................................. 38-39
Table 7:
Environmental and Demographic Characteristics in San Antonio’s Bike
Sharing System (Quarter Mile Service Area) ............................. 40-42
Table 8:
Environmental and Demographic Characteristics in San Antonio’s Bike
Sharing System (Half Mile Service Area) .................................. 43-45
Table 9:
Environmental and Demographic Characteristics Correlations ........46
Table 10:
Summary of Regression Analyses Result .........................................47
vii
Introduction
As the number of bike sharing systems increase, nationally and internationally,
the ability to understand and predict the use of these systems becomes increasingly
important. While effective and well planned bike sharing systems may not solve all of the
mobility issues in a city, they can be a significant part of the solution and help make cities
more livable places. Developing precise and calculated methods of estimating bike
sharing use and demand is an integral part of future planning efforts.
Through a geospatial and statistical analysis, this study aims to use environmental
and demographic characteristics that are commonly associated with bikeability,
walkability, and active transport to measure their relationships with checkouts at bike
sharing stations in four Texas cities. This study also aims to measure the statistical
significance of groups of these environmental and demographic characteristics to see
whether or not it is possible to use them as a means of predicting demand and checkouts
at bike sharing stations.
The significance of this research is that by identifying a method for predicting
checkouts at bike sharing stations, cities and bike sharing systems can more effectively
plan future stations and optimize existing stations in a way that best suits the needs of an
individual city.
1
Literature Review
Bike sharing systems are becoming increasingly popular in the United States with
over 30 US cities operating advanced “3rd generation” bike sharing systems as of 2013
(PeopleForBikes, 2013). Because of this, there has been a growing interest in research
relating to bike sharing systems, what affects them, and how to plan them effectively. By
examining the existing literature on bike sharing systems, bicycling, and walkability, it
might be possible to get a grasp on what factors affect bike sharing systems and access to
them.
Research on bike sharing systems covers a variety of topics. Studies might focus
on who’s using these systems (Efthymiou et al., 2013), how to plan them (Krykewycz et
al., 2010; dell’Olio et al., 2011), and how to more effectively operate them (Liu et al.,
2012; Sayarshad et al., 2012; Vogel et al., 2011). The majority of the research and
literature on bike sharing systems is from the last five years, indicating that bike sharing
as a topic of academic discussion is relatively new and that the field has been receiving
growing interest. Studies focus specifically on individual systems, as well as the concept
of bike sharing as a whole.
Research focusing on factors that influence demand of bike sharing systems
addresses a few different topics. In their paper, “Factors Influencing Travel Behaviors in
Bikesharing,” by Kim, Shim, In, and Park, a regression analysis was done using five
different variables that may affect the use of bike sharing systems. It was found that
buildings associated with commercial land uses promoted demand more than buildings
associated with residential land uses. It was also found that parks encourage use of bike
sharing systems three to five times more than schools or subway stations (Kim et al.,
2012).
2
Another study that was done on Australian bike sharing systems in Melbourne
and Brisbane was based on surveys of members of bike sharing systems. It was found
that respondents 18-34 years old and the presence of docking stations within 250 meters
of a place of work were statistically significant predictors of membership. Other
significant indicators of membership included high income, reactions to mandatory
helmet legislation, and riding activity over the previous month (Fishman et al., 2014).
Factors relating to the individual users of the bike sharing systems as well as the
cities in which the bike sharing systems are located are both used in research that is
focused on demand of bike sharing systems. Age is cited in multiple studies as a factor
that influences whether or not someone uses bike sharing systems (Efthymiou et al.,
2013; Fishman et al., 2014). A study on survey results from people aged 18-35 years old
in Greece found that the respondents aged 26-35 were more reluctant to join bike sharing
systems than the younger respondents (Efthymiou et al., 2013).
A Chinese study that analyzed 69 bike sharing systems in China aimed to figure
out what affects bike sharing use. It was found that the ridership and turnover rate
increased as government expenditure, the number of bike sharing members, and the
number of docking stations increased (Zhao et al., 2014). This study stood out from
others because it focused on factors other than environmental factors or factors relating to
the individual users of the bike sharing systems in an attempt to show what may be
affecting use of the systems.
When considering bike sharing systems, it is important to examine factors that
relate to whether or not someone is going to ride a bike, shared or personal. A study that
focused on bike paths and bike lanes and their effect on bike commuter rates found that,
even when controlling for land use, climate, socioeconomic factors, gasoline prices,
3
public transport supply, and cycling safety, cities with more bike paths and bike lanes had
significantly higher bike commute rates (Buehler & Pucher, 2011).
Bicycling, like walking, is considered “active transport.” What determines
whether or not someone is going to choose active transport over some other form of
transportation, such as public transit or a private automobile, is often considered highly
dependent on environmental factors. This is doubly important for bike sharing since
people will typically access a checkout station on foot and then cycle before completing
the final leg of the journey on foot. Therefore, it is important to know which
environmental factors are related to walkability and are associated with physical activity.
A wide variety of research has been conducted on walkability and which
environmental factors are related to whether or not someone is going to walk (Azmi &
Ahmad, 2015; Gori et al., 2014; Grasser et al., 2012; Hanja et al., 2013; Maghelal &
Capp, 2011). These studies often use a GIS (geographic information systems) approach
(Azmi & Ahmad, 2015; Hanja et al., 2013; Maghelal & Capp, 2011). Variables such as
intersection density and population density are commonly cited as indices of walkability
(Azmi & Ahmad, 2015; Grasser et al., 2012). Other studies address whether or not
indices that are commonly associated with walkability are actually correlated with
walking trips. A study in Montréal measured whether or not commonly used walkability
indexers were actually correlated with walking trips and it was found that they were
highly correlated (Manaugh & El-Geneidy, 2011). However, one study conducted in
Italian cities suggested that single measures of connectivity were not great indicators of
walkability and that a combination of other measures would be a more accurate measure
of walkability.
A number of approaches have been taken for estimating demand of bike sharing
systems. One such approach was taken in Philadelphia to assist with the planning of a
4
bike sharing system there. A GIS analysis was done to analyze locations that would be
best suited for bike sharing stations. This was done using a raster analysis based on
criteria that a number of European cities had come up with for scoring bike sharing
demand. Job and population densities were considered important for this analysis
(Krykewycz et al., 2010). In other words, characteristics of other cities were taken and
applied to Philadelphia to try and estimate bike sharing demand.
Overall, the literature surrounding bike sharing systems, walkability, and active
transport suggest that environmental factors can significantly influence whether or not
someone is going ride a bike or walk instead of drive or use public transit. The implication
of this is that the environment surrounding bike sharing stations needs to be considered in
the planning process and could potentially contribute to the success of a station or of the
system as a whole.
5
Methods
Whether or not certain characteristics of the built environment as well as certain
demographic characteristics affect checkouts at bike sharing stations in Texas cities was
measured through a series of geospatial and statistical analyses. These analyses were done
for each of the four cities as well as for varying geographic levels within each city. This
was done by establishing geographic study areas, measuring the presence of various
characteristics within these study areas, and then running a correlation analysis to
determine whether or not these characteristics affect checkouts at bike sharing stations. A
multiple regression analysis was conducted to develop a model for predicting checkouts
within each of the cities based on environmental and demographic characteristics. An
additional regression analysis was done with all cities to develop a model that could be
used to predict checkouts in Texas cities.
GEOSPATIAL ANALYSIS
The initial geospatial analysis was done by using ArcMap to create service areas
around each of the bike sharing stations in all of the cities and then measuring each of the
characteristics within these service areas. The service areas were created by using the street
network. However, freeways were removed from the street network before service areas
were created since people are not able to use freeways to access a bike sharing station on
foot or by bike. Quarter mile and half mile service areas were generated for each of the
bike sharing stations in the four cities. These are the different geographic levels in which
the geospatial analysis took place.
The next step was to quantify the demographic and built environment
characteristics within each of the service areas. Similar demographic and environmental
characteristics were measured for each of the cities. However, there were some
6
differences due to data availability and quality. Each of the following characteristics was
measured within each of the station service areas unless data was unavailable for a
particular city.
1. Intersections
2. Bike Paths (Miles)
3. Bike Lanes (Miles)
4. Sidewalks (Miles)
5. Building Area (Square Feet)
6. Bus Routes
7. Bus Stops
8. Bus Trips (Within a 24 hour period on a weekday)
9. Rail Stops
10. Rail Trips (Within a 24 hour period of a weekday)
11. Other Bike Sharing Stations
12. Percentage of Residential Land Uses
13. Percentage of Commercial/Office land Uses
14. Percentage of Educational Land Uses
15. Total Population
16. Young Population (18-34)
17. Jobs
18. Parks (Yes or No)
Environmental data were collected from the cities of Austin, Fort Worth, Houston,
and San Antonio. Population data were collected from the 2010 Census at the block level.
Employment data were collected from LEHD (Longitudinal Employer-Household
Dynamics) at the block level for 2011. Population and Employment data were measured
for each block, divided by the area in each block, and then multiplied by area for each block
7
within the station areas. This was done to account for incomplete blocks within the service
areas. If half of a block was inside a service area, then half of the population on that block
would be counted within the service area.
AUSTIN
Austin was the only city with all of the before mentioned data available. The
environmental and demographic characteristics were measured and summarized in tables
1 and 2 in the Appendix for each of the 45 bike sharing stations at the quarter mile and half
mile service area.
FORT WORTH
In Fort Worth, intersections, bike paths, bike lanes, sidewalks, building area, bus
routes, bus stops, bus trips, rail stops, rail trips, other bike sharing stations, total population,
young population, jobs, and parks were measured and quantified for quarter mile and half
mile service areas for each of the 35 checkout stations. Sufficient land use data was not
available from the City of Fort Worth. Tables 3 and 4 in the appendix show the measured
environmental and demographic characteristics for Fort Worth’s bike sharing system at the
quarter mile and half mile service area level.
HOUSTON
In Houston, intersections, bike paths, bike lanes, bus stops, bus routes, rail stops,
rail trips, parks, other stations, total population, young population, and jobs were measured
and quantified for quarter mile and half mile service areas for each of the 28 checkout
stations. Building footprint, sidewalk, and land use data were unavailable for Houston.
Tables 5 and 6 in the appendix show the measured environmental and demographic
characteristics for Houston’s bike sharing system at the quarter mile and half mile service
area level.
8
SAN ANTONIO
In San Antonio, intersections, bike paths, bike lanes, sidewalks, bus routes, bus
stops, bus trips, other bike sharing stations, total population, young population, jobs, and
parks were measured and quantified for quarter mile and half mile service areas for each
of the 53 checkout stations. Land use and building footprint data were not available for San
Antonio. Rail related characteristics were not considered for San Antonio since San
Antonio does not have a rail system. Tables 7 and 8 in the appendix show the measured
environmental and demographic characteristics for San Antonio’s bike sharing system at
the quarter mile and half mile service area level.
CORRELATION ANALYSIS
Once the environmental and demographic characteristics had been quantified in all
four cities for the quarter mile and half mile service areas, a correlation analysis was done
to measure the correlation, if any, between each of the characteristics and checkouts in all
four cities. The results of the correlation analysis can be found in table 9 in the appendix.
STATISTICAL ANALYSIS
Ten separate multiple regression analyses were conducted to determine whether or
not multiple variables selected from measured characteristics could be used to predict
checkouts. A regression analysis was done at each geographic level in each city as well as
two additional regression analyses at each geographic level for all of the cities grouped
together. All available variables were used initially for each regression model, then outliers
were taken away if their removal resulted in a more significant model while not greatly
lowering the R2 value.
9
Results
A series of analyses were conducted for each city at varying geographic levels.
Each individual characteristic was measured against checkouts at bike sharing stations in
each of the four cities to see if there was a strong correlation between an individual
characteristic and checkouts. A multiple regression analysis was also conducted for each
city at varying geographic levels to see if a combination of environmental and demographic
characteristics could be used to predict checkouts at bike sharing stations in each city. A
separate multiple regression analysis was also conducted for all of the cities combined
together to see if a general model could be created to measure checkouts at bike sharing
stations.
CORRELATION ANALYSIS
The results of the correlation analyses are summed up bellow. The results can also
be seen in table 9 in the appendix, which shows the correlations between each characteristic
and checkouts in all four cities at both the quarter mile and half mile levels.
Austin
Intersections, sidewalks, building area, parks, bike paths, bike lanes, other stations,
bus routes, bus stops, bus trips, rail stations, rail trips, residential land uses, commercial
land uses, educational land uses, total population, young population, and jobs were each
compared to checkouts in Austin at both the quarter mile and half mile level.
Quarter Mile
When comparing the environmental and demographic characteristics within a
quarter mile service area of each bike sharing station in Austin, no particular characteristic
had a very strong correlation with checkouts. Bike paths was the characteristic with the
10
highest correlation with checkouts at 0.424 with the next highest being the presence of
parks with a correlation of 0.394. Building area, bike lanes, rail stations, rail trips,
commercial land uses, and the young population had essentially no correlation with
checkouts at bike sharing stations in Austin. Intersections had a relatively high correlation
compared to the others at -0.355. However, the correlation was negative, meaning that as
intersections decreased, checkouts increased.
Half Mile
The correlation analysis between environmental and demographic characteristics
within a half mile service area of each bike sharing station in Austin and checkouts revealed
that no particular characteristic was strongly correlated with checkouts. Once again, bike
paths had the highest correlation with checkouts, except there was a correlation of 0.57 at
the half mile level instead of a correlation of 0.424. The presence of parks became less
strongly correlated with checkouts in Austin when measured at the half mile level and the
number of intersections became more strongly correlated with checkouts. Most other
characteristics were not strongly correlated with checkouts.
Fort Worth
Intersections, sidewalks, building area, parks, bike paths, bike lanes, other stations,
bus routes, bus stops, bus trips, rail stations, rail trips, total population, young population
and jobs were each compared to checkouts in Fort Worth at both the quarter mile and half
mile level.
Quarter Mile
Individually, most environmental and demographic characteristics measured within
a quarter mile service area of bike sharing stations were not strongly correlated with
checkouts except for bike paths, which had a correlation of 0.776. The presence of parks
11
had the second most strongly correlated characteristic at 0.402. Most other characteristics
were weakly correlated while bike lanes, bus routes, bus trips, rail stations, rail trips, and
jobs had essentially no correlation with checkouts.
Half Mile
At the half mile level, most characteristics were weakly correlated with checkouts.
Bike paths still had the strongest correlation with checkouts, but the correlation decreased
from 0.776 at the quarter mile level to 0.581 at the half mile level. The correlation between
the presence of parks and checkouts became significantly weaker at the half mile level. The
correlation dropped from 0.402 at the quarter mile level to 0.127. The correlation between
sidewalks and checkouts increased at the half mile level to become the second most highly
correlated characteristic at -0.413. Most other characteristics became slightly more or
slightly less correlated with checkouts at the half mile level, but maintained a relatively
weak correlation with checkouts. Bike Lanes, bus routes, rail stations, and rail trips had
essentially no correlation with checkouts.
Houston
Intersections, parks, bike paths, bike lanes, other stations, bus routes, bus stops, rail
stations, rail trips, total population, young population, and jobs were each compared to
checkouts in Houston at both the quarter mile and half mile level.
Quarter Mile
Most environmental and demographic characteristics within a quarter mile service
area were not strongly correlated with checkouts in Houston. Intersections had the highest
correlation at -0.499, meaning that checkouts increased as the number of intersections
decreased. Total population had the second strongest correlation with checkouts at -0.381.
12
Bike lanes, other stations, bus routes, and jobs had essentially no correlation with checkotus
at bike sharing stations.
Half Mile
Environmental and demographic characteristics were generally weakly correlated
with checkouts at the half mile level. Intersections maintained the highest correlation with
checkouts and increased from -0.499 to -0.536. However, most characteristics remained
weakly correlated with checkouts. Bike paths, bike lanes, and jobs had essentially no
correlation with checkouts at the half mile level.
San Antonio
Intersections, sidewalks, parks, bike paths, bike lanes, other stations, bus routes,
bus stops, bus trips, total population, young population, and jobs were each compared to
checkouts in San Antonio at both the quarter mile and half mile level.
Quarter Mile
None of the environmental and demographic characteristics measured within a
quarter mile service area were strongly correlated with checkouts. All of the characteristics
except for one had a correlation between -0.3 and 0.3. Sidewalks had the strongest
correlation at -0.315, meaning that checkouts increased as the total length of sidewalks
decreased. Bike paths, bike lanes, and other stations all had essentially no correlation with
checkouts in San Antonio.
Half Mile
At the half mile level, all environmental and demographic characteristics were
weakly correlated with checkouts at bike sharing stations in San Antonio. Intersections
were more correlated with checkouts at the half mile level than at the quarter mile level
13
and with a correlation of -0.361 had the strongest correlation out of all of the characteristics
at the half mile level. Bike paths had essentially no correlation with checkouts while other
characteristics had very weak correlations with checkouts, both positive and negative.
REGRESSION ANALYSES
Multiple environmental and demographic characteristics were used in a multiple
regression to see if a combination of characteristics could be used to predict checkouts at
bike sharing stations in each of the four cities at the quarter mile and half mile levels. An
additional regression was done at each geographic level with all of the stations from all of
the cities combined into a single group of bike sharing stations in Texas cities to see if
environmental and demographic characteristics could be used to predict checkouts at a
general statewide level.
Austin
Quarter Mile
Out of the available characteristics for Austin, intersections, bus routes, other
stations, parks, and commercial land uses were left in the regression analysis. Removing
other characteristics from the analysis made the model more significant and had very
limited effects on the R2 value. The end model for measuring environmental and
demographic characteristics had a significance of 0.000 and an R2 value of 0.476, meaning
that the environmental and demographic characteristics measured within a quarter mile of
bike sharing stations could account for roughly 48 percent of the variability in checkouts
at bike share stations.
14
Half Mile
When a multiple regression analysis was conducted using environmental and
demographic characteristics that were measured within a half mile of bike sharing stations
a final R2 value of 0.56 was achieved with a significance of 0.001. This means that roughly
56 percent of the variation in checkouts could be attributed to the environmental and
demographic characteristics that were used in the regression with 99 percent accuracy.
Characteristics were removed from the regression so that a high significance could be
achieved without greatly altering the R2 value. Bike paths, bike lanes, sidewalks, bus
routes, parks, rail trips, bus trips, commercial land uses, total population, and jobs were
used in the final half mile regression model.
Fort Worth
Quarter Mile
At the quarter mile level, all of the available environmental and demographic
characteristics for Fort Worth were used in the regression model, except for rail stops, since
removing different variables would reduce the R2 value without greatly improving the
already high significance level. Using intersections, sidewalks, building area, parks, bike
paths, bike lanes, other stations, bus routes, bus stops, bus trips, rail stations, rail trips, total
population, young population and jobs, an R2 value of 0.83 was achieved with a
significance of 0.000, meaning that roughly 83 percent of the variation in checkouts could
be attributed to environmental and demographic characteristics that were measured within
a quarter mile of Fort Worth’s bike sharing stations.
Half Mile
At the half mile level, intersections, sidewalks, bike routes, bike lanes, bus trips,
bus routes, other stations, total population, and jobs were used in the final regression model.
15
This produced an R2 value of 0.62 and with a significance of 0.001, meaning that roughly
62 percent of the variation in checkouts could be attributed to the environmental and
demographic characteristics that were used with a 99.9 percent confidence level.
Houston
Quarter Mile
Out of the available environmental and demographic characteristics in Houston,
other stations, bus trips, bus stops, jobs, total population, and young population were used
for a multiple regression analysis. Other characteristics were taken away if their removal
made the model more statistically significant and did not greatly decrease the R2 value. The
final model resulted in an R2 value of 0.503 at a significance level of 0.014, meaning that
roughly 50 percent of the variation in checkouts in Houston could be attributed to the
characteristics that were used in the model with 98.6 percent accuracy.
Half Mile
At the half mile level, intersections, bike lanes, bus stops, jobs, total population,
young population, and rail stops were used in the final regression model. This produced an
R2 value of 0.521 with a significance level of 0.022, meaning that roughly 52 percent of
the variation in checkouts in Houston could be accounted for by the characteristics that
were used in the regression model with 97.8 percent accuracy.
San Antonio
Quarter Mile
Out of the available characteristics for San Antonio, sidewalks, bike lanes, bike
paths, parks, and other stations were used in the final regression model after non-essential
characteristics were removed from the analysis. This produced and R2 value of 0.217 with
16
a significance level of 0.036, meaning that roughly 22 percent of the variation in checkouts
could be attributed to the selected environmental and demographic characteristics that were
used in the regression analysis with 96.4 percent accuracy.
Half Mile
Sidewalks, bike paths, parks, total population, young population, and bus trips were
used for San Antonio’s half mile regression analysis. This produced an R2 value of 0.286
with a significance level of 0.013, meaning that roughly 29 percent of the variation in
checkouts could be accounted for by the selected characteristics that were used in the
analysis with 98.7 percent accuracy.
All Systems
Quarter Mile
Out of the available common characteristics that each city shared, intersections,
bike paths, bike lanes, bus routes, other stations, parks, bus trips, young population, and
jobs were used in a multiple regression analysis to try and find out whether or not
environmental and demographic characteristics of the built environment could be used to
predict checkouts at a bike sharing station in a Texas city. Characteristics were taken away
from the analysis if doing so could increase significance while not significantly lowering
the R2 value. This regression analysis resulted in an R2 value of 0.274 with a significance
level of 0.000, meaning that roughly 27 percent of the variation in checkouts in all four
bike sharing systems could be accounted for by the characteristics that were used in the
regression analysis with almost 100 percent accuracy.
17
Half Mile
Intersections, bike paths, bike lanes, bus routes, other stations, parks, bus stops, bus
trips, total population, and jobs were used in the multiple regression analysis that was done
for bike sharing stations in all four cities. These characteristics were all measured within a
half mile service area of the bike sharing stations. This regression analysis produced an R2
value of 0.33 with a significance level of 0.000, meaning that roughly 33 percent of the
variation in checkouts across all four cities could be attributed to the environmental and
demographic characteristics that were used in the analysis with almost 100 percent
accuracy.
18
Discussion
CORRELATION ANALYSIS
The correlation analyses that were conducted between the environmental and
demographic characteristics that were measured at varying geographic levels revealed
that, for the most part, a single characteristic is not a good predictor of checkouts at bike
sharing stations and most characteristics were not strongly correlated with checkouts.
Fort Worth is the main exception to this since there was roughly a 78 percent correlation
between checkouts and the length of bike paths that were measured within a quarter mile
service area of bike sharing stations.
There was also a lack of consistency between how different characteristics were
correlated with checkouts in each of the cities. A characteristic that might be relatively
highly correlated with checkouts in one city would have almost no correlation with
checkouts in another city. This can be seen when looking at bike paths. In both Austin
and Fort Worth, bike paths had a relatively high correlation with checkouts compared to
other characteristics that were measured at the quarter mile level, but the same
characteristic had a very weak correlation with checkouts in both Houston and San
Antonio when measured at the quarter mile level.
Whether or not a characteristic has a stronger or weaker correlation with
checkouts when the characteristic was measured within a quarter mile or half mile service
area is also inconsistent. Sometimes a characteristic might have a stronger correlation
when measured at the quarter mile level and a weaker correlation when measured at the
half mile level in one city, but the opposite would be true for the same characteristic in
another city. Bike paths within a quarter mile service area had a stronger correlation with
checkouts than bike paths within a half mile service area in Fort Worth, but bike paths
within a half mile service area had a stronger correlation with checkouts than bike paths
within a quarter mile service area in Austin. This happens again with parks. In Austin,
Fort Worth, and Houston the presence of parks have a stronger correlation with checkouts
when measured at the quarter mile level and a weaker correlation with checkouts when
measured at the half mile level. However, in San Antonio the presence of parks had a
19
stronger correlation with checkouts when measured at the half mile level rather than at
the quarter mile level.
One surprising result is that the number of intersections is somewhat correlated
with checkouts in all four cities, but correlation is negative. A higher intersection density
typically means smaller blocks, which his often associated with a more walkable
environment. However, when it comes to bike sharing, checkouts tended to increase
slightly when the number of intersections decreased. This was true for all four cities at
both the quarter mile and half mile levels. The length of sidewalks also had a negative
correlation in all cities at all geographic levels. Checkouts tended to increase slightly at
bike sharing stations as the total length of sidewalks within the service areas around bike
sharing stations decreased.
While many characteristics were only weakly correlated with checkouts, there was
usually some consistency between cities in whether or not the correlation was positive or
negative. However, there are a couple of situations where a characteristic might have a
positive correlation in one city and a negative one in another city. This can be seen when
looking at jobs and how it correlates with checkouts. Jobs are not strongly correlated with
checkouts in any city, but the correlation, although weak, is negative in Fort Worth,
Houston, and San Antonio. In Austin, the correlation is positive. This also happens with
bus routes, bus stops, and bus trips. The correlation is not strong in any city, but it is
negative in Fort Worth, Houston, and San Antonio while positive in Austin.
REGRESSION ANALYSIS
The regression analyses used the available environmental and demographic
characteristics to create a statistical model that could predict checkouts at bike sharing
stations. The characteristics used in these regressions and the results that were produced
varied widely from city to city and demonstrated that using environmental and
20
demographic characteristics as a tool for predicting checkouts at bike sharing stations is a
task easier said than done.
Out of the ten multiple regression analyses that were completed, the analysis that
was done for Fort Worth’s bike sharing system using characteristics that were measured
within a quarter mile service area of bike sharing stations stood out from the others. The
R2 value produced from this regression analysis was significantly higher than the R2
values that were produced in any of the other analyses. With an R2 value of 0.83 and a
significance level of 0.000, approximately 83 percent of the variation in bicycle
checkouts can be attributed to the environmental and demographic characteristics that
were used in the multiple regression analysis with almost 100 percent accuracy. This
stands out since none of the other regression models had an R2 value quite as high. The
contrast is very apparent when comparing Fort Worth’s quarter mile regression model
with San Antonio’s quarter mile regression model, which had an R2 value of only 0.217.
Environmental and demographic characteristics were measured both within a
quarter mile service area of bike sharing stations and within a half mile service area of
bike sharing stations. This was done because it was not clear exactly how much of an
effect a characteristic might have on checkouts. Does a park half a mile away from a bike
sharing station have an effect on checkouts in the same way that a park a quarter mile
away from a bike station does? Does the presence of sidewalks within a half mile of a
bike sharing station matter if people are usually only willing to walk a quarter mile? The
lack of clarity on this is why a regression analysis was done for each city using quarter
mile service areas and using half mile service areas. For all of the cities except Fort
Worth, the R2 value was greater for the half mile service area analysis. This could
indicate that, within these cities, environmental and demographic characteristics that
aren’t necessarily in the immediate vicinity of a bike sharing station could still be having
some sort of effect on checkouts. However, this would not be true in Fort Worth since the
quarter mile service area analysis produced a much higher R2 value. This could indicate
that checkouts in Fort Worth are much more dependent on the immediate surroundings
than in other cities.
21
When the multiple regression analyses were being conducted, characteristics that
were non-vital to the analysis and that could be removed without significantly decreasing
the R2 value while making the model more statistically significant were removed. This
left only the groups of characteristics that were contributing heavily to the R2 value.
Individually, some of these characteristics were not very significant, but when measured
with other things, they became a more important part of the analysis. One interesting
aspect about this is that there was a lack of consistency between which characteristics
were important in one city and which were important in another city. In Fort Worth, all of
the characteristics could be used, except the number of rail stops, to produce a high R2
value and a low significance level, while in Austin, only five characteristics seemed to be
necessary in the quarter mile service area analysis to produce a statistically significant
model that did not greatly lower the R2 value. Characteristics that affect checkouts do not
appear to be universal and are highly dependent on the city that the bike sharing system is
located in.
The lack of consistency between cities is also apparent in the analyses that were
done using an aggregate of all four of the bike sharing systems. In both the quarter mile
service area analysis and the half mile service area analysis, relatively low R2 values were
produced. This could indicate that cities and bike sharing systems do not interact with
each other in the same manner in different geographic locations. Figuring out how one
city works and applying what has been learned to another city might not necessarily be
very effective.
It is unclear why the regression analyses in different cities produced such varying
results. It could be that there are many other variables at play. Many of these variables
might not necessarily be easily quantified and measured. An example of this could be
cycling culture. If a city has a strong cycling culture it might not matter as much where a
bike sharing station is located. Whereas if a city does not have a strong cycling culture,
22
what ultimately leads to a person’s decision about whether or not to checkout a bike could
be much more dependent on the surrounding environment.
23
Conclusions and Limitations
While this study aimed to be as precise and consistent as possible, there were
some limitations that may have limited the effectiveness of the study. Most of the data
used came from each jurisdiction that the bike sharing systems reside in. These cities do
not necessarily have the same data and the data is not necessarily of consistent quality
from city to city. As the research found, environmental and demographic characteristics
do not necessarily have the same relationship with checkouts from city to city, meaning
that what could highly correlated with checkouts in one city might not really be all that
significant in another city. That being said, just because building areas were not very
important in one city, does not necessarily mean that they are not important in another
city. However, it is hard to measure this if that data is not available. When running the
regression analyses for the aggregate of all four cities, only characteristics that were
commonly shared could be used in the analysis. The end result was a relatively low R2
value. Had more characteristics been commonly shared, it is possible that a more
insightful statistical model could have been created.
The nature of bike sharing systems is another limitation for conducting research
on bike sharing systems. The flexibility of these systems allows for a station to be moved
to another location if it is not performing well. However, this means that a station cannot
be used in an analysis if it was not in the same location for the six month period that
checkouts were measured in.
All four of the bike sharing systems that were used in the analysis are relatively
small. Houston’s bike sharing system has less than 30 checkout stations while San
Antonio’s system has more than 50. Compared to much larger systems, such as the ones
in New York or Washington DC, this leaves a relatively small sample size for conducting
a statistical analysis. A much larger system with hundreds of stations might provide more
accurate results and deeper insight into the relationships between environmental and
demographic characteristics and checkouts at bike sharing stations.
24
This study examined whether or not environmental and demographic
characteristics were correlated with checkouts at bike sharing stations in four different
cities as well as whether or not these characteristics could be used to produce a statistical
model for predicting checkouts at bike sharing stations within specific cities and in general.
The results indicated that this varies greatly from city to city and could be very dependent
on a number of variables that are not necessarily visible or easy to quantify. While these
characteristics are related to checkouts to an extent, depending on the city, using the
findings to create general guidelines for bike sharing planning is not a straightforward and
simple task. Examining how environmental and demographic characteristics are related to
checkouts at bike sharing stations might be more effective if done on a case-by-case basis
for individual cities rather than for bike sharing as a whole.
25
Appendix
Bus Trips
Percent
Residential
Percent
Commercial/
Office
1.93
39.11
716
16.29
26.22
20
3645
2.09
0
8
950
0
3
214
0
0
14
2992
1
0
0
4
1
0
0
6
1
1
0
0
6
0
1
0
1
1
1
34
0
748,344
4
4.53
943,911
4.75
947,790
0
0
0
0
7
2nd & Congress
22
1.02
0.21
5.09
1,388,802
29
2
1
0
0
10
3rd & West
18
1.57
0
2.52
695,725
12
2
1
0
0
5
4th & Congress
25
0.08
0.21
6.08
1,681,629
37
2
1
0
0
5th & Bowie
22
0.73
0
3.93
1,079,001
8
2
1
0
5th & San Marcos
34
0.05
0.07
3
512,263
6
1
0
0
8th & Congress
25
0
0.53
6.17
1,731,949
39
2
1
ACC - Rio Grande & 12th
24
0.2
0
5.15
798,269
17
1
ACC - West & 12th
21
0.33
0
4.21
662,809
2
1
7
1.26
0.25
2.94
805,534
17
17
0
0.98
1.61
394,717
4
0.08
0.16
0.26
38,387
19
0
0.18
4.74
1,054,222
15
0
0.7
2.63
20
0
0.72
21
1.87
0
Barton Springs & Riverside
Barton Springs @ Kinney
Ave
Barton Springs Pool
Bullock Museum @
Congress & MLK
Capital Metro HQ - East 5th
at Broadway
Capitol Station / Congress
& 11th
City Hall / Lavaca & 2nd
1,816
1,388
13,245
0
761
425
9,419
0
1,412
702
1,559
51.88
0
780
438
15,329
17.76
43.86
0
1,585
809
6,855
12.89
13.18
0
490
133
1,294
0.05
50.4
0
222
104
51,832
928
8.96
44.22
8.29
547
283
4,696
388
10.23
41.51
8.87
356
173
2,255
11
2177
8.75
51.12
17.05
524
353
5,537
0
7
830
47.23
53.79
0
672
251
1,681
0
0
2
104
0
0
0
180
108
19
0
0
0
6
662
43.17
32.74
19.62
4,099
3,857
9,392
0
0
0
0
4
896
31.34
8.54
8.21
764
196
1,971
34
1
1
0
0
11
2458
0.86
45.11
0
142
56
40,737
33
1
1
0
0
13
2664
0
33.69
0
831
492
8,779
26
Bus Stops
1.42
Other
Stations
Jobs
20
Young
Population
1,133,193
Total
Population
6.55
Percent
Educational
0.14
Sidewalks
(Miles)
0
Bike Lanes
(Miles)
27
Bike Paths
(Miles)
17th & Guadalupe
Station
Name
Rail Trips
882
Rail Stations
46.19
Parks (y/n)
8.86
Bus Routes
2602
Building
Area (Sq.
Ft.)
Intersections
Table 1: Environmental and Demographic Characteristics in Austin’s Bike Sharing system (Quarter Mile Service Area)
Table 1 Continued
Convention Center / 4th St.
@ MetroRail
Convention Center/ 3rd &
Trinity
22
0.36
0.39
5.17
1,629,804
6
2
1
1
18
0.11
0.44
4.7
1,457,142
8
3
0
1
Davis at Rainey Street
17
0.35
0
1.79
433,195
10
0
1
0
East 11th St. & San Marcos
41
0
0.13
4.69
770,141
7
1
1
East 11th Street at Victory
Grill
38
0
0
5.54
781,660
2
1
East 6th & Pedernales St.
21
0
0.14
2.65
741,297
2
East 6th at Robert Martinez
18
0
0
2.73
809,012
4
Guadalupe & 21st
26
0
1.01
4.93
1,405,662
6
1.6
0
2.8
19
1.07
0.04
Long Center @ South 1st &
Riverside
Nueces @ 3rd
Palmer Auditorium
7
2
7
2
8
910
3.63
30.13
0
1,060
310
5,970
9
766
5.25
29.49
0
547
187
4,921
0
3
442
28.42
13.57
0
983
456
386
0
0
9
651
33.22
16.17
0
838
433
717
1
0
0
7
539
46.83
11.29
0.25
953
463
511
0
1
0
0
2
142
34.31
19.75
3.75
1,114
335
441
0
1
0
0
3
194
14.73
36.18
8.79
1,345
499
670
18
1
0
0
0
10
3410
14.15
17.16
43.75
3,369
3,069
2,505
650,113
16
1
1
0
0
9
1862
14.11
36.13
6.31
519
340
4,776
4.04
873,443
32
1
1
0
0
14
2885
5.37
31.71
0
1,188
654
16,370
8
0.24
0
1.58
705,018
4
0
1
0
0
7
496
51.49
18.16
0.58
757
436
3,853
Pfluger Bridge @ W 2nd
Street
20
1.81
0
1.14
240,743
8
0
1
0
0
3
228
12.93
4.16
0
299
215
445
Plaza Saltillo
25
0
0
3.24
755,419
4
0
1
1
7
2
9
674
23.89
14.36
0
764
203
1,583
Rainey St @ Cummings
8
0.51
0
0.97
258,728
5
0
1
0
0
0
0
29.22
8.43
0
938
444
307
Red River & 8th Street
31
0.53
0.38
5.37
1,153,246
16
1
1
0
0
5
755
1.19
39.71
0
984
318
14,860
Republic Square @
Guadalupe & 4th St.
32
0.04
0.04
6.09
1,381,748
36
2
1
0
0
23
4124
2.95
47.11
0
1,125
636
24,558
Riverside @ S. Lamar
10
1.18
0
1.27
206,930
8
1
1
0
0
2
418
0
20.78
0
362
184
1,631
San Jacinto & 8th Street
25
0.1
1.01
6.21
1,698,103
26
3
0
0
0
6
1215
1.61
48.6
0
735
190
48,164
South Congress &
Academy
18
0
0.26
1.09
787,693
3
1
1
0
0
4
320
39.52
21.54
55.75
1,139
759
2,064
South Congress & Elizabeth
24
0
0.01
2.67
750,226
3
1
0
0
0
3
552
50.64
12.59
31.85
891
460
1,687
South Congress & James
21
0
0.25
1.78
792,229
3
2
1
0
0
4
632
41.66
19.1
44.8
1,001
604
1,832
22
0
0
4.43
822,673
20
0
0
0
0
8
2182
2.27
53.23
0
307
87
13,005
19
0.13
1.13
3.64
850,487
17
0
1
0
0
13
2564
0.26
43
0
6
1
12,761
State Capitol @ 14th &
Colorado
State Capitol Visitors
Garage @ San Jacinto &
12th
27
Table 1 Continued
Toomey Rd @ South Lamar
13
0.34
0.38
1.65
374,007
6
1
1
0
0
5
574
4.38
52.09
1.43
450
218
2,198
7
866
3.27
39.18
0
1,049
299
17,310
Trinity & 6th Street
26
0.06
0.74
6.17
1,765,880
13
3
0
1
7
2
UT West Mall @
Guadalupe
23
0
1.06
3.95
1,420,860
17
1
0
0
0
13
4434
13.93
12.84
68.94
4,515
4,414
2,535
Waller & 6th St.
32
0
0.13
3.52
659,293
2
1
0
0
0
6
426
20.49
18.24
0
492
135
1,436
West & 6th St.
20
0.72
0
4.82
1,096,560
8
2
1
0
0
8
1004
14.9
49.4
0
1,558
751
16,740
8
0.06
0.62
0.85
11,552
1
1
1
0
0
2
104
0
0
0
23
13
121
Zilker Park at Barton
Springs & William Barton
Drive
28
Rail Trips
Bus Stops
Bus Trips
Percent
Residential
Percent
Commercial/
Office
Percent
Educational
31
4
0
0
0
28
8,197
15.49
31.4
10.36
4,742
3,974
24,914
2nd & Congress
67
4.79
0.74
15.69
4,407,278
38
9
1
1
72
41
5,806
3.19
37.27
0
2,436
1,229
71,433
3rd & West
74
4.18
0.04
12.36
2,736,282
33
6
1
0
0
29
4,445
7.69
30.78
0
2,581
1,335
38,172
4th & Congress
86
3.2
0.83
19.86
4,965,708
42
10
1
1
72
51
7,938
2.08
37.37
0
2,652
1,218
80,537
5th & Bowie
83
3.57
0.04
12.14
2,573,150
33
5
1
0
0
30
4,598
10.61
28.11
0
2,602
1,324
42,261
137
0.54
0.38
14.06
2,651,108
15
3
1
1
72
22
2,094
20.81
14.61
2.46
2,821
965
9,057
96
0.28
1.71
21.01
4,681,903
45
7
1
0
0
48
8,441
2.25
41.61
0
2,229
957
90,801
ACC - Rio Grande & 12th
104
0.82
0.21
18.13
2,900,004
35
3
1
0
0
22
4,940
14.82
33.94
2.17
1,934
986
16,867
ACC - West & 12th
100
0.94
0.14
17.2
2,832,003
35
2
1
0
0
16
2,334
17.26
34.31
2.19
2,037
1,003
14,921
Barton Springs & Riverside
25
4.85
0.49
6.67
2,400,271
23
3
1
0
0
27
3,875
15.64
25.03
16.43
2,174
1,415
14,929
Barton Springs @ Kinney
Ave
50
1.56
1.46
4.55
1,494,235
7
2
1
0
0
12
1,254
29.53
17.24
0.3
1,548
619
3,606
Barton Springs Pool
11
0.45
0.79
1.19
109,108
1
1
1
0
0
5
260
2.77
0.79
0
342
198
491
74
0
1.37
15.26
3,562,888
37
3
0
0
0
35
10,224
13.87
22.6
29.1
6,900
6,430
17,629
58
0.1
1.35
8.18
2,289,313
7
1
1
0
0
18
2,928
27.87
11.53
1.92
2,007
568
2,543
87
0.41
1.69
18.71
4,031,563
42
5
1
0
0
45
10,019
2.16
41.5
0.73
1,246
588
71,325
70
5.75
0.31
15.14
3,782,384
42
8
1
0
0
47
7,710
2.64
31.74
0
2,398
1,268
39,437
107
1.33
0.96
15.97
3,959,193
44
7
1
1
72
28
3,531
4.27
31.17
0
2,144
782
56,961
90
2.5
0.95
15.66
3,972,431
43
8
1
1
72
29
3,657
3.89
31.75
0
2,558
1,042
36,709
89
1.77
0.34
9.6
2,389,212
12
3
1
1
72
17
2,108
16.73
16.98
2.38
2,560
1,019
8,962
160
0.5
0.52
16.72
2,861,619
20
4
1
0
0
27
2,805
24.78
16.2
1.59
1,944
931
11,447
5th & San Marcos
8th & Congress
Bullock Museum @
Congress & MLK
Capital Metro HQ - East 5th
at Broadway
Capitol Station / Congress
& 11th
City Hall / Lavaca & 2nd
Convention Center / 4th St.
@ MetroRail
Convention Center/ 3rd &
Trinity
Davis at Rainey Street
East 11th St. & San Marcos
29
Jobs
Rail Stations
3,976,111
Young
Population
Bus Routes
20.05
Total
Population
Building
Area (Sq.
Ft.)
1.53
Other
Stations
Parks (y/n)
Sidewalks
(Miles)
0
Bike Paths
(Miles)
97
Intersections
17th & Guadalupe
Station
Name
Bike Lanes
(Miles)
Table 2: Environmental and Demographic Characteristics in Austin’s Bike Sharing System (Half Mile Service Area)
Table 2 Continued
East 11th Street at Victory
Grill
140
0.16
0.41
16.83
2,545,543
14
2
1
0
0
26
2,062
28.67
12.36
4.02
2,085
980
6,461
East 6th & Pedernales St.
79
0
0.74
10.69
2,591,116
7
2
1
0
0
20
2,962
30.64
16.41
3.84
3,155
1,018
2,026
East 6th at Robert Martinez
97
0
0.42
12.26
2,768,399
7
1
1
0
0
27
2,642
32.68
15.06
6.18
3,508
1,250
1,774
Guadalupe & 21st
112
0
1.62
17.35
5,001,844
39
3
0
0
0
28
8,775
28.6
16.11
28.08
15,277
14,615
11,820
Long Center @ South 1st &
Riverside
28
4.81
0.49
7.42
2,433,786
24
3
1
0
0
27
3,729
13.4
27.25
16.86
2,274
1,469
12,697
Nueces @ 3rd
75
4.75
0.04
14.73
3,085,496
38
7
1
0
0
39
6,671
5.54
31.42
0
3,124
1,652
32,999
Palmer Auditorium
40
1.51
0.77
5.82
1,978,381
22
3
1
0
0
27
3,562
26.34
27.21
12.39
1,966
1,090
9,373
Pfluger Bridge @ W 2nd
Street
60
5.27
0.01
6.96
1,589,015
14
5
1
0
0
16
1,948
6.62
20.07
0
2,328
1,218
24,535
Plaza Saltillo
102
0
0.53
14.61
2,437,360
7
2
1
1
72
33
2,908
24.3
15.57
5.53
2,771
1,031
4,002
Rainey St @ Cummings
48
1.14
0.04
3.52
792,200
10
1
1
0
0
3
352
21.81
8.07
3.74
1,499
681
2,065
Red River & 8th Street
129
1.19
1.31
17.71
4,340,942
32
7
1
1
72
31
5,093
4.88
29.55
0
2,078
897
61,608
Republic Square @
Guadalupe & 4th St.
90
3.76
0.47
19.37
4,297,523
41
8
1
0
0
52
9,280
4.82
37.34
0
3,118
1,623
75,133
Riverside @ S. Lamar
41
4.03
0.86
5.07
1,135,394
13
3
1
0
0
12
1,316
7.9
19.56
0.34
1,074
546
3,903
San Jacinto & 8th Street
111
0.7
1.92
20.85
4,942,374
44
8
1
1
72
44
7,990
1.33
37.85
0
1,923
796
71,466
South Congress &
Academy
57
0.37
0.5
4.76
2,426,100
12
2
1
0
0
11
2,080
40.82
20.72
24.31
2,445
1,347
7,625
South Congress & Elizabeth
75
0
0.8
6.48
2,498,685
5
2
1
0
0
13
1,385
40.48
10.47
20.96
2,299
1,099
3,070
South Congress & James
67
0
0.8
5.16
2,335,660
14
2
1
0
0
9
1,178
43.13
13.6
25.66
2,610
1,338
4,643
83
0.04
1.01
18.21
3,139,244
35
4
1
0
0
31
7,411
4.64
37.6
4.68
1,470
702
29,376
83
0.47
2.32
15.82
3,333,765
39
5
1
0
0
38
6,974
2.03
34.47
0
871
529
66,959
State Capitol @ 14th &
Colorado
State Capitol Visitors
Garage @ San Jacinto &
12th
Toomey Rd @ South Lamar
46
2.93
1.2
4.76
1,192,263
10
4
1
0
0
17
1,558
16.94
19.08
0.32
1,205
522
4,607
Trinity & 6th Street
116
1.45
1.54
20.39
4,931,900
45
8
1
1
72
40
5,152
1.89
35.94
0
1,979
705
78,986
UT West Mall @
Guadalupe
105
0
1.79
15.95
4,667,026
23
2
0
0
0
29
9,238
23.96
10.95
30.92
13,024
12,487
7,359
Waller & 6th St.
142
0.33
0.35
15.47
2,639,676
14
4
1
1
72
27
2,497
22.95
13.13
2.27
2,798
1,044
7,621
30
Table 2 Continued
West & 6th St.
94
2.67
0.04
17.44
3,753,147
42
7
1
0
0
45
7,970
11.21
37.14
1.65
3,367
1,727
50,449
Zilker Park at Barton
Springs & William Barton
Drive
18
1.02
1.36
1.78
224,963
1
1
1
0
0
8
416
6.75
3.52
0.17
549
246
1,523
31
Jobs
Young
Population
Total
Population
Parks (y/n)
Other
Stations
Building
Area (Sq.
Ft.)
Bus Routes
Rail Trips
Rail Stops
Bus Trips
Bus Stops
Bike Lanes
(Miles)
Paved Bike
Routes
(Miles)
Sidewalks
(Miles)
Intersections
Station
Name
Table 3: Environmental and Demographic Characteristics in Fort Worth’s Bike Sharing system (Quarter Mile Service Area)
2597 W. 7th St.
19
1.71
0
0.5
2
60
0
0
1
554,863
1
1
223
109
470
5th & Penn
22
3.49
0.13
0.31
2
130
0
0
2
513,668
0
0
190
134
2,325
Art Museums
13
2.6
0
0
8
210
0
0
3
360,661
0
0
43
15
404
Belknap & Taylor
33
4.03
0.01
0.31
6
572
0
0
13
1,011,826
1
1
1,648
811
6,752
Burnett Park
36
5.25
0
0.87
11
783
0
0
13
956,752
0
1
411
147
15,933
Central Library
41
6.42
0
0.41
12
938
0
0
15
1,070,963
2
1
1,791
756
5,664
City Hall
43
4.24
0
0.73
15
1,384
0
0
23
1,136,144
2
1
180
72
15,461
City Place
40
4.69
0
0.68
11
1,128
0
0
15
1,105,880
5
1
1,799
859
12,066
Convention Center
34
3.56
0
0.63
17
2,711
1
41
31
1,208,542
2
1
63
25
12,732
Fort Worth Bike Sharing
20
2
0
0.32
3
205
0
0
7
527,060
1
0
21
7
252
Gendy & Lansford
13
2.09
0
0
2
15
0
0
3
646,205
0
0
55
17
613
Higginbotham
37
1.97
0
0.35
6
106
0
0
5
486,096
0
1
25
10
1,236
ITC North
25
2.96
0
0.34
7
1,632
1
41
30
539,274
2
1
11
1
855
ITC South
21
2.87
0
0.53
3
1,057
1
41
29
365,079
1
1
1
0
319
Magnolia & Henderson
32
5.58
0
0.61
6
186
0
0
1
706,599
0
1
583
181
1,032
Magnolia & Hurley
26
4.39
0
0.4
7
240
0
0
2
788,084
0
0
408
131
2,212
Magnolia & Lipscomb
33
4.15
0
0.66
9
447
0
0
2
744,639
0
0
317
96
1,028
Museum Place
21
3.22
0
0.14
6
277
0
0
3
671,422
0
0
137
50
835
Omni Hotel Fort Worth
33
2.28
0
0.15
10
915
1
41
21
658,034
1
1
130
69
5,477
Park Place and Enderly
18
2.61
0
0
4
114
0
0
1
639,189
0
0
163
26
415
S. Main & Daggett
31
3.41
0
0.63
8
484
1
41
7
576,248
2
0
79
26
512
Sundance Square North
48
3.76
0
0.4
14
793
0
0
17
1,115,004
2
1
80
16
10,183
32
Table 3 Continued
Sundance Square South
47
4.62
0
0.89
16
1,572
0
0
17
1,427,381
3
1
515
164
13,635
T&P North
37
2.16
0
0.3
6
429
1
41
21
688,554
1
1
249
143
4,096
T&P South
29
2.91
0
0.66
9
518
1
41
8
490,571
1
0
43
24
771
TCU
Texas Health Harris
Methodist Hospital Fort
Worth
13
2.52
0
0
5
225
0
0
2
628,133
0
0
921
794
102
30
3.48
0
0.11
0
0
0
0
2
964,696
0
0
72
3
6,007
The T Offices
30
2.3
0
0.21
8
418
0
0
6
302,174
0
0
532
98
689
3
0.81
0.08
0
0
0
0
0
0
0
0
0
0
0
6
The Trailhead at Clearfork
Trinity Park
6
0.85
0.41
0
0
0
0
0
3
0
0
1
0
0
0
UNT Health Science Center
22
2.92
0
0
4
179
0
0
3
505,759
0
0
66
21
1,985
W. 7th St. & Stayton
17
1.81
0.17
0.46
0
0
0
0
1
389,570
1
1
130
64
406
W. Berry & University
18
3.04
0
0
6
233
0
0
3
675,640
0
0
1,040
922
4,352
Weatherford & Main
41
4.26
0
0.37
13
877
0
0
15
946,351
2
1
65
26
11,629
West 7th
19
2.56
0
0.3
6
262
0
0
4
907,823
0
0
176
118
1,095
33
Bus Routes
4
1,890,192
2
1
703
320
2,179
5th & Penn
88
12.03
1.31
1.08
9
496
0
0
9
1,634,396
0
1
1,476
643
4,798
Total
Population
Bus Stops
Jobs
0
Young
Population
0
Parks (y/n)
318
Other
Stations
9
Building
Area (Sq.
Ft.)
0.83
Rail Trips
0.63
Rail Stops
5.74
Bus Trips
61
Bike Lanes
(Miles)
Paved Bike
Routes
(Miles)
2597 W. 7th St.
Station
Name
Sidewalks
(Miles)
Intersections
Table 4: Environmental and Demographic Characteristics in Fort Worth’s Bike Sharing System (Half Mile Service Area)
69
11.07
0
0.17
22
824
0
0
4
3,500,675
4
1
371
185
4,146
Belknap & Taylor
116
14.08
0.85
0.82
24
1,899
0
0
17
2,856,263
5
1
2,508
1,132
20,186
Burnett Park
159
17.13
0
2.34
42
3,439
0
0
30
3,873,943
6
1
2,936
1,447
27,000
Central Library
153
17.51
0.12
2.34
38
2,737
0
0
26
4,070,386
8
1
2,897
1,426
37,076
City Hall
133
13.69
0
2.14
37
3,937
1
41
31
3,577,030
8
1
1,011
436
28,117
City Place
141
15.96
0.56
1.37
37
2,733
0
0
26
3,502,314
7
1
2,502
1,137
33,591
Convention Center
147
13.38
0
2.04
44
4,438
2
82
31
3,790,920
8
1
743
272
33,613
Fort Worth Bike Sharing
115
6.34
0
1.58
17
781
1
41
17
1,340,724
2
1
265
96
1,107
58
9.34
0
0
16
439
0
0
3
2,519,446
2
1
588
200
2,671
Higginbotham
135
11.4
0
1.66
31
1,849
1
41
24
2,838,581
5
1
1,072
523
25,129
ITC North
122
9.02
0
1.52
32
3,436
1
41
31
2,906,110
6
1
225
85
20,776
ITC South
111
7.16
0
1.22
26
3,046
1
41
31
2,044,325
4
1
131
62
13,966
Magnolia & Henderson
136
19.83
0
1.48
17
718
0
0
3
3,016,051
2
1
2,098
605
5,583
Magnolia & Hurley
110
16.15
0
0.91
17
688
0
0
2
2,817,534
2
1
1,288
343
6,151
Magnolia & Lipscomb
133
18.24
0
1.82
21
1,099
0
0
5
2,474,283
1
1
1,684
483
3,256
86
12.74
0
0.39
21
724
0
0
4
2,858,684
3
1
1,511
537
3,789
Art Museums
Gendy & Lansford
Museum Place
Omni Hotel Fort Worth
126
10
0
2.06
37
3,036
2
82
31
2,653,825
7
1
567
271
22,093
Park Place and Enderly
77
13.08
0
0.32
9
262
0
0
3
2,934,116
1
0
1,630
345
2,750
S. Main & Daggett
125
8.78
0
2.03
26
1,234
1
41
21
1,902,430
3
1
621
211
2,895
Sundance Square North
151
14.35
0.2
1.33
35
2,884
1
41
30
3,283,090
7
1
2,588
1,230
21,457
Sundance Square South
165
17.79
0.09
2.04
45
3,573
0
0
30
4,225,484
9
1
2,544
1,160
36,060
34
Table 4 Continued
T&P North
154
T&P South
TCU
Texas Health Harris
Methodist Hospital Fort
Worth
The T Offices
9.62
0
2.09
31
3,387
2
82
31
2,367,842
7
1
125
8.8
0
1.75
26
1,283
1
41
21
2,042,324
3
1
58
9.54
0
0
10
403
0
0
3
2,301,212
1
1
118
15.59
0
1.01
15
779
0
0
2
2,709,067
1
0
113
6.24
0.68
0.74
16
725
0
0
8
996,256
0
1
330
177
17,586
402
190
4,294
2,932
2,227
1,998
845
102
12,747
1,096
215
857
The Trailhead at Clearfork
17
2.5
0.44
0
0
0
0
0
0
3,421
0
0
3
1
10
Trinity Park
14
2.69
1.23
0
1
78
0
0
3
174,321
0
1
184
3
188
UNT Health Science Center
79
12.18
0
0.14
22
832
0
0
4
2,746,050
3
0
697
256
3,760
W. 7th St. & Stayton
53
5.82
1.13
0.71
6
180
0
0
4
1,652,317
1
1
623
217
1,670
W. Berry & University
Weatherford & Main
West 7th
65
10.06
0
0
16
564
0
0
3
2,440,252
0
1
3,726
3,161
4,989
136
12.81
0.5
1
28
2,168
0
0
27
3,067,663
6
1
2,354
1,039
17,835
74
9.56
0
0.55
22
808
0
0
4
2,633,314
3
1
864
368
2,849
35
Bus Routes
0
535
7
0
0
103
City Hall
31
0.23
0
54
1
1
3,948
27
0
0
23,829
9
3
Dallas & Smith
31
0
0
57
1
2
5,865
34
0
0
40,666
41
14
Elgin & Smith
52
0
0
14
0
0
2,135
18
0
0
7,924
861
467
Freed Library
33
0
0
15
0
0
783
18
0
0
1,484
650
283
4
0
0
0
1
0
0
0
0
0
5
11
1
La Branch & Lamar
29
0
0
44
1
2
2,265
20
0
0
10,333
366
122
Lamar & Crawford
22
0
0
23
1
1
1,147
11
0
0
4,768
366
122
Lamar & Milam
32
0
0
78
1
2
9,051
49
348
2
46,956
246
86
Leonel Castillo Comm Ctr / South St. & Henry
40
0
0
16
1
0
92
2
0
0
17
620
140
Main & Dallas
32
0
0
76
0
1
6,911
36
696
4
30,906
305
114
Market Square
33
0.38
0
62
1
1
5,956
29
348
2
5,780
610
267
McKinney & Caroline
32
0
0
64
0
1
5,470
36
174
1
29,110
771
293
Menil Collection / Alabama & Mandell
19
0
0
1
0
0
180
8
0
0
334
921
363
METRO Transit Center
44
0
0
61
0
0
5,493
35
522
3
2,498
518
144
MFAH/ Fannin & Binz
44
0
0
8
1
0
1,135
11
348
2
2,665
641
115
Milam & Webster
35
0
0
34
0
0
1,299
16
0
0
2,187
1,001
533
Project Row House / Holman & Live Oak
40
0
0
3
1
0
675
11
0
0
154
689
140
Rusk & St. Emanuel
33
0
0.06
5
0
0
284
4
0
0
443
551
356
6
0.37
0
1
1
0
0
0
0
0
2
73
39
Smith & Capitol
32
0.45
0
54
1
0
5,711
34
0
0
25,296
96
46
Spotts Park
20
0
0.05
6
1
0
92
4
0
0
2,081
296
146
Stude Park
16
0.03
0
2
1
0
110
3
0
0
24
218
64
Herman Park Lake Plaza
Sabine Bridge
36
903
Jobs
Rail Stops
Bus Stops
Young
Population
1
Total
Population
9
Rail Trips
0
Bus Trips
0
Other
Stations
19
Parks (y/n)
Bike Lanes
(Miles)
1919 Runnels
Station
Name
Bike Paths
(Miles)
Intersections
Table 5: Environmental and Demographic Characteristics in Houston’s Bike Sharing System (Quarter Mile Service Area)
297
Table 5 Continued
Taft & Fairview
57
0
0
0
0
0
0
0
0
0
699
1,238
464
Tellepsen YMCA
33
0
0
64
0
0
7,499
35
348
2
16,527
231
57
UHD/Main & Franklin
28
0.33
0
61
1
1
4,231
23
696
4
1,148
699
332
West Gray & Baldwin
52
0
0
31
0
0
767
13
0
0
1,000
1,540
1,121
Westheimer & Waugh
40
0
0.65
5
1
0
1,334
15
0
0
1,456
650
264
37
Bus Routes
2,314
23
0
0
527
2,835
979
City Hall
169
1.45
0.1
78
1
4
16,791
101
348
2
79,818
554
215
Dallas & Smith
158
0.53
0.16
78
1
5
19,587
107
696
4
86,564
638
187
Elgin & Smith
178
0
0
18
1
0
4,561
48
348
2
16,695
3,293
1,489
Freed Library
143
0
0
15
1
0
1,760
42
0
0
2,974
3,408
1,598
Jobs
Rail Stops
Bus Stops
Young
Population
0
Total
Population
1
Rail Trips
14
Bus Trips
0
Other
Stations
0
1919 Runnels
Parks (y/n)
Bike Lanes
(Miles)
88
Station
Name
Bike Paths
(Miles)
Intersections
Table 6: Environmental and Demographic Characteristics in Houston’s Bike Sharing System (Half Mile Service Area)
9
0
0
3
1
0
0
0
0
0
64
63
5
La Branch & Lamar
133
0
0.12
69
1
4
11,527
80
348
2
57,234
2,533
899
Lamar & Crawford
126
0
0.2
66
1
3
9,432
67
348
2
40,603
968
367
Lamar & Milam
141
0.23
0
79
1
7
24,283
129
696
4
104,244
1,280
490
Leonel Castillo Comm Ctr / South St. & Henry
116
0
0.13
33
1
0
1,728
22
0
0
372
1,966
446
Main & Dallas
129
0
0
83
1
8
24,137
130
1,218
7
99,473
1,843
697
Market Square
117
1.24
0
72
1
2
16,602
88
870
5
44,733
5,771
2,774
McKinney & Caroline
121
0
0
78
1
4
19,150
114
522
3
94,586
3,051
1,102
93
0
0
6
1
0
1,731
31
0
0
2,580
4,149
1,834
METRO Transit Center
166
0
0
75
0
3
17,154
103
1,218
7
28,310
1,962
786
MFAH/ Fannin & Binz
156
0
0
8
1
0
2,410
24
696
4
4,701
2,598
758
Milam & Webster
168
0
0
65
0
3
10,486
76
696
4
17,015
3,309
1,885
Project Row House / Holman & Live Oak
133
0.14
0
7
1
0
2,140
36
0
0
682
2,484
581
Rusk & St. Emanuel
140
0.23
0.45
30
0
0
2,957
29
0
0
2,997
1,444
632
15
0.86
0
20
1
0
0
0
0
0
11
197
104
153
1.56
0
76
1
4
18,510
111
522
3
69,290
2,288
986
Herman Park Lake Plaza
Menil Collection / Alabama & Mandell
Sabine Bridge
Smith & Capitol
Spotts Park
81
0.2
0.61
6
1
0
1,044
16
0
0
3,222
3,299
1,847
Stude Park
71
0.32
0
15
1
0
741
19
0
0
495
1,635
571
38
Table 6 Continued
Taft & Fairview
188
0
0
6
1
0
1,648
16
0
0
2,829
Tellepsen YMCA
150
0
0.02
81
1
5
19,986
109
870
5
52,120
1,225
451
UHD/Main & Franklin
119
1.13
0
71
1
1
13,677
75
870
5
34,753
11,730
6,388
West Gray & Baldwin
219
0
0.22
62
1
1
5,092
49
0
0
10,811
5,364
2,947
Westheimer & Waugh
157
0
1.13
6
1
0
2,808
34
0
0
3,853
3,753
1,301
39
4,290
1,764
115 E Crockett
58
5.87
1221 Broadway
17
1800 Broadway
42
Ace Mart
30
11,682
29
351
Jobs
Young
Population
Total
Population
Bus Stops
Bus Trips
Bus Routes
Other
Stations
Parks (y/n)
Bike Paths
(Miles)
Bike Lanes
(Miles)
Sidewalks
(Miles)
Intersections
Station
Name
Table 7: Environmental and Demographic Characteristics in San Antonio’s Bike Sharing System (Quarter Mile Service Area)
0
0
1
6
67
102
13,874
1.14
0.3
0
1
1
7
932
4
11
2
83
2.51
0.73
0
0
1
7
2,106
12
103
31
789
3.41
0.22
0
1
0
6
1,054
11
365
98
226
Acequia Park
0
0
0
0.01
1
1
0
0
0
2
0
2
Alamo Plaza
51
5.5
0.26
0
1
4
52
5,176
20
606
154
10,572
Bexar County Garage
28
3.91
0.63
0
1
1
46
6,326
17
808
395
1,788
Big Tex
7
0.76
0.19
0
1
1
5
453
6
114
43
217
Blue Star
10
1.04
0.28
0
1
1
5
576
7
145
51
273
Central Hub
30
4.1
0.34
0
1
2
28
5,641
32
89
23
8,064
Children's Museum
47
5.97
0.04
0
1
4
71
11,782
28
492
130
15,827
Concepcion Park
6
1.1
0.62
0
1
0
2
104
2
179
41
8
Confluence Park
17
1.87
0
0
0
0
2
104
2
358
106
29
Ellis Alley
15
2.19
0.5
0
0
0
10
2,085
11
93
23
285
Espada Dam
0
0
0
0.01
1
1
0
0
0
6
1
0
Flores @ Cesar Chavez
12
2.73
0.3
0
1
0
19
3,452
19
57
21
1,042
Geekdom
41
5.59
1.4
0
1
1
42
7,397
23
426
118
6,425
Hays Street Bridge
20
2.6
0.41
0
1
0
2
186
2
395
101
147
1
0.17
0.03
0
1
1
8
595
4
0
0
94
Hemisview
23
3.37
0
0
1
0
12
1,876
12
874
236
2,363
Hotel Havana
21
3.63
0.58
0
1
1
31
4,694
15
200
82
3,521
La Villita
32
3.14
0.34
0
1
3
57
6,140
27
4
1
2,613
Liberty Bar
21
4.78
0.43
0
1
0
3
735
7
515
125
181
Hemisfair Park
40
Table 7 Continued
Madison Square Park
49
4.93
0.72
0
1
1
17
4,004
18
252
112
6,401
Main Plaza
40
5.01
0.58
0
1
2
70
11,221
25
897
409
4,877
Market Square
37
3.77
0
0
1
1
22
7,848
17
868
487
1,877
Milam Park
33
4.15
0.04
0
1
1
32
6,809
23
672
304
4,225
Mission Concepcion
14
1.92
0.61
0
1
0
2
480
6
351
71
40
Mission Espada
0
0
0.01
0.32
1
0
0
0
0
12
2
0
Mission Park Pavilions
8
0.8
0.31
0.29
1
0
0
0
0
336
84
38
Mission Road
5
1.59
0.49
0
1
0
0
0
0
222
55
0
Mission San Jose
11
0.87
0
0
1
0
2
166
3
75
16
189
Mission San Juan
10
0.1
0
0
1
0
1
18
2
17
2
0
MPO
33
5.13
0.4
0
1
1
27
4,692
24
297
60
2,594
0
0
0
0.5
1
0
0
0
0
10
3
0
Pearl Brewery
32
2.65
0.67
0
0
1
7
938
6
10
2
1,005
Rand Building
49
5.54
0.79
0
1
2
72
12,135
33
448
116
9,919
Roosevelt Park
8
1.36
0.15
0
1
0
4
363
2
41
5
25
S.A. Central Library
34
4.79
0.9
0
1
2
16
3,958
17
241
98
7,157
S.A. Convention Ctr.
37
2.93
0.03
0
1
3
65
6,079
21
54
12
3,202
S.A. Museum of Art
11
1.97
0.35
0
1
1
7
696
7
4
0
325
SA Zoo
13
0.66
0.03
0.02
1
0
2
120
5
0
0
188
SAHA
15
2.99
0.5
0
1
0
10
2,015
17
285
51
1,082
9
1
0.03
0
0
0
10
834
4
1
0
362
The Luxury
23
2.71
0.55
0
1
2
11
980
6
20
4
222
The One Stop
19
2.12
0.15
0
0
0
10
2,219
15
248
67
4,359
Travis Park
44
5.92
0.1
0
1
3
66
11,193
27
589
157
13,252
USO
60
4.97
0.16
0
1
2
68
14,484
34
258
46
8,431
0
0
0
0.96
1
0
0
0
0
9
2
0
31
4.96
0.38
0
1
2
28
5,290
26
95
21
5,949
Padre Park
Sunset Station
VFW Blvd
VIA Super Stop
41
Table 7 Continued
Visitor's Center
49
5.03
0.08
0
1
3
57
6,895
20
525
136
10,258
Witte @ Parking Garage
16
0.71
0.22
0.07
1
0
6
1,422
7
93
19
322
YMCA Tripoint
35
2.21
0.63
0
1
0
3
345
6
286
90
355
42
Jobs
Young
Population
Total
Population
Bus Stops
Bus Trips
Bus Routes
Other
Stations
Parks (y/n)
Bike Paths
(Miles)
Bike Lanes
(Miles)
Sidewalks
(Miles)
Intersections
Station Name
Table 8: Environmental and Demographic Characteristics in San Antonio’s Bike Sharing System (Half Mile Service Area)
115 E Crockett
175
20.37
1.6
0
1
10
74
33,730
104
1,153
222
29,504
1221 Broadway
88
5.61
1.09
0
1
2
12
2,204
12
48
8
732
1800 Broadway
145
9.75
1.77
0.12
1
1
11
4,212
24
862
246
2,784
Ace Mart
121
16.42
1.41
0
1
2
12
5,408
53
2,437
615
1,566
Acequia Park
0
0.12
0
0.03
1
1
0
0
0
22
2
25
Alamo Plaza
181
19.97
1.5
0
1
9
74
29,021
94
968
226
29,618
Bexar County Garage
136
17.02
1.8
0
1
8
74
30,022
91
1,718
654
17,560
43
7.25
0.67
0
1
3
10
2,819
26
954
237
920
Big Tex
Blue Star
54
8.76
0.78
0
1
3
10
3,360
29
1,083
254
925
Central Hub
128
16.05
1.07
0
1
8
67
18,665
77
1,379
299
14,513
Children's Museum
189
22.34
2.08
0
1
11
74
37,591
114
1,407
285
31,898
46
6.65
1.83
0
1
0
7
1,492
19
1,054
243
67
Concepcion Park
Confluence Park
58
7.13
0.73
0
1
1
10
710
11
1,347
303
160
101
10.51
1.34
0
1
1
17
4,852
33
474
103
1,715
1
0.28
0
0.05
1
1
3
0
0
27
3
27
Flores @ Cesar Chavez
101
12.43
1.27
0
1
3
54
12,109
49
1,189
469
5,541
Geekdom
180
20.74
2.89
0
1
9
73
31,188
91
2,165
739
27,815
Hays Street Bridge
117
12.49
1.94
0
1
0
16
2,034
17
1,370
285
951
Hemisfair Park
48
4.92
0.39
0
1
5
58
8,269
35
21
5
4,225
Hemisview
94
11.3
0.47
0
1
3
20
5,287
39
1,570
420
8,160
Hotel Havana
150
18.4
2.66
0
1
8
46
17,683
70
1,077
320
22,971
La Villita
134
15.87
1.15
0
1
11
71
23,525
81
844
152
23,915
92
15.57
1.16
0
1
5
27
5,907
42
1,561
344
3,565
Ellis Alley
Espada Dam
Liberty Bar
43
Table 8 Continued
Madison Square Park
170
18.61
2.57
0
1
2
41
15,773
62
705
227
14,754
Main Plaza
161
19.81
2.4
0
1
10
74
35,856
95
2,272
832
24,724
Market Square
122
13.65
1.45
0
1
4
67
20,288
66
2,106
761
9,857
Milam Park
149
15.25
1.81
0
1
5
72
21,090
68
2,662
965
13,988
47
5.3
1.32
0
1
1
7
1,815
19
1,373
271
349
1
0
0.02
0.57
1
0
0
0
0
29
5
0
Mission Park Pavilions
38
3.94
0.61
0.31
1
0
4
1,406
20
2,159
535
221
Mission Road
43
7.22
0.99
0.11
1
0
0
0
0
1,523
354
10
Mission San Jose
36
2.48
0.18
0.08
1
0
4
948
13
947
211
529
Mission San Juan
24
0.16
0
0.01
1
0
1
18
2
93
19
3
116
18.26
1.23
0
1
6
45
12,035
63
1,790
437
10,324
0
0.04
0
1.03
1
0
0
0
0
26
5
0
Pearl Brewery
122
10.06
1.76
0.12
1
1
11
3,580
24
833
252
3,027
Rand Building
174
21.22
2.72
0
1
11
74
38,076
98
2,485
886
31,655
Roosevelt Park
50
7.27
0.44
0
1
0
8
2,102
14
924
163
434
S.A. Central Library
170
18.72
2.91
0
1
4
46
19,570
77
852
266
16,452
S.A. Convention Ctr.
141
14.04
0.61
0
1
10
70
22,350
73
978
183
23,551
S.A. Museum of Art
118
11.41
1.64
0
1
2
12
2,338
19
425
96
1,479
SA Zoo
36
1.45
0.31
0.29
1
0
4
492
13
78
32
534
SAHA
80
11.93
1.18
0
1
1
19
6,038
44
920
217
3,899
Sunset Station
46
4.71
0.94
0
0
1
16
3,625
22
190
38
958
142
10.14
1.82
0
1
2
12
3,040
20
253
43
1,823
Mission Concepcion
Mission Espada
MPO
Padre Park
The Luxury
The One Stop
94
10.17
0.85
0
1
3
16
5,748
45
1,107
283
15,475
Travis Park
184
22.42
2.81
0
1
11
74
34,712
105
1,597
371
32,461
USO
148
17.79
1.22
0
1
13
74
32,953
94
1,265
317
28,089
0
0.09
0
1.52
1
0
0
0
0
315
103
4
129
18.02
1.58
0
1
8
71
21,062
83
1,378
246
14,108
VFW Blvd
VIA Super Stop
44
Table 8 Continued
Visitor's Center
168
18.54
1.38
0
1
8
72
28,423
94
1,025
217
25,697
Witte @ Parking Garage
60
5.42
0.87
0.59
1
0
8
3,196
17
826
224
1,691
YMCA Tripoint
97
8.09
1.38
0.02
1
0
4
850
17
1,762
738
1,341
45
Intersections
Sidewalks
Building
Area
Parks
Bike Paths
Bike Lanes
Other
Stations
Bus Routes
Bus Stops
Bus Trips
Rail Stations
Rail Trips
Residential
Commercial/
Office
Educational
Total
Population
Young
Population
Jobs
Table 9: Environmental and Demographic Characteristics Correlations
0.25 Miles
-0.355
-0.148
-0.021
0.394
0.424
-0.095
0.225
0.276
0.203
0.126
-0.044
-0.044
-0.208
0.033
-0.101
-0.115
-0.083
0.107
0.5 Miles
-0.453
-0.268
-0.111
0.172
0.57
-0.148
0.274
0.088
0.069
0
-0.002
-0.002
-0.323
0.084
-0.122
-0.183
-0.146
0.225
0.25 Miles
-0.208
-0.354
-0.253
0.402
0.776
0.056
0.094
-0.056
-0.194
-0.072
-0.038
-0.038
NA
NA
NA
-0.135
-0.135
-0.027
0.5 Miles
-0.352
-0.413
-0.389
0.127
0.581
-0.077
-0.085
-0.068
-0.263
-0.107
-0.018
-0.018
-0.222
-0.191
-0.109
NA
NA
0.292
0.153
-0.015
0.076
-0.161
-0.248
-0.171
-0.143
-0.143
-0.381
-0.164
-0.09
0.205
0.085
0.098
-0.103
-0.154
-0.283
-0.195
-0.194
-0.194
-0.185
-0.104
-0.095
0.125
-0.007
-0.075
-0.023
-0.179
-0.286
-0.259
NA
NA
-0.253
-0.251
-0.213
0.155
0.029
-0.319
-0.163
-0.269
-0.265
-0.241
-0.162
-0.243
-0.232
Austin
Fort Worth
Houston
0.25 Miles
-0.499
0.5 Miles
-0.536
NA
NA
NA
San Antonio
0.25 Miles
-0.263
-0.315
0.5 Miles
-0.361
-0.319
NA
46
NA
NA
NA
Table 10: Summary of Regression Analyses Results
Characteristics Used
Characteristics Omitted
Intersections, Bus Routes, Other Stations, Parks, Commercial/Office
Bike Paths, Bike Lanes, Sidewalks, Bus Routes, Parks, Rail Trips, Bus Trips,
Commercial/Office, Total Population, Jobs
Sidewalks, Building Area, Bike Paths, Bike Lanes, Bus Stops, Bus
Trips, Rail Stations, Rail Trips, Residential, Educational, Total
Population, Young Population, Jobs
Intersections, Building Area, Other Stations, Rail Stops, Residential,
Educational, Young Population
R2
Significance
Austin
0.25
0.5
0.476
0.000
0.56
0.001
0.83
0.000
0.62
0.001
Intersections, Sidewalks, Parks, Bike Paths, Bike Lanes, Rail Stops,
Rail Trips
0.503
0.014
Parks, Bike Paths, Other Stations, Bus Routes, Bus Trips, Rail Trips
0.521
0.022
Sidewalks, Bike Lanes, Bike Paths, Parks, Other Stations
Intersections, Bus Routes, Bus Stops, Bus Trips, Total Population,
Young Population, Jobs
0.217
0.036
Sidewalks, Bike Paths, Parks, Total Population, Young Population, Bus Trips
Intersections, Bike Lanes, Other Stations, Bus Routes, Bus Stops, Jobs
0.286
0.013
Bus Stops, Total Population
0.274
0.000
0.33
0.000
Fort Worth
Intersections, Sidewalks, Bike Routes, Bike Lanes, Bus Stops, Bus Trips, Rail Trips,
Bus Routes, Building Area, Other Stations, Parks, Total Population, Young
0.25 Population, Jobs
Intersections, Sidewalks, Bike Routes, Bike Lanes, Bus Trips, Bus Routes, Other
0.5 Stations, Total Population, Jobs
Rail Stops
Building Area, Parks, Bus Stops, Rail Stations, Rail Trips, Young
Population
Houston
0.25
0.5
Other Stations, Bus Trips, Bus Stops, Jobs, Total Population, Young Population
Intersections, Bike Lanes, Bus Stops, Jobs, Total Population, Young Population, Rail
Stops
San Antonio
0.25
0.5
All Cities
0.25
0.5
Intersections, Bike Paths, Bike Lanes, Bus Routes, Other Stations, Parks, Bus Trips,
Young Population, Jobs
Intersections, Bike Paths, Bike Lanes, Bus Routes, Other Stations, Parks, Bus Stops,
Bus Trips, Total Population, Jobs
47
Young Population
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49