Embedded Files
Problem:
How can a business gain a better understanding of their customer distribution when all they have is a spreadsheet with customer addresses?
Analysis Procedures
Often a business will collect demographic information from their clientele. Visualizing this information is important to understanding the data and the first step to understanding clients is to know where they are located. ArcGIS Pro was used to generate an address locator file created from street map data downloaded from the Wake county government website. The address locator and the data from the spreadsheet were then run through the Geocoding tool giving an exact map location for each residence. This information was then plotted with a small graphical icon image on a map.
In Part I of solving this problem, customer ZIP codes were geocoded. I used the Excel to Table tool to convert the spreadsheet of customer addresses into a table for use in ArcGIS Pro. I then created an address locator file of all Wake County ZIP codes in the geodatabase. This file manages address information as a reference for the customer address feature layer during geocoding. The customer address table and the ZIP code address locator file were then integrated in a batch geocoding process using the Geocode Addresses tool. Due to errors in the original customer data spreadsheet, the geocoding results were unable to match 44 addresses. Through the feature class layer menu, five of the geocoding results were reviewed and edited via interactive matching in the Rematch Addresses layer function.
Geocoding by ZIP code did not provide an optimal image of the distribution of customers across the county. To gain a better understanding of customer dispersion, individual addresses needed to be plotted. The customer data was once again geocoded in a similar manner as in Part I with some important differences. The first difference was to add a new field, “Address” to the customer address table. This was needed in order to use the US Address-Dual Ranges style during geocoding. I used the Calculate Field tool to combine street numbers and names into the new “Address” field. The address locator file was then created using Wake County street data instead of the Wake County ZIP code data. The Geocode Address tool was then used to plot the exact geographic location of each customer address. Once again, there were a number of addresses (48 total) that could not be matched. Two of the unmatched results were corrected through the “Rematch Addresses” layer function. In the final step, I selected three key addresses in the geocoded customer data attribute table. These addresses were labeled on the map by using the expression $feature.user_Address in the labeling properties menu.
Results
Click images for a larger version
The centroid of each ZIP code is shown by the green symbol, labeled with the 5-digit ZIP code and the number of customers in the ZIP code.
The center of each icon is placed on the location of each customer's address. Major roads in the county are provided for reference. Closer examination shows secondary roads in light grey. Zip Codes are still shown with light brown lines.
Application and Reflection
Problem description:
When groundwater becomes contaminated, tracing it's movement is important to determine the source, stop it from continuing and warn those who could be impacted by it. While groundwater generally moves downhill like surface water there are varying geologies that alter the path and speed with which it moves. In trying to follow the flow of a contaminant underground collecting samples of well water and seeing the distribution of pollutant concentrations is important.
Data needed:
Obtaining samples of well water can be costly and time consuming. Currently, Wake County residents on well water can submit a sample of water for free water quality testing. This along with addresses of the wells would be one passive method of collecting required data for initial analysis. Further analysis may require the active collection of water samples from wells not voluntarily submitted. These wells could be determined from a separate geocoding analysis of well drilling permits issued by the county.
Analysis procedures:
Once a robust dataset of samples has been collected and concentrations for contaminants entered into a database, the results of the sample can be geocoded. These results can then be analyzed statistically analyzed to identify any hotspots. Repeating this process of collection and analysis on a regular basis would produce information showing the dynamics of groundwater contamination and dilution.
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