In the following steps, you'll download the temperature measurements and add them to a map. Then, you'll explore the data with a histogram to confirm that an urban heat island effect is present.
Download and explore the project
First, you'll download the project containing the temperature measurements and open it in ArcGIS Pro.
- Download the Analyze_Urban_Heat_Using_Kriging.zip file.
- Locate the downloaded file on your computer.
Depending on your web browser, you may have been prompted to choose the file's location before you began the download. Most browsers download to your computer's Downloads folder by default.
- Right-click the file and extract the contents to a convenient location on your computer, such as your Documents folder.
- Open the unzipped folder to view the contents.
- If you have ArcGIS Pro installed on your computer, double-click Analyze_Urban_Heat_Using_Kriging.ppkx to unpack and open the project.
If you don't have ArcGIS Pro or an ArcGIS account, you can sign up for an ArcGIS free trial.
- Sign in using your ArcGIS Online account.
- If necessary, open the Madison Temperature map and click the Contents tab.
The Madison Temperature map consists of the Light Gray basemap and two feature layers: Temperature_Aug08_8pm and Block_Groups. The Temperature_Aug08_8pm layer contains 139 points spread across Madison, Wisconsin, covering the city center and surrounding rural areas. Each point represents the location of a sensor measuring temperature changes at 15-minute intervals. The points in the Temperature_Aug08_8pm layer represent temperature measurements in degrees Fahrenheit taken on August 8, 2016, at 8:00 p.m. at each of the sensors.
In the layer, sensor locations are symbolized in shades of yellow to red representing change in temperature in degrees Fahrenheit. The lightest shade of yellow corresponds to 73 degrees Fahrenheit (22.78 degrees Celsius), and the darkest shade of red corresponds to 86 degrees Fahrenheit (30 degrees Celsius).
- On the ribbon, on the Map tab, in the Navigate group, click Explore.
- Pan and zoom around the city of Madison to get a sense of the area and the location of sensors.
Note:A city center can be over 10 degrees warmer than the surrounding countryside. In Madison, higher temperatures are found in the middle of the city, and lower temperatures in the surrounding suburban and rural areas. This suggests the presence of the urban heat island effect, but more quantitative analysis is needed to confirm the effect.
- In the Contents pane, right-click Temperature_Aug08_8pm, and choose Attribute Table to open the attribute table for this layer.
The table contains a record of attribute values for each of the 139 individual sensor points. The TemperatureF field maintains the temperature measurement value.
- In the Temperature_Aug08_8pm table, right-click the TemperatureF field and choose Sort Descending.
In the TemperatureF field, the highest temperature recorded is 83.869 degrees Fahrenheit and the lowest recorded value is 73.429.
- Close the Temperature_Aug08_8pm table.
- On the ribbon, on the Map tab, in the Navigate group, click Explore.
- Using the Explore tool, zoom to the location of sensors within the Madison city center.
The city center is located roughly between Lake Mendota and Lake Monona.
Note:Many of the highest temperature locations found within the city center are also in close proximity to lakes, which may be contributing to higher temperatures in the summer (August) by increasing humidity levels in the surrounding areas. For this study, we will ignore this factor, but it may warrant additional exploration later as you refine your workflow.
- In the Contents pane, check Block_Groups.
- Right-click Block_Groups and choose Zoom To Layer.
The Block_Groups layer represents census block groups in the city of Madison and surrounding townships. Block groups are symbolized by the density of residents over the age of 65, calculated by dividing the population over age 65 by the area of the block group in square kilometers.
These block groups will serve as the extent of the study area for the exercise. As a final step, you'll predict the average temperature in each block group to locate areas of Madison that are characterized by both high average temperatures and a high density of residents over the age of 65.
- In the Contents pane, uncheck Block_Groups.
Create a histogram chart for temperature
The first step in developing an interpolation workflow for temperature in Madison is to explore the data and look for interesting features. You can gain a lot of insight by looking at the symbolized points on the map, but you should also explore the data using interactive charts. For this data, a histogram chart is most relevant. The histogram chart allows you to see the distribution of temperature values in order to determine which temperatures are most prevalent in the data points. You will also use selections to identify the points representing the highest and lowest temperature measurements.
- In the Contents pane, right-click Temperature_Aug08_8pm, point to Create Chart, and choose Histogram.
The Histogram pane opens. Initially, it is empty.
- In the Histogram pane, click the Properties button, to open the Chart Properties pane.
- In the Chart Properties pane, on the Data tab, under Variable, for Number, choose TemperatureF.
- In the Statistics group , leave Mean checked, and check Median and Std.Dev.
The chart updates to show a histogram of temperature measurements and the chart title Distribution of TemperatureF appears. Additionally, the Statistics group in Chart Properties updates, showing various statistics for the TemperatureF histogram field.
In the chart, a red vertical line is displayed at the mean (average) temperature value (79.4 degrees). Temperature values are spread fairly evenly between the minimum and the maximum, with the largest number of points showing a temperature between 79.5 and 81.3 degrees. The median temperature is displayed in purple and the Standard Deviation in orange.
In the chart statistics, the Count value is 139 points and the Min and Max temperature values are 73.4 and 83.9 degrees, respectively.
- In the Distribution of TemperatureF histogram, drag a box over the left two bins to select all points that represent locations with the lowest temperature measurements.
The points with the lowest temperature measurements are selected on the Madison Temperature map. These lower temperature measurements are located mostly in the suburban and rural areas surrounding the Madison city center.
- In the Distribution of TemperatureF histogram, drag a box over the last two bins on the right to select locations with the highest temperature measurements.
In the Madison Temperature map, most of the highest temperature measurements are located in the downtown city center area of Madison and additionally in adjacent areas to the northeast and southeast of the city center.
- Close the Chart Properties and Chart panes.
- On the ribbon, on the Map tab, in the Selection group, click Clear to unselect features.
- In the Project pane, save your Madison Temperature project.
In this lesson, you used a histogram to explore distribution of temperature measurements. You found that higher temperature measurements were situated in and around the city center, and that lower temperature measurements were observed in the surrounding suburban and rural areas. This distribution of the temperature values strongly suggests the presence of the urban heat island effect. In the next lesson, you'll use the Geostatistical Wizard to interpolate temperature measurements to create a temperature map for the entire city of Madison and surrounding townships.