INTRODUCTION
This project highlights stretches of the bike trails in Eau
Claire County and Chippewa County of Wisconsin which are potential spots for bike sharing
stations. The purpose of this lab was to answer a simple spatial question using
the skills learned this semester in ArcGIS. Eau Claire will soon have a bike
sharing program, and locations in which the bikes are parked will heavily
influence their use. Selection was made using two criteria: stretches of the bike
trails which lie outside areas at risk of floods and within a reasonable distance of a
park. Flooded bike stations would be quite problematic due to the cost of the
bikes themselves and the electronic station used to pay for the bike use.
Additionally, placement of bikes in the parks would alleviate potential complications
for the city. Since parks are city owned, it would be easier to add these
stations within the property line than to negotiate with businesses or
landowners for the use of their property.
DATA SOURCES
The county borders were extracted from the ESRI downloaded data from 2013. The water and bike trail data was
extracted from the Chippewa Valley Bike Map map package. I am concerned
the trail and water information I used is incomplete. There were many layers
with ambiguous names and missing descriptions, so I had to decipher which
contained the data I wished to use. Data was also labeled differently for
Chippewa and Eau Claire County, so combining the data that was similar required
inference. Additionally, I am not sure if the bike trail layer contains areas
of the road which have bike lanes. Theoretically, many types of roads or
sidewalks could be considered bike paths, but details about what was contained
in the “Bike Trails” layer was not included.
METHODS
To implement the restrictions, I extracted the layers I was interested
in from the map package and placed them into my project geodatabase, along with the
selection of “Eau Claire” and “Chippewa” counties from the ESRI data. From the
CV Bike Map, layers depicting similar features were separated by county, so I
first needed to combine these into one coherent layer. To begin, I combined the polygon layers “ChipCo_Water”
and “ECCo_hydply” using the Union tool to create one layer of both sets of
bodies of water. I used the Merge tool to combine the layers “ChipCo_hydln” and
“ChipEC_Sreams_DNR” to create a combined layer of the streams and centerlines.
Next, I used the Buffer tool to create a two layers depicting the area within 50
feet of these water features. I used the Union tool to combine these
buffers and dissolved the resulting polygons to create the layer depicting areas at
risk of flooding. Since 50 feet is relatively small, it is not included in the
map as it is not visible at this scale. Next, I combined the Eau Claire and Chippewa parks into one layer
using the Union tool. Upon closer inspection, many paths ran very near parks, but often did not overlap them. Since it would be convenient for the city to use park land for the stations, and being near a bike trail is sufficient for users, I created a 200 foot buffer of the parks to allow the nearby trails to be considered in the selection. Next, I used the Erase tool to eliminate areas of the
bike trail that overlapped the flood risk area. I intersected this layer with
the parks buffer to create a layer of the areas that bike sharing stations
could be placed. This area is hot pink on the map.
I have included the data flow model, which maps this process.
RESULTS
This analysis greatly narrowed down the bike path areas that
can host bike sharing stations. I think intersections of bike paths in the selected area would be ideal for placement so bikers could travel a loop.
EVALUATION
If this project were repeated, it may be valuable to
research the typical flooding range of the rivers in the area. Perhaps a 100
foot buffer or use of water table information would be more appropriate.
Additionally, it may be economically beneficial to only place stations near
businesses, particularly establishments with food. To make sure bike sharing
stations were easily accessible, I was going to eliminate areas that were
within a mile or two of a major road. However only one location was marginally
outside of that area, and major roads are not necessarily a good indicator of accessibility
or population density. Instead, population density would be a good factor to consider in
future analysis.
