The open data movement has made more and more data available for academics to download and use for their research. But how can we map this data? This workshop will take you through the process of acquiring data from the Los Angeles Open Data portal and visualizing it on ArcGIS for further analysis.
Almost 2 million records! Let’s filter it down to something more manageable.
Now add the filter:
Export the data
Cleaning up those coordinates
Open the downloaded data in Excel. Scroll to the right until you see the Location column.
Hmm, that’s strange, the latitude and longitude columns are in the same column! ArcGIS does not like this. Let’s clean this up.
First, find and replace the brackets.
Select the Location column
Bring up the find and replace tool (ctrl-h)
For “Find what”, enter an open bracket “(“
Click Replace All
Repeat for the closing bracket.
Split the column into two:
Choose, delimited, check the “Comma” box, and finish.
Rename the column headers to Latitude and Longitude
Let’s map it!
Add the csv file to a new ArcMap project. Right click, Display XY Data…
Set X to Longitude
Set Y to Latitude
Click Edit for the coordinate system
Enter “WGS 1984” in the search box
Choose WGS 1984
Now save your new layer as a shapefile, or geodatabase:
Project the data
Our data is currently in a geographic coordinate system (WGS1984). Let’s change this to a projected coordinate system (UTM Zone 11N).
Click on the search tool
Type “project” and click on Project (Data Management)
Now, set the projection of the data frame. Right click on Layers, and go to properties. Then, set the coordinate system to NAD 1983 UTM Zone 11N
Hot spots?
Let’s find crime hot spots by race. Select incidents where the person arrested was classified as Hispanic (H). In the menu bar, go to Selection, Select by attribute. Enter the following SQL statement:
Victim_Decent = ‘H’
Now perform a kernel density to visualize the density of Hispanic arrests in Los Angeles. In the search box, enter “kernel” and click on the Kernel Density (Spatial Analyst) tool. Enter the four boxes as shown below:
Add a basemap, and change the symbology to make the visual more powerful:
The open data movement has made more and more data available for academics to download and use for their research. But how can we map this data? This workshop will take you through the process of acquiring data from the Los Angeles Open Data portal and visualizing it on ArcGIS for further analysis.
The Los Angeles Open Data Portal
https://data.lacity.org/
Inspect the data
Almost 2 million records! Let’s filter it down to something more manageable.
Now add the filter:
Export the data
Cleaning up those coordinates
Open the downloaded data in Excel. Scroll to the right until you see the Location column.
Hmm, that’s strange, the latitude and longitude columns are in the same column! ArcGIS does not like this. Let’s clean this up.
First, find and replace the brackets.
Repeat for the closing bracket.
Split the column into two:
Choose, delimited, check the “Comma” box, and finish.
Rename the column headers to Latitude and Longitude
Let’s map it!
Add the csv file to a new ArcMap project. Right click, Display XY Data…
Now save your new layer as a shapefile, or geodatabase:
Project the data
Our data is currently in a geographic coordinate system (WGS1984). Let’s change this to a projected coordinate system (UTM Zone 11N).
Click on the search tool
Type “project” and click on Project (Data Management)
Now, set the projection of the data frame. Right click on Layers, and go to properties. Then, set the coordinate system to NAD 1983 UTM Zone 11N
Hot spots?
Let’s find crime hot spots by race. Select incidents where the person arrested was classified as Hispanic (H). In the menu bar, go to Selection, Select by attribute. Enter the following SQL statement:
Victim_Decent = ‘H’
Now perform a kernel density to visualize the density of Hispanic arrests in Los Angeles. In the search box, enter “kernel” and click on the Kernel Density (Spatial Analyst) tool. Enter the four boxes as shown below:
Add a basemap, and change the symbology to make the visual more powerful:
Repeat the process for other race categories:
Data in this tutorial