Sidewalk Committee Analysis Project
Janesville, located in south
central Wisconsin, is a progressive council-manager administered city of more
than 63,000 people. During my summer internship in the Community Development
Department at City Hall, I assisted the GIS Coordinator, Kirby Benz, with a
variety of projects powered by GIS. One such project involved analyzing areas
within the Janesville Municipality that were in the greatest need for sidewalk
construction. A large portion of the city as well as many surrounding
neighborhoods were not equipped with sidewalks, most did not even have plans for
future sidewalk construction. A community sidewalk committee had been formed by
city council after the 7 year project to build 63 miles of sidewalk had came to
a halt. The program was disbanded and restructured due to large community
skepticism on proposed construction areas. While some residents saw the benefits
of have having a more connected city, opponents to the plan did not want to pay
for or maintain the new sidewalks.
One aspect of this project consisted of analyzing seven site suitability variables which were determined by the 12 person community sidewalk committee and also approved by city council members. These variables were heavily debated among council and committee members however a consensus was finally reached. The sidewalk variables were defined as follows; proximity to schools (1/4 & 1/2 mile buffer), proximity to transit bus stops (1/4 & 1/2 mile buffer), proximity to public facilities such as churches, parks, clinics and retail centers (1/4 & 1/2 mile buffer), street class (major arterial, minor arterial, collector), existing sidewalk gaps, housing density (>4 units & <2 units), and pedestrian/automotive accidents. The GIS analysis was performed on each individual sidewalk segment for each city parcel. An index ranking was then applied to sidewalk parcel segments depending on their spatial location to each of the seven variables described above.
The community sidewalk committee was in charge of developing the ranking system. Higher scores were given to sidewalk areas closer to schools, public facilities and transit stops, higher accident risk areas, locations with existing sidewalk gaps, areas adjacent to high density housing and streets classified as high use. For example, sidewalk segments within a 1/4 mile of a school were given a ranking of 20 while areas within a 1/2 mile were given a ranking of 15. Based on the seven criteria, the highest possible ranking was 100. Sidewalk construction is being analyzed by zone with there being 9 different city zones; zones 1 and 2 are referenced in the three buffer analysis maps below. These visual tools directly assisted in the sidewalk committee's analysis and subsequent recommendation on sidewalk construction priority around the city. The following three maps provide a brief insight into how we developed the sidewalk ranking index maps.
One aspect of this project consisted of analyzing seven site suitability variables which were determined by the 12 person community sidewalk committee and also approved by city council members. These variables were heavily debated among council and committee members however a consensus was finally reached. The sidewalk variables were defined as follows; proximity to schools (1/4 & 1/2 mile buffer), proximity to transit bus stops (1/4 & 1/2 mile buffer), proximity to public facilities such as churches, parks, clinics and retail centers (1/4 & 1/2 mile buffer), street class (major arterial, minor arterial, collector), existing sidewalk gaps, housing density (>4 units & <2 units), and pedestrian/automotive accidents. The GIS analysis was performed on each individual sidewalk segment for each city parcel. An index ranking was then applied to sidewalk parcel segments depending on their spatial location to each of the seven variables described above.
The community sidewalk committee was in charge of developing the ranking system. Higher scores were given to sidewalk areas closer to schools, public facilities and transit stops, higher accident risk areas, locations with existing sidewalk gaps, areas adjacent to high density housing and streets classified as high use. For example, sidewalk segments within a 1/4 mile of a school were given a ranking of 20 while areas within a 1/2 mile were given a ranking of 15. Based on the seven criteria, the highest possible ranking was 100. Sidewalk construction is being analyzed by zone with there being 9 different city zones; zones 1 and 2 are referenced in the three buffer analysis maps below. These visual tools directly assisted in the sidewalk committee's analysis and subsequent recommendation on sidewalk construction priority around the city. The following three maps provide a brief insight into how we developed the sidewalk ranking index maps.
1) Sidewalk Committee: School and Population Density Analysis Map - Zones 1 and 2
Areas within a 1/4 mile school buffer and sidewalks adjacent to larger population densities were ranked higher
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3) Sidewalk Committee: Public Facilities Buffers Analysis Map - Zones 1 and 2
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Areas located within the
1/4 mile public facilities buffer were ranked higher than areas just located
within the 1/2 mile bufferFinal Product
After the total rank (out of
100) was calculated for each individual property segment based on the seven
criteria, the data was then classified using a natural breaks classification
method for five class intervals. The natural breaks method or (Jenks)
classification, groups features with similar values while trying to maximize the
differences between the means of the five different classes. The final sidewalk
committee analysis map is shown below. The committee is now tasked with the
difficult decisions of choosing which areas to start construction, what side of
the street (if not both) should be constructed, how the project should
be financed and what sort of construction time line should be established. In
early July, the Janesville City Council ultimately voted to install more than
two miles of sidewalk.
By the time we were finished
with this project, I was able to get a firm grasp on how policy, procedures and
public opinions heavily influenced the course of action within City Hall. This
analysis project is just one example of how GIS can be leveraged to address
important local community development decisions.
Water/Sewage Utility Network Analyst
Originally, this internship was
specifically for the position of Utility Network Analyst. However, I was
frequently pulled off this primary project to assist in other departmental
mapping endeavors (sidewalk map analysis, assessment maps). At first,
the inaccurate and incomplete layers within the large utility data sets made
this project seem very daunting. I quickly devised a systematic quality control
plan and soon realized how important an accurate, up to date utility network is
for a cities overall infrastructure plan.
I had permission to work in the
departments ArcSDE (Spatial Database Engine) geodatabase which allowed editing
rights to all of the utility data files and layers. I quickly gained a new skill
set in ArcGIS as I learned the in and outs of the GIS department and ArcSDE;
these skills included general data structure, versioned editing (reconcile &
post), advanced SQL scripts, definition query's, advanced editing tools and
cartographic techniques in ArcMap's layout view to just name a few. I also
gained valuable experience in photo interpretation.
In 2006, the City of Janesville
contracted the Waukesha based engineering firm Ruekert Mielke to build the
city's sewage, water and storm utility networks. However, budgetary constraints
and limited access to asbuilt field inspection books resulted in inconsistent
and incomplete data layers. To put it simply, the utility layers were lacking in
attribute and spatial correctness. My job as a GIS Analyst was to contribute
towards making the utility networks a more comprehensive GIS layer. This was
attained primarily through quality assurance assessments as I will further
describe below.
In the GIS, the city of
Janesville is divided into quadrants using a map grid system. By using this map
grid, I was able to systematically check for spatial and attribute
inconsistencies in each quadrant (K-13, P-5, S-20, etc). The combination of 80
scale blue prints and aerial photographs provided two basic resources in which I
could interpret and therefore represent the GIS data layers being examined.
Performing quality assurance on
the GIS utility network data sets required the interpretation and comparison of
80 scales (Left), and the 2011 aerial photo of Janesville Wisconsin (Right).
Asbuilt field inspection books were also referenced as needed.
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The process of utility QC consisted
of panning up and
down streets checking both sewer and water utility layers for spatial accuracy
as well as populating/updating inaccurate or missing feature attributes.
Inaccuracies would then be documented or fixed on the fly. Google Earth street
view provided a platform for double checking spatial discrepancies.
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| As I pan up and down streets, I am checking for spatial accuracy as well as populating discrepancies in missing attributes |
Spatially Inaccurate
Utility Feature Examples
The left image is showing a
common spatial inaccuracy within the sanitary sewage layer. Note the manhole
point feature is about 8 feet south of the actual manhole location on the aerial
photo. Fixing this problem requires a spatial adjustment as well as an attribute
update. The right image is documenting a common error within the water utility
later. It is imperative to correct spatially inaccurate hydrant locations as
well as update their accompanying attributes. Fixing these discrepancies
contributes towards building a more accurate utility network.
Building A
Geometric Network
As many GIS professionals know,
a geometric network is comprised of a set of simple and complex edge
features (water mains) and junction features (tee's, valves) that have a
particular position, connectivity and shape which all participate in a linear
system. Topology or "connectivity rules" must be defined within a network such
as which type of junction can connect to a each edge. Solvers (downstream trace,
upstream trace, isolation trace and path trace) are used to execute network
analysis. Solvers help to model real world problems such as isolating mains and
valves due to a break. Weights (diameter, length, material) can be applied to
features within the network to also better model flow and
connectivity.
Comparable Property Assessment Map
When property owners challenge
the assessment of their parcels, the Community Development Department utilizes
GIS to construct property comparability assessment maps. I created a variety of
these maps (shown below) which spatially illustrated the assessed subject
property being challenged (Black) along with three other properties in the area
with similar credentials (Red). The Board of Review uses these maps to help
facilitate their decision making on how certain parcels are
assessed.
Rock Energy Coop Grid: Janesville
The Water Utility Department
in Janesville requested a map that illustrated what areas are serviced by the
Rock Energy Cooperative. Below is a locational map showing Rock Energy
Cooperatives service grid within the Janesville municipality.
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| Multiple clips and a monochromatic color design were used to create the map above |
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