using gis to identify and analyze prospective locations john mazzello crp 551 final project july 27,...

Using GIS to Identify and Analyze Prospective Locations

John MazzelloCRP 551 Final Project

July 27, 2011

Problem StatementWhat are the best locations in Iowa for

locating an after-school educational enrichment program, using school test scores data, poverty rates, and access to outdoor natural resources (i.e., parks)?

For the purposes of this assignment, assume that all public schools in Iowa are potential locations for an after-school program.

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John Mazzello: Selecting Locations for After-School Programs

Background and Rationale I worked for an organization that ran before- and after-

school programs at a number of public school locations. When starting a program at a new location, we attempted to secure grant funding, such as from the Department of Education’s 21st Century Community Learning Centers program (21CCLC).

21CCLC is targeted toward rural or urban schools with high poverty and low test scores, and is designed to improve students’ performance in their classes and on standardized tests.

When applying for 21CCLC funding, applicants have to demonstrate that their proposed school locations meet the eligibility requirements.

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John Mazzello: Selecting Locations for After-School Programs 3

Background and Rationale I developed this project because:

I wanted to show that the skills we have learned in class can be used to identify possible locations for similar after-school programs.

An analysis performed using GIS techniques can provide strong supporting evidence for a grant application.

While at Iowa State, I have also been volunteering with an organization that attempts to connect students with Iowa’s natural and cultural resources. This organization believes that students perform better when they have access to educational activities outside the classroom.

After-school programs let students experience activities that may be impossible during the school day because of curriculum requirements, such as outdoor recreation and learning.

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Analysis Criteria:Therefore, I will perform a suitability analysis

to identify schools in Iowa that meet the following criteria :An underperforming school (designated by the

Iowa Department of Education as a “School in Need of Assistance”

Located in an area with high poverty rates (upper 20% of census tract poverty rates)

Located in close proximity to a city or county park (within two miles)

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GIS Processes Used (Summary)1. Creation of a file geodatabase for storing

downloaded data and layer files, as well as results of geoprocessing and analysis.

2. Geocoding the addresses of public schools in Iowa.3. Creation of buffers around high poverty locations to

identify potential schools.4. Creation of buffers around park and recreation

opportunities to identify potential schools.5. Intersect the poverty buffers and the park buffers

to create preferred areas for after-school programs.

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Problem-Solving ApproachStep 1: I downloaded contact information for

all public schools in Iowa from the National Center for Education Statistics (NCES) Elementary/Secondary Information System.

This listing contains 1468 schools in Iowa.I will use this listing to geocode the addresses

of each school.While NRGIS also contains data on Iowa

schools, I am using the NCES data instead because its school names match the names in another data set I will use later.

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Problem-Solving Approach

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Example of the public school contact information tables from NCES

Problem-Solving ApproachStep 2: I downloaded State of Iowa data

from the Natural Resources Geographic Information Systems Library (NRGIS).

Downloaded layers were:Iowa CountiesPoverty by Incorporated PlaceConservation and Recreation Lands with Public

AccessIowa Roads (2006), Highways, and Interstate

HighwaysJuly 27, 2011


Problem-Solving ApproachStep 3: Next, I used ArcCatalog to create a

file geodatabase and add the school address table and the NRGIS layers.

Step 4: I added the county, roads, and school address data from the geodatabase into an ArcMap document and verified the coordinate system (NAD 1983 UTM Zone 15N).

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Problem-Solving ApproachStep 5: I geocoded the school

addresses using ArcMap’s built-in address locator: US Streets Geocode Service (ArcGIS online)

91% of the addresses were geo-coded automatically. I rematched the others as in chapter 7 of the GIS Tutorial 1 book (by correcting addresses, finding them on Google Maps, using the Street layer, etc.).

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Problem-Solving ApproachStep 6: I downloaded a list of 2010-11 Schools

in Need of Assistance (SINA) from the Iowa Department of Education. Schools that do not meet test score benchmarks or participation requirements under the No Child Left Behind law are designated as SINA.

The SINA data was in PDF format. After downloading, I needed to manipulate the data in Excel to “clean up” the columns and make sure that school names matched the names from the address list I downloaded from NCES.

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Problem-Solving ApproachStep 7: Using the school name, I joined a

table with the schools in need of assistance to the geocoded schools layer in ArcMap. This then allowed me to select just the subset of schools that were in need of assistance (i.e., were underperforming according to No Child Left Behind) using “Select By Attributes.”

The 328 remaining schools are the locations I used as potential after-school program sites.

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Data conversion: PDF…

To Excel…

To ArcMap

Problem-Solving ApproachStep 8: Using NRGIS data, I added a new layer to

my map for poverty levels in incorporated places. While the entire state is not covered by

incorporated places, over 95% of schools in need of assistance are located within one. Furthermore, all but 5 schools are located within one mile of an incorporated place. Therefore, I am comfortable using poverty data for these places to identify schools that meet my criteria.

Note that NRGIS poverty data is from 2000. Please see later slides for a discussion of why I chose to use this data instead of 2010 Census data.

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Problem-Solving ApproachStep 9: Using the Symbology feature in the layer

properties, I classified the 2000 poverty rate for each incorporated place as five quantiles. The quantile of incorporated places with the highest poverty had rates of 12.7% to 50% of the population in poverty.

I exported the upper 20% of places to a new layer.I then created a 1-mile buffer around these high-

poverty incorporated places, and selected the schools that fell within the buffer. These are the 113 schools that meet my high poverty rate criterion.

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All Four Quartiles of Poverty Rates in Incorporated Places, with Schools

Problem-Solving ApproachStep 10: I added the Conservation and

Recreation Lands with Public Access layer, which I downloaded from NRGIS.

The lands in this layer have many types of owners and purposes. There are city parks, forest reserves, National Wildlife Refuges, and more.

The most appropriate types for convenient student after-school use are arboretums, city parks, county parks, and greenbelts. I exported these types of recreation lands to a new layer.

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Problem-Solving ApproachStep 11: Using the Buffer function, I created

a 2-mile buffer around these student-friendly recreation areas. 2 miles is an appropriate distance for students and after-school program leaders to travel (on foot or by vehicle) to a recreational area on a relatively frequent basis.

The schools that fall within the buffered areas meet my proximity to a city or county park criterion. 125 schools meet this criterion.

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Student-Friendly Park Lands with 2-Mile Buffer and Schools

Problem-Solving ApproachStep 12: Using the Intersect geoprocessing

function, I created a new layer reflecting the overlap between the high-poverty buffers and the parks and recreation buffers.

The schools that fall in this intersected area are those that meet both the high poverty and access to recreational areas criteria. These are the schools that can be considered the best locations for after-school programs according to the criteria I developed.

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High-Poverty and Park Buffers, and Intersection, with Schools

Problem-Solving ApproachOut of the original 1468 public schools in Iowa:

328 are identified as schools in need of assistance.113 are in high-poverty areas (upper 20% poverty rates).125 are within 2 miles of a student-friendly park land.And only 31 meet all three criteria. This represents

only 2% of all public schools in Iowa.This project has been able to demonstrate that

geoprocessing can be used to address the problem statement. A large number of schools has been refined into a small number of preferred locations.

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Final Recommended After-School Site Locations

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DiscussionThis project was definitely a learning experience. I believe

I got some good practice navigating the many sources of data available online and in performing geocoding and geoprocessing on real-life data.

However, there were some areas where I feel I could improve the project if I were to do it again. First, I had originally wanted to use Census 2010 Tiger/Shapefile and American Factfinder data for demographics like income, which I instead took from NRGIS. I planned to use Census Block data, but when I tried to download the data for the entire state, the Census website could not process all the blocks at once (it kept timing out). Instead, I had to download blocks for each county individually, which would have been unreasonably time-consuming.

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DiscussionNext, I thought about doing the project for just one county,

but counties with enough interesting schools (like Polk County) had so many schools and parks in close proximity that almost every school met the criteria. The project would not have been as interesting.

I also had originally planned to use proximity to a large number of students (5-17 year old population) as a criteria, instead of parks. However, when I was unable to download block data, I found that whole Census tracts were simply too large to use convincingly for this purpose.

Furthermore, even if I had downloaded the Block data instead of using data from NRGIS, the blocks would have been so small on the statewide map that it would have been difficult to see individual Block differences.

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DiscussionTherefore, I think I should have thought more clearly about the focus

area of my project before starting it.Next, I would have paid closer attention to the requirements of data

cleanup. Almost everything I downloaded, from NCES and from the Iowa Department of Education as well as from the Census (while I was trying to use that information) required a lot of editing in Excel.

My criteria may also have been too restrictive. For example, using poverty rates, I Identified Ames but not Des Moines as a high-poverty, area. Clearly, one could argue that Des Moines needs after-school programs, especially as it has many schools in need of assistance.

Finally, I realize that because of the small size of the buffers around incorporated areas and parks, when my maps are zoomed to the entire state, they look less “interesting” than I had originally hoped. Therefore, I added a small sample map to “spice up” my layout.

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Copies of Final Outputs: Overall Map

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Copies of Final Outputs: Schools

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Copies of Final Outputs: Schools and Poverty

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Copies of Final Outputs: Schools and Parks

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Copies of Final Outputs: Final Recommendations

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Copies of Final Outputs: Sample Zoomed-In Map

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References Gorr, W.L. and Kurland, K.S. (2011). GIS Tutorial 1: Basic Workbook. Redlands, CA:

ESRI Press. Iowa Department of Education. (2010). Schools and Districts in Need of Assistance

(SINA/DINA). http://educateiowa.gov/index.php?option=com_content&view=article&id=1923&catid=497&Itemid=2617

Iowa Department of Natural Resources. (2011). Natural Resources Geographic Information Systems Library. http://www.igsb.uiowa.edu/nrgislibx/

National Center for Education Statistics. (2011). Elementary and Secondary Information System. http://nces.ed.gov/ccd/elsi/

U.S. Department of Education. (2011). 21st Century Community Learning Centers. http://www2.ed.gov/programs/21stcclc/index.html

Used but not included in final project: U.S. Census Bureau. (2011). American FactFinder. http://factfinder2.census.gov U.S. Census Bureau. (2011). Downloading Data from the New American FactFinder to use

with TIGER/Line Shapefiles. http://www.census.gov/geo/www/tiger/wwtl/brochures/DownloadingAFFData.pdf

U.S. Census Bureau. (2011). Working with TIGER/Line Shapefiles. http://www.census.gov/geo/www/tiger/wwtl/wwtl.html

July 27, 2011


using gis to identify and analyze prospective locations john mazzello crp 551 final project july 27,...

Documents

proposed school locations

school programsbackground

underperforming school

school day

school programsjohn

potential locations

high poverty locations

best locations