A Spatial Analysis of Visitors to Downtown Flint

7/30/2019 A Spatial Analysis of Visitors to Downtown Flint

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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


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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)


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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


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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)



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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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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)



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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.



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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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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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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.



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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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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.

A Spatial Analysis of Visitors to Downtown Flint

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