CYCL e TROPOLIS Analyzing america’s no.1 bike city cort eidem crp 551 : july 2011 cort eidem crp 551 : july 2011

Bike Commuter Cities City% Bike Minneapolis4.4% Portland4.2% Sacramento3.7% San Francisco3.7% Tucson3.3% Fresno2.9% Boston2.6% Seattle2.6% Washington, D.C2.5% Albuquerque2.4% U.S. Deptartment of Transportation 2008

setting the scene Bicycling Magazine recently named Minneapolis the #1 Bicycling community in the U.S.

So What’s the Problem? Many cities in America (including Minneapolis) are still designed primarily for vehicle traffic This leaves many people without alternative transportation options

Goals To find new sources of data To analyze transportation data in order to locate areas of special concern To create an output map that solves a transportation problem Where can Minneapolis expand bike trails in order to provide an immediate benefit?

Why does it matter? The recent recent economic recession coupled with a shift towards “Green living” has been a catalyst for bicycle infrastructure in the U.S. Minneapolis is constantly upgrading their bike trail network - making bicycling within the city safer and more accessible. As petrol prices continue to rise, more communities will depend on alternative transportation infrastructure - such as bike paths.

Me + GIS = ? I sought out to perform a basic analysis that could be used to site a future bicycle trail in currently neglected neighborhoods Motivations : I ride a bike... everywhere I wanted to learn where to locate data for Minnesota, & figure out how to apply the data in a useful manner

The forgotten corner Analysis of Census Data revealed a marked drop off in bicycle commuters in an area North of Downtown Minneapolis. Further investigation showed that this decline occurred at the county boundary line. Perhaps Anoka County does not have as much to invest into bicycle infrastructure as Hennepin County. Regardless, this area caught my attention, and I believe that bike paths would be welcome here due to the high percentage of bicycle commuters in surrounding neighborhoods.

Methodology Download & Process Tabular Data in Spreadsheet (This was more difficult than it should have been due to the fact that I’m poor, and use OpenOffice rather than Excel.) (Also, I’m fairly computer illiterate) Geodatabase Manage Census Data & Transportation Data Geoprocessing Merge, Clip, Union, Buffer, Rinse, Wash, Repeat Select By Location & Attribute Creating New Shapefiles

Collecting Data MetroGIS DataFinder DNR Data Deli /tgrshp2010.html US Census Bureau I found two new locations for GIS data (MetroGIS & the MN DNR Data Deli) I also used an old friend (US Census), but downloaded new TIGER 2010 data

Tabular Data Processed Census Data using OpenOffice

Geoprocessing I became very efficient at Geoprocessing through lots of Unions, Merging, and Clipping of layers I wanted to remove extraneous data in order to focus attention on this area.

Joins, queries, and fun Joined Census data to DNR shapefiles Set Definition Queries to Select appropriate data for presentation

My final analysis area shows the high numbers of bikers near downtown, and highlights the Columbia Heights neighborhood as an outlier. I also wanted to look at data regarding race & income. This analysis showed Columbia Heights households have a bit more income, but there are still no opportunities for cycling once petrol prices become too high FINAL POSTER

Thank You, Come Again If I could do things over, I would have used 3D Analyst to create a section profile of the elevation along new bike routes. I was able to create a 3D topography of the Twin Cities as per Tutorial 10. Highly valuable tutorial for visual presentations. (This left me curious if the software can merge with Google SketchUp or Google maps for future projects?) Additional data on street trees, or building footprints would have enhanced the project

Final Thoughts I succeeded in locating new sources for data & teaching myself how to apply that data. However, I had to adjust my initial proposal concept because I couldn’t make enough sense out of available land cover data to make a cohesive final project - something that probably would have been more achievable if this course was face to face, rather than online. C’est la vie.