Census Data

As a GIS analyst with the Office of State Human Resources, I was required to develop a series of thematic maps and report on possible spatial patterns in the U.S. Census data to help in a decision-making process for a new job creation program target area. For this assignment the objectives were to Search for, retrieve, evaluate the suitability of, and integrate datasets for specific types of spatial analysis applications, develop the ability to independently locate, filter, prepare, link, and map U.S. Census Bureau tabular data and TIGER/Line® shapefiles, prepare data, and integrate and analyze data in various formats. And demonstrate ability to apply global and/or local pattern analysis to U.S. Census Data.

Strategies: For this problem, I used ESRI's ArcGIS Pro 2.8 along with tools such as the Cluster and Outlier Analysis (Anselin Local Moran's I) tool and the join feature. I Downloaded census tract shapefile from TIGER/line® geography website this polygon layers were used as reference data, then projected the shapefile to NAD_1983_2011_StatePlane_North_Carolina_FIPS_3200_Ft_US. My main input data “percent poverty” was obtained from the U.S census bureau website in table form. U.S Census Table information: ID: ACSST5Y2020.S1701 Title: POVERTY STATUS IN THE PAST 12 MONTHS.

Description: Census provides the official counts of the population and housing units for the nation, states, counties, cities, and towns. It also provides estimates of the population for the nation, states, counties, cities, and towns and intercensal housing unit estimates for the nation, states, and Exact field name S1701_C03_007M is attributes contains the percent poverty data for each tract of the census data.

Methods:

Part 1: To complete this part, I performed add a join, after matching the field type of my data to the U.S census data with calculate field tool. After calculating the fields to match the variable type I added a join to combine their attribute table and obtain percent poverty of each GEOID in our study area (one to one) no new feature class was created. Next, I open the symbology panes and changed the primary symbology to graduated colors and the field to percent poverty to display in on my map.

Part 2: To complete this exercise I performed Outlier Analysis (Anselin Local Moran's I to show hot spots and cold spots based on feature locations and the ranking of the calls. Given a set of weighted features, this tool identifies statistically significant hot spots, cold spots, and spatial outliers using the Anselin Local Moran's I statistic. Followed by overlaying the data on the census data to see if there is some type of relationship.

Process diagram

While completing this assignment I learned about the Anselin Local Moran's I.Given a set of weighted features (census tracts based of poverty level map, the tool identifies statistically significant hot spots, cold spots, and spatial outliers which is our area of interest for job creations

New problem description: Jewelry store wants to find Where the high median household incomes are located in Raleigh towards the major cities of the state (i.e., Raleigh, Durham, Chapel Hill, Greensboro, Charlotte, etc.) to select which in what areas they should open new stores.

Data needed: AFF (American Fact Finder) website to find our data of interest, and we will then use the TIGER (Topologically Integrated Geographic Encoding and Referencing System) website to find an associate shapefile

To solve this problem: I will link the table and shapefile using the add join tool based on the GeoID field, which we created within the tables to ensure both columns had the same Field type. An income gradient was used to visually separate the counties based on income. Finally, I conducted a hot spot analysis on the counties and looked for clustering.