Spring 2024 GIS Lab Assignment: Mapping Socioeconomic Data

Spring 2024 Socioeconomic GIS School of City & Regional Planning Lab Assignment #1 Assigned: Jan 19 th , 2024 Due: Jan 28 th , 2024 Objectives:  Familiarize yourself with basic ArcGIS mapping functions  Familiarize yourself with commonly used geographic boundary datasets that are publicly available  Explore how different visualization techniques can influence the way data is communicated and interpreted  Access, map and evaluate spatial data from a variety of sources I. Download geographic boundary files: a. Download 2022 census tract, zipcode tabulation area (zcta) and county boundary shapefiles from this location: https://www.census.gov/geo/maps-data/data/tiger-line.html . Limit your download to the state of Georgia. Figure 1.Standard Hierarchy of Census Geographic Entities. Source: https://www2.census.gov/geo/pdfs/reference/geodiagram.pdf b. Define and describe the characteristics of a census tract, zcta and county. (discuss how they are defined, their purpose, their socioeconomic/demographic characteristics, statistical reliability, etc.) Page 1 of 12

Spring 2024 Socioeconomic GIS School of City & Regional Planning A census tract is a geographic area defined by the Census Bureau. These areas are designed to be relatively homogeneous for population characteristics, economic status, and living conditions. Census tracts are smaller subdivisions of counties or cities and are typically defined to contain between 1,200 and 8,000 people. The census turns the zip code into a polygon and is a statistical entity developed by the United States Census Bureau to produce an aerial representation of a mail route. Unlike ZIP Codes, which are primarily used for mail delivery, ZCTAs are created for tabulating census data. A county is a geographical and political subdivision of a state, typically consisting of multiple cities, towns, or rural areas. Counties are a common administrative division in many countries, including the United States. They serve as units of local government and are responsible for various functions, such as law enforcement, public education, and local infrastructure. Counties can vary widely in size, ranging from small, densely populated urban counties to large, sparsely populated rural counties. c. Create an overlay map in ArcGIS Pro showing the geographic correspondence between the three boundary types. Your map should clearly communicate the spatial relationships between all three spatial entities (Hint: use a combination of color and texture to visualize their relationships effectively). Zoom into a portion of the state where all three spatial boundaries are visible. Your map should have all the standard map elements including a legend, north arrow and scale bar. Page 2 of 12

Spring 2024 Socioeconomic GIS School of City & Regional Planning II. Evaluate data estimates for each geographic scale: a. Download 2022 5-yr median household income estimates for census tract, zipcode tabulation area (zcta) and county from this location: Census_Table_S1903 . Limit your download to the state of Georgia . i. Why are the American Community Survey 5-yr population estimates preferred over the 1-yr and 3-yr estimates? (hint: https://www.census.gov/programs-surveys/acs/guidance/estimates.html ) The 5-year estimates generally have a larger sample and smaller margins of error compared to the 1-year estimates. b. Map the median household income estimates for each spatial unit (hint: you will need to join the tabular data with the spatial data). Page 3 of 12

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Spring 2024 Socioeconomic GIS School of City & Regional Planning Page 4 of 12

Spring 2024 Socioeconomic GIS School of City & Regional Planning c. Explore visualizing the data using the “Natural Breaks” and “Quantile” methods of classification. Basically, you will have two maps for each spatial scale (total of 6 maps). Tracts: ZCTA: Page 5 of 12

Spring 2024 Socioeconomic GIS School of City & Regional Planning Counties: i. Define and describe the “Natural Breaks” and “Quantile” methods, discuss their advantages and disadvantages. Natural breaks aim to find natural groupings or breaks in the data that maximize the differences between classes. It optimizes the arrangement of data values into classes based on the inherent patterns in the data. Natural breaks are good at grouping similar values and maximizing the differences between classes. The disadvantage of natural breaks is that this method can create classes containing widely varying number ranges. In quantile classification, every class accommodates an identical count of features. A major advantage to quantile classification is that they are well suited for evenly distributed data. This classification method ensures an even distribution of data values across classes, eliminating the presence of empty or inadequately populated classes. The main drawback of the quantile classification approach is that items assigned to the same class may exhibit significantly varied values, especially when the data is not evenly spread across its range. ii. What do you observe for each of the methods of classification? Does the spatial distribution appear different based on the classification method used? When comparing natural breaks to quantile classifications at each geographic level, there is observable variation in the spatial distribution. Specifically, at the tract level, the quantile classification reveals a higher proportion of areas with high median income compared to the natural breaks. d. Define and briefly describe (2-3 sentences) in your own words, the following terms: i. Standard error Page 6 of 12

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Spring 2024 Socioeconomic GIS School of City & Regional Planning The standard error is a measure that quantifies the amount of variability or uncertainty associated with a sample statistic . It measures the precision of an estimate. ii. Margin of error Margin of error indicates the amount of uncertainty or potential error associated with the results of a survey or poll. It is expressed as a range within which the true population parameter is likely to fall with a certain level of confidence. A smaller margin of error implies a more precise estimate, while a larger margin of error suggests greater uncertainty in the survey results. iii. Coefficient of Variation Coefficient of variation measures the relative variability or dispersion of a set of values in relation to their mean. It is solved by dividing the standard deviation by mean and then multiplying by 100. A low coefficient of variation indicates that the values in the dataset are relatively close to the mean, suggesting lower relative variability. On the other hand, a high coefficient of variation suggests greater relative variability among the values. e. Map the median household income estimates, the associated margin of error (MOE) and coefficient of variation (CV) for each spatial unit. (Refer to Appendix 3 in the following document for definitions and calculations: https://www.census.gov/content/dam/Census/library/publications/2009/acs/ ACSstateLocal.pdf) i. Describe the spatial distribution of the MOE at each scale. Do you see larger MOEs in urban or rural areas? Do they vary based on the size of the tract, zcta or county?(hint: to make the maps comparable, manually adjust your data categories to have the same range). The accuracy of data measurements varies depending on the geographical level of analysis. At the tract level, there is a notably larger margin of error, and this variability is spread across different tracts. On the county level, the margin of error is higher in rural areas compared to urban areas. In contrast, at the tract level, urban areas exhibit a higher margin of error. When looking at the ZCTA level, the margin of error is diverse and not concentrated specifically in rural or urban areas. Page 7 of 12

Spring 2024 Socioeconomic GIS School of City & Regional Planning ii. Using the CDC recommended thresholds below ( refer to SampleSizeError_CDC.pdf on Canvas Week 2 Module ), map the CV and associated reliability categories at each scale. What inferences can you make about data reliability from these maps? Page 8 of 12

Spring 2024 Socioeconomic GIS School of City & Regional Planning Both at the county and ZCTA levels, there is a substantial presence of high coefficient variation based on the estimate reliability categories. At the tract level, there is a more pronounced concentration of medium coefficient variation. In general, the data presented in these maps indicate lower reliability. III. Understand crosswalks As an analytics professional, you might encounter situations where you have to convert data from one spatial scale to another. Crosswalk files help establish that relationship between spatial scales. In this section you will gain experience in the use of a Geographic Correspondence Engine which will assist you in performing the spatial conversion. For this lab, we will use the MABLE/Geocorr22 geographic correspondence engine published by the Missouri Census Data Center located here: https://mcdc.missouri.edu/applications/geocorr.html Step 1 : Select “Georgia” for your state selection Step 2: Select Census Tract as the source and zcta as the target Page 9 of 12

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Spring 2024 Socioeconomic GIS School of City & Regional Planning Step 3: Select 2020 Census population for the weighting variable. Take a minute to look at the other options . These options become really important when you are converting between geographies that are not nested. For example, in this lab, we are negotiating between tracts and zctas (refer to hierarchy diagram on page 1 to see their spatial relationship) Step 3: Select CSV file as your output format. Run request. Page 10 of 12

Spring 2024 Socioeconomic GIS School of City & Regional Planning Step 4: You should see a processing status window as shown below. Open the CSV file once processing is complete. Step 5: Open the CSV file in Excel. Interpret the results for any one census tract and its relationship with associated zctas (see example on next page). Page 11 of 12

Spring 2024 Socioeconomic GIS School of City & Regional Planning Answer: Interpreting the CSV output. Tract 9603 has a one-to many relationships with 3 zctas. Meaning that tract 9603 overlaps with three different ZCTA. We can see that 93% of the population of tract 9603 live within ZCTA 31624. All the weights for the tract add up to 1. Page 12 of 12 Interpreting the CSV output:  - Tract 9501 has a one-to- many relationship with 3 zctas. What that basically means is that tract 9501 contributes spatially or overlaps with three different zctas.  - The “allocation factor” represents what portion of the tract population lives in each of the zctas. For example, 86% of tract 9501 population resides in zcta 31513  All the weights for a tract or “source” will add up to 1

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Lab Assignment_1_2024

Related Documents