/n HOPKINS TRANSIT STATION ANALYSIS
GEOG 502
INTRODUCTION
For this project, we use Hopkins transit station analysis data. For this transit station analysis, the study area
is Hopkins. Hopkins is a suburban city in Hennepin County, Minnesota, United States, located west of
Minneapolis (Figure 1). The city is four square miles in size and is surrounded by the larger, west suburban
communities of Minnetonka, Saint Louis Park, and Edina. Hopkins is about 98% developed with little
remaining vacant land.
The objective of the project is to demonstrate our understanding in GIS and our ability to solve real world
problems using our geospatial data management and analysis skills. Expected outputs are outlined for
guiding the analysis processes. These guidelines are by no means limit your analysis. Please use your
creativity to get any outputs from your analysis.
Winnipeg
BRUNSWI
Quebec City
Montreal
MAINE
Ottawa VERMONT
MINNESO
Toronto
NEW
HAMPSHIRE
MASSACHUSETTS
Minneapolis wisCONSIN
NEW YORK
RI
MICHIGAN
CT
New York
°
Chicago
PENNSYLVAN
IOWA
ASKA
OHIO
ILLINOIS
INDIANA
Indianapolis
Washington
tes
Kansas City
KANSAS
MISSOURI
VIRGINIA
KENTUCKY
Nashville
TENNESSEE
NORTH
CAROLINA
Charlotte
innetonka
SOUTH
CAROLINA
OKLAHOMA
Atlanta
ARKANSAS
MISSISSIPPI ALABAMA
GEORGIA
Dallas
TEXAS
Austin
San Antonio
Jacksonville
Orlando
LOUISIANA
°
Houston
New Orleans
St Louis Park
100
Hopkins
169
FLORIDA
494
Miami
The
Bahamas
62
Edina
LEON
Figure 1: Study area location map.
The following instructions are produced to give you some of the scenarios how the expected
outputs are produced. You can use your own creativity to produce the expected outputs. Feel free
to change the scenarios in producing the expected outputs.
PART I: PRODUCE STANDARD LAYOUT MAP
1. Create a workspace for this project, and copy all the data into a directory.
2. Create a base map using Arc Pro or ArcMap.
3. Load the proposed station location (station), a 0.7 mile radius circle (station_buffer1),
and parcels (parcels_shady_oaks), into a data view.
4. Create and print a properly titled map, north arrow, scale bar, grid, your name and simple
description in place of a formal legend. Save the map composition (..aprx or .mxd file)
for later work (see course project_video_1).
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PART II – PROJECTIONS, HILLSHADE, EDITING
ArcToolbox
1. Add the Focus_Zone shapefile, dem_shadyoaks1, NE, NW, SE, and SW to the project
created in Part 1. Examine the properties for each data set, particularly the map projection
of each data set (right click the data name in the table of
contents, then properties-source). Some data layers are not in our
preferred projection, UTM, NAD83 zone 15 meters. Project all
offending data layers into our preferred UTM. Make sure the
data frame coordinate system is also set to UTM, NAD83 zone
15 meters. From now on, any reference to data layers is to these
new, projected layers.
2. Create a hillshade layer from the DEM, and save it as an output
grid (ArcToolbox-Spatial Analyst-Surface-Hillshade, specify an
output data file – see the graphic on right/below, see course
-
project_video_2).
3. Now create a “buildings” data layers. Use ArcCatalog to create
an empty polygon data layer, in the UTM zone coordinate
system you are using. Display the NE, NW, SE, and SW, 1-foot
resolution images. Using the images as a guide, digitize all the
buildings within the "Focus_Zone” area. If you wish, you may
choose a different area, of approximately the same size, and
digitize all the buildings within the area (see course
project_video_3). Save the data to a new data layer, named
something like "buildings."
4. TurnIn: print a properly composed map that shows the hillshade,
parcels, buildings, and station location. Place the hillshade on the
bottom, the station and buildings on top, and make the parcels
transparent (no color or hollow) to make the hillshading visible.
Symbolize the parcel lines, buildings, and station so they stand
out.
Server Tools
Spatial Analyst Tools
Conditional
Density
Distance
Extraction
Generalization
Groundwater
Hydrology
Interpolation
Local
Map Algebra
Math
Multivariate
Neighborhood
Overlay
Raster Creation
Reclass
Solar Radiation
Surface
Aspect
Contour
Contour List
Contour with Barriers
Curvature
Cut Fill
Hillshade
Observer Points
PART III: CONTRIBUTING WATERSHED AND IMPERVIOUS SURFACE
Rainfall runoff, accumulation, and management is important. We want to identify areas with high
flow accumulation so that we may avoid flooding within the focus area, and not contribute to
problems below the focus area. Here, we wish to identify areas with high impervious surface that
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drain into the focus area, and areas with high impervious surface that are within the focus area. We
will prioritize mitigation for those areas with higher than 35% impervious surface. You may ask
why 35% threshold for impervious areas? This is experts' recommendation for managing
impervious areas and accept as it is recommended by the experts.
Calculate likely runoff flowpaths (in the ArcToolbox, use the Spatial Analyst Tools, then under
Hydrology, choose Fill - Flow direction - Flow accumulation, see course project_video_4).
Display and symbolize flow accumulation to show watercourses draining the area. Note that flow
accumulation values are recorded as cells, so you'll need to calculate the number of cells for that
given area to identify the watercourse. You can find the cell dimension by right clicking on the
TOC (Table of Content in ArcMap) entry for the flow accumulation layer, then properties – source,
and remember the data are stored in UTM meters. The cell dimension is given, so you can calculate
the number of square meters per cell, and then the number of cells per hectare, given that there are
10,000 square meters per hectare. Set the threshold in your symbology at this number of cells,
white above, black below, to identify watercourses (see course project_video_4).
Where is the discharge point? To
get the discharge point, please
create the river network and show
where the water is leaving the focus
area (see course project_video_5).
Create a "discharge point" at the
watershed outlet where it leaves the
focus area layer, and calculate the
corresponding watershed (you
should have an area that appears
similar to the graphic to the above -
note the discharge point is a small
green dot at the bottom of the focus
area, and the station location the
yellow dot. There may be a
difference between this map and
your new map. This difference is
expected and you can continue to
produce your own outputs from the
analysis.
Discharge location
Now we would like to identify areas that have greater than 35% impervious cover within both the
focus area and the contributing watershed. You should end up something like the feature below
(see also the course project_video_6).
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Discharge location
Discharge location
First, load the impervious surface layer. Reclassify
the layer (see course project_video_6,) Spatial
Analyst-Reclassify) to categorize those areas above
35% to a value of 1, and below 35% as 0. Note that the yellow dot is the proposed train station
within the watershed.
Create and export a layout that shows the areas greater than 35% impervious surface. Set the
symbol for <35% to no color, and >35% to red. Set the transparency to 50% (Properties –
Display tab, then fill the transparency box with a 50% value).
Display these over the images, for a layout pane that looks similar to the above figure at the right
side, and add a legend, North arrow, scalebar, and title, as usual. Again, your output may be
different from the above figure. This is acceptable and expected.
TurnIn: Now turn the maps you produced for watershed management.
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PART IV - HIGH DENSITY POPULATION SERVICE AREAS, EXISTING
AND TO DEVELOP
Your remaining tasks (see course project_High density population service areas_video_7) are
to create a map that depicts a parcels which currently fall (completely or partly) within areas of
high population density, and b) areas with low population density that are also inexpensive, and
thus candidates for increasing population density.
For a) calculate population density from the Shady Oaks census data layer. Note that these data
are bit messy, population are in the populatio2 item, and there are some small polygons that are
artifacts/errors. Also note that these data are in UTM NAD83 Zone 15N coordinates, but this is
not recorded for these data (no prj file), so you will likely need to fix this (ArcToolbox, the data
management tools, projections).
Add a float or double column, calculate the area in sq km for each polygon (to 4 decimal places at
least). Select and delete polygons with areas < 0.0049 sq km, add another column, and calculate
the population density.
Identify areas with a population density greater than or equal to 1000 persons per sqkm within the
boundary of shady_oak_census. Save these polygons to another data layer, named something like
CenHiDen.
Identify polygons in the Shady Oaks parcels layer that
intersect with these high-density census areas (use the
tool we learned in the Lab exercises to select, top row
of main window, Selection, Select by location, select
features from parcel_shady_oaks that intersect with
your CenHiDen layer). Save these parcel polygons to a
new layer, and clip them to the Station Buffer1 layer.
Remove all the parcel/polygons with a value = 0 in the
TOTALVAL column (these are public lands). You
should have a set of polygons similar to the figure at the
right.
Now, identify candidate areas for increased density. Go back to the parcels_shady_oak layer, and
add a value per square meter column, similar to your calculation for population density for the
census data layer. You'll need to create a column and calculate the area via calculate geometry,
and then create another column to hold the value per square meter. Total value for the parcel is in
the item TOTALVAL, divide by area to get value per square meter. Delete those parcels with
totalval of zero, these are public or restricted land. Within the area of the station buffer, identify
those parcels that have a value per square meter < $60/sq meter. Now, select from these low-cost
parcels those parcels that have a popden of less than 250 people/square kilometer (as you did
above, first identify the census areas with low popden, then select by location). Clip to the study
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