1. An assessment of bike networks and their connectivity using Python
GTECH 731 Final Project - Eric Svenson
Cyclable cities
An assessment of bike networks and their connectivity using Python

2. NYC: A “bike Friendly” City
The Bloomberg and De Blasio administrations added over miles of bike lanes between 2006 and 2015
Have recently made it on many lists for top “bike friendly” cities because of these additions
“Sustainable Streets” – NYC’s 2009 transportation initiative - uses bike lane additions as the main determining factor for increased ridership
Ridership doubled between 2007 and 2011

3. Lanes make a network
The number of lanes in a network is not the best determinant of ridership
Connectivity is a more important factor in the amount of use of a bike network (Schoner & Levinson 2014)
Can people get to where they need to go safely?

4. Objectives
Write a Python script that prepares bike network shapefiles in a standardized way so they can be read into networkx
Use networkx to generate network information and connectivity statistics for the bike network
Compare connectivity for different cities; cities with high connectivity serve as models for cities with lower connectivity
Evaluate the script’s ability to achieve objectives

5. WHY Networkx?
Network datasets in geodatabases could provide similar information, but...
There is limited ability to quickly automate the creation of networks in a geodatabase
No standardized way to create and maintain bike network datasets between cities
Shapefile format is most common for bike lane data
Networkx is fast, and has many algorithms and functions specific to networks

6. Data New York City bike routes shapefile (2015)
Department of Transportation Data Feeds
Polyline shape data – many small line segments, not useable as such in networkx
Standardize: edges meeting only at intersections and ends
Each segment contains street name, among other attributes
Various other city government websites for other bike network shapefiles (San Francisco, Pasadena, Atlantic City)

7. Prepping Shapefiles: Arcpy
Goals:
Dissolve street polylines so that there are vertices only at intersections and ends/beginnings
Make script easily useable for any user (no paths hard-coded in script)
Do it quickly!

8. ArCPY

9. ArCPY (Cont.)

10. Arcpy result
Edge shapefile where vertices are only at intersections and dangling ends (points shown for visualization only)

11. GDAl: The “ogr” under The Bridge
Geospatial Data Abstraction Library: a raster/vector translator
OGR Spatial Library – simple vector features
Key function: “read_shp”, which reads shapefiles into a networkx digraph
Installation not a simple process
Install GDAL core for specific Python version/build
Configure system path: for GDAL and your python.exe
Install python bindings
Work around other GDAL versions
Make sure your python IDE also has other modules needed!

12. Help with gdal Installing GDAL for Windows:
gdal-and-ogr-for-python-on-windows/

13. OGR at work

14. Networkx: take it and run
Once in Networkx, must make the resulting directional graph an undirected graph (no to/from data)
Can make use of any functions/algorithms that aren’t specifically used for digraphs
Can use results of these in your own functions

15. NetworkX

16. New york

17. Pasadena, california

18. Atlantic City, New jersey

19. evaluation
Gamma index produced more consistent and expected results, and showed fairly low connectivity.
Run time fairly low: 30 seconds – 1 minute depending on shapefile size
Alpha index (coefficient representing number of possible circuits in largest subgraph) shows very low number of circuits for all cities.
Something lost (or gained) in conversion from digraph to graph?
May need tweaking if no “street” field

20. Future research
With more time: compare to cities with best-rated bike networks, test indices for accuracy
Use more descriptive algorithms with Networkx to get a better picture (e.g. clustering of nodes)
Determine a way to test how resulting indices would differ from random distribution
How does layout of cities affect results?
Historical comparison of bike lanes, change in connectivity
NYC from 2006 to 2015 and beyond

21. sources
Schoner, J., & Levinson, D. (2014). The missing link: bicycle infrastructure networks and ridership in 74 US cities. Transportation, 41(6), doi: /s
Nyc.gov,. (2015). NYC DOT - Bicyclists - Network and Statistics. Retrieved 1 May , from
Nyc.gov,. (2015). NYC DOT - Data Feeds. Retrieved 13 May 2015, from