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7/30/2019 A Spatial Analysis of Visitors to Downtown Flint
1/20
Prepared by Calix Martinez and Juan Villarreal
University of Michigan Flint- Department of Earth and
Resource Science
Spatial Analysis of Perspectives and Addresses of Visitors to Downtown Flint
Department of Earth and Resource Science
April 2013
Prepared for: Downtown Development Authority
7/30/2019 A Spatial Analysis of Visitors to Downtown Flint
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Prepared by Calix Martinez and Juan Villarreal
Contents
INTRODUCTION......................................................................................................................... 2
GOALS........................................................................................................................................... 2
DATA/METHODS ........................................................................................................................ 3
ANALYSIS .................................................................................................................................... 3
2. Data collection ........................................................................................................................3
4. Analysis of the data ................................................................................................................6
b. Spatial Interpolation Method #1: Nearest Neighbor (Thiessen Polygon) ................................6
c. Spatial Interpolation Method # 2: Average Nearest Neighbor ...............................................8
d. Spatial Interpolation Method #3: Kernel Density Estimation ............................................... 10
e. Spatial Interpolation Method # 4: Inverse Distance Weighting ............................................ 12
f. Spatial Prediction method #1: Kriging ................................................................................ 15
g. Spatial Prediction Method #2: Geographically Weighted Regression ................................... 18
Conclusion. .................................................................................................................................. 20
Bibliography ................................................................................................................................ 20
INTRODUCTIONThe purpose of the project, carried out by the University of Michigan-Flint Department of
Earth and Resource Science, was to understand why people are coming to downtown Flint and
where they were coming from. Our study area consisted of people in downtown Flints businessesand streets. Participants were asked questions regarding what would bring them downtown
more often, what would encourage them to move downtown, and do you think thatdowntown Flint is improving. The data collected was with the purpose of statistically and
spatially analyzing the data, with the use of programs such as Arcmap and SPSS, to look for any
spatial relationships or correlations. The goal of the project was to deliver any significantrelationships found to the Flint Downtown Development Authority to assist them with current
and future businesses.
GOALS
The objectives of the project consisted of finding spatial relationships with where our survey
participants lived and how they answered certain questions. Questions that we were interested ininvolved:
Where people who come to downtown Flint are from. Survey respondent density and theyre predicted attitudes regarding Flint.(Assignment 4) The spatial location of our respondents and theyre perception of Flint.(Assignment 5)
7/30/2019 A Spatial Analysis of Visitors to Downtown Flint
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Prepared by Calix Martinez and Juan Villarreal
DATA/METHODS ArcMap was used to perform spatial analysis of our collected data.
o ArcMap is the primary application used in ArcGIS and is used to perform a widerange of common GIS tasks as well as specialized, user-specific tasks.
The data that was necessary to complete the survey was: The DDA master save file, thesurvey responses, addresses and their geocoded points, shapefiles of Michigan and its zip
code zones, a set coordinate system to be used in ArcMap.
The purpose of the survey was to gauge how often individuals come into the downtownarea and for what reasons. Whether it is for recreational reasons like shopping, eating,
etc., or for business reasons like seeking social services, or to be educated. These are just
a few of the questions that were asked to respondents and they were to gauge their
visiting to the area in terms of how often they come into Flint. The survey also tried to
ask respondents what else could be done to downtown Flint, in terms of improvement to
help bring in more visitors/residents to the area. For this questions like, I would visit
downtown more often if crime was reduced, and I would consider living downtown ifthere was more affordable housing that suited my needs were applied to see what factors
people are looking at and considering before, during, and after their trip to the area.
Data collection and retrieval for the survey was done the University of Michigan FlintsERS (Earth Resource Science) department sent out students/surveyors to different
businesses and establishments within the downtown Flint area. After receiving individual
IRB approval, each team was given a specific set of business and during late January
through early February the teams traversed the streets and met up with local business
owners for permission to approach and survey their employees/patrons. Surveys took
around five to ten minutes to complete and were collected either from business owners,
employees and patrons visiting the business.
ANALYSIS
1. The first project step that was taken was completing out a(n) IRB certification.Through the University of Michigan, we completed a series of readings and tests
to ensure we collected data correctly and ethically.
2. Data collection was the next step in the process. We had collected a large sampleof surveys from participants in the downtown Flint area. Making an extra effort to
achieve a large variety of patrons and business owners, we also made sure they
filled out the surveys correctly to make sure we received quality spatial data to
work with.a. The main purpose of the survey was to attempt to understand why people
are, or are not, visiting or living in downtown Flint, in order to make this
area an inviting place to work, play, and live.
3. Preparing the Data was our next step. On this assignment my partner and I usedgeocoding tools to map the survey respondents. Looking at our steps throughout
the project they followed as such: Beginning off, we looked at the DDA survey
7/30/2019 A Spatial Analysis of Visitors to Downtown Flint
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Prepared by Calix Martinez and Juan Villarreal
database and looked for any discrepancies and made adjustments as needed,
filling in or correcting any spatial information that was filled out inaccurately.
From here we checked the addresses and intersections with Google Maps to verify
that all the listings and corrections we made were accurate. From here the actual
Geocoding process began, using different techniques/methods we were able to
spatially reference addresses and locations. Once this was completed we were
able to produce a map of our respondents and their relative locations.
a. In preparing the data, we had made certain that the responses to the surveywere properly filled out through double checking answers ourselves and
referencing locations/addresses using Google Maps. Going through the
data we found that some cross streets werent in their listed city which was
an error on the part of the respondent, seeing this we corrected these to the
right cities. If a street was the only thing listed in the response, we had to
look up area codes for the corresponding roads; if a city or a location, i.e.
East Village Inn then we also had to find a zip code for these locationsas well. One problem we encountered was when we changed entries with
just a city to the cross streets of the cities center. The problem with this
is that arc map will be centering the entries with just within a polygon and
not an actual cross street. So, we had to compare the changed data to the
original data and re-do the data processing. After we had completed the
data verification and cleanup we opened up Arcmap and added the
database to the map and began the geocoding process.
b. Starting the process we opened the program and added our respondentdatabase to the file, other data that we added included a roads, zip-code
layer, and DDA addresses. Next we exported the DDA addresses and thezip codes as a database table. From here we created a dual address locator
and a new address locator based on zip code. Creating the dual address
locator we added the roads clip layer data as reference data, we created its
role to be a primary table using default values, in the field map area.
Creating the address locator we used similar techniques to the dual address
locator except the zip code layer was the reference data.
c. We geo-processed the addresses, and originally had a 73% match with thelocations. Next, we examined the candidates and re-matched them
accordingly with results that had at least a 65% match. After, we re-
matched them; we were able to produce a 90 % match. Geocoding using
zip codes was similar in a lot of ways to the dual address locator. There
were some differences though; the geocoding percent matched was 99%
originally. After we had completed this step the geocoding was finished
and all that was left was to double check for any errors.
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4. Analysis of the data was the next step. This involved using spatial interpolationtechniques through Arcmap to assess the density of our survey respondents, how
often they come to Flint and their perception of Flint.
a. Preliminary steps to Spatial Interpolation:i. Combined our address points and zip-code points into one file
(shape file).
ii. Removal of any respondents that did not answer questions that wewere interested in. The questions of interest were how often they
came to Flint, and if they thought the city was improving.
b. Spatial Interpolation Method #1: Nearest Neighbor (ThiessenPolygon)
i. Creates Thiessen polygons from point input features. Each thiessenpolygon contains only a single point input feature. Any location
within a thiessen polygon is closer to its associated point than toany other point input feature. (ArcGIS, Version 10)
Example Illustration (ArcGIS, Version 10)
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ii. The thiessen polygons in the map tell us that survey respondentswithin the Flint area live very close together, but as you travel out
from Flint the density decreases in other words, a large majority of
our respondents were from the Flint area and were clustered
together.
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c. Spatial Interpolation Method # 2: Average Nearest Neighbori. This SI method calculates a NN index based on the mean distance
from each point to its nearest neighboring point. The Nearest
Neighbor Index is the ratio between the observed mean distance to
the expected mean distance between points. If the index is less
than 1, the pattern exhibits clustering; if the index is greater than 1,
the trend is toward dispersion or competition. (Rybarczyk, 2013)
Example Illustration (ArcGIS, Version 10)
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Prepared by Calix Martinez and Juan Villarreal
ii. After running the average nearest neighbor interpolation, our ratiowas 0.388211. This tells us that our pattern is slightly clustered and
not dispersed, because our value is less than one. In other words,
our respondents seem to live closer to one another versus being
more spaced out.
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d. Spatial Interpolation Method #3: Kernel Density Estimationi. This SI methodology creates a continuous surface that represents
the quantity per unit area that is fitted to the point feature class.
The control points in our case will the geocoded DDA survey
respondents. This technique essentially produces a hotspot map,
similar to the nearest neighborhood and Thiessen polygon
analysis. (Rybarczyk, 2013)
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ii. After running the kernel density estimation we can see the hotspots of our survey participants. Many seem to be right in the Flint
area. Another interesting pattern is the ring that is surrounding the
center hotspot. This suggests that a large percentage of the people
downtown are made of up of people who live outside the city.
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Prepared by Calix Martinez and Juan Villarreal
e. Spatial Interpolation Method # 4: Inverse Distance Weightingi. IDW creates a continuous surface based on a weighted average.
One prime attribute of this method is that points that are redundant
spatially are treated as one. This is important for us because our
survey respondents did not provide accurate addresses in some
cases so their residential locations may be the same as others. We
will interpret the predict density and perceptions of Flint using two
variables, how often they visit Flint, and if they think Flint is
getting better. (Rybarczyk, 2013)
Example Illustration (ArcGIS, Version 10)
7/30/2019 A Spatial Analysis of Visitors to Downtown Flint
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Prepared by Calix Martinez and Juan Villarreal
ii. After running the inverse distance weighting tool, using peoplesperception of whether downtown Flint is improving or not as a
variable, we can see the perceptions of our participants in regards
to if they think Flint is improving or not. There are several hotspots
in and around the Flint area, and even a few far out east towards
the thumb area. However, we do some cooler spot with participants
who dont think that Flint is improving. Most notably spots to the
north, south, southeast, and southwest.
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iii. After running the distance inverse weighing tool, using how oftenpeople come to downtown Flint as a variable, we can see that
many people close to the Flint area visit downtown several times a
week. We can also see that people south of Flint, seem to visit
downtown Flint about a couple times a month. It would seem
distance plays a factor in how often one visits downtown, but there
may be more to it than that. It would seem there are people along a
segment of I-75, south of Flint, who visits Flint several times a
week. This area may be worth further investigation in marketing
downtown businesses.
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f. Spatial Prediction method #1: Krigingi. This tool is used to convert point data to contiguous polygons that
represent zones. Any areas within the zone are closer to its
original point, than any other original point. The kriging formula
involves calculating the difference squared between the values of
the paired locations. The image below shows the pairing of one
point (the red point) with all other measured locations. This
process continues for each measured point.
Example Illustration (ArcGIS, Version 10)
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Prepared by Calix Martinez and Juan Villarreal
ii. After running the kriging tool, using if people thought downtownFlint was improving as a variable, we found an interesting pattern.
Here you can see people who scored as strongly disagree and
disagree are the minority. There are spatially located
sporadically to the north, northwest, south, and southwest.
Interestingly there seem to be an absence of these values to the
east. Surrounding the downtown area and to the east, we see a
strong presence of people who think downtown Flint is improving.
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Prepared by Calix Martinez and Juan Villarreal
iii. After running the kriging tool, using how often people come todowntown Flint, we found an interesting spatial distribution. It
would seem no one scored as never coming to downtown Flint and
a strong population to the west, northwest, north, east, and southeast
of the city say they come several times a week. Looking directly
south of the city you can see a population that comes downtown
once a week or less.
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Prepared by Calix Martinez and Juan Villarreal
g. Spatial Prediction Method #2: Geographically Weighted Regressioni. This is a local form of linear regression used to model spatially
varying relationships. In this model, our dependent variable will be
how often people come to downtown Flint.
Example Illustration (ArcGIS, Version 10)
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Prepared by Calix Martinez and Juan Villarreal
ii. The average observed value from the survey is 3.8, while thepredicted value is about the same at 3.829. This means in terms of
economic development, that our predictions are about on point
with the observed values, meaning that there is a population that is
regularly visiting the downtown area that could be utilized to help
bring more economic development to the area.
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Prepared by Calix Martinez and Juan Villarreal
To conclude, there were relatively few surprises and we were able to accurately collect,
prepare, use, and interpret our data. Collecting of the surveys was relatively easy and went
unhindered. Preparing the data through Geocoding and referencing our spatial points allowed us
to see where respondents are located near. Our analysis through spatial interpolation shows us
that survey respondents within the Flint area live very close together, but as you travel out from
Flint the density decreases, also our respondents seem to live closer to one another versus being
more spaced out. From our kernel density mapping we see that the map suggests that a large
percentage of the people surveyed downtown are made of up of people who live outside the city.
From our inverse distance weighted results we see that the majority of people do feel that the city
is improving. However, there are still some small pockets of people who do disagree as seen on
the map. From our kriging techniques we can see a large majority of the population agree, that
downtown Flint is definitely approving, but there are few that still need some convincing. Also,
through kriging, we can see a strong population that visit the city weekly that live within 15
miles of the city. Lastly, through our geographically weighted regression we were able to
determine that current populations frequenting the area have taken notice of recentdevelopments, and it is available to be used to bring about more improvements.
BibliographyArcGIS. (Version 10). ArcMap Desktop Help.
Rybarczyk, G. (2013).Project Assignment #4, Spatial Interpolation. Flint: GeographicInformation Science II.