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Once the tool has finished running, you will see a message at the bottom of the Geoprocessing pane, indicating that the tool has completed with warnings.

1. Click the arrow to the left of the tool name to expand the Hover over the Completed with warnings message to display the tool parameters and messages.
2. (Pic ToolMessages)

Percent Commuting By Carpool

As the Geoprocessing pane doesn’t reset after a tool has finished running, it is easy to rerun tools with slightly modified settings. In future versions of ArcGIS Pro, batch processing is also supported, which facilitates multiple runs of the same tool within a single interface.

1. For 'Input Field', use the drop-down menu to select the  Percent_CarTruckOrVan_carpooled  field.
2. Check Generate Report.
3. Click Run.

Percent Commuting By Public Transportation

1. For 'Input Field', use the drop-down menu to select the  Percent_PublicTransportation  field.
2. Check Generate Report.
3. Click Run.

Under the lower 'Messages' section, notice that the warning message indicates that the neighborhood search distance used in the analysis was 38893 feet. Since the distance threshold does impact the statistical results, it is important to make note of it. You will learn more about the impact of the distance threshold later on. While the Moran's Index and z-score are also displayed in the messages, they are easier to interpret in the context of the report.

1. Within the 'Spatial Autocorrelation Parameters and Messages' pop-up window, click the Report File hyperlink to open the report in your default web browser.

In the top left, notice that the z-score is 39.43. In the top right, notice that any z-score larger than 2.58 has less than 1% chance of occuring randomly. In the center section is the normal distribution curve and a dotted line illustrating where the z-score for this analysis is located on that curve. It also illustrates that a positive z-score indicates that the data is clustered, while a negative z-score would indicate that the data is dispersed. Below the diagram is a helpful summary sentence, "Give the z-score of 39.43, there is a less than 1% likelihood that this clustered pattern could be the result of random chance." Because the z-score is more than an order of magnitude larger than 2.58, you will notice in the top left that the p-value is actually 0.00, which means there is no chance that this pattern is random. While simply knowing that the percentages of people who drove alone to work are highly clustered might not provide you with actionable knowledge, what this result does indicate is that there is a strong pattern present, which is worth investigating further. Because you know your data displays strong clustering, you can now ask more interesting questions. Is it the high or low percentages of commuters driving alone that are clustered? Where are they clustered? You will see find that all of the spatial statistics tools build upon each other to provide additional knowledge.

Percent Commuting By Carpool

As the Geoprocessing pane doesn’t reset after a tool has finished running, it is easy to rerun tools with slightly modified settings. In future versions of ArcGIS Pro, batch processing is also supported, which facilitates multiple runs of the same tool within a single interface.

1. In the Geoprocessing pane, for 'Input Field', use the drop-down menu to select the Percent_CarTruckOrVan_carpooled field.
2. Click Run.
3. Hover over the Completed with warnings message and click the Report File hyperlink.

This time, the z-score is 28.06, which is slightly lower than before, but still indicates that the percent of people who carpool to work is also highly clustered.

Percent Commuting By Public Transportation

1. In the Geoprocessing pane, for 'Input Field', use the drop-down menu to select the Percent_PublicTransportation field.
2. Click Run.
3. Hover over the Completed with warnings message and click the Report File hyperlink.

Again, the z-score indicates that the percent of people who took public transportation to work is highly clustered. The enormous z-score of 55.84 indicates that the public transportation variable is the most highly clustered of the three. Logically, this makes sense, because we would think people living near metro stations or major bus routes would be more likely to use them and therefore people taking public transportation to work would be clustered around those stop locations.

Incremental Spatial Autocorrelation

1. At the top of the Geoprocessing pane, click the Back arrow button.
2. Within the Analyzing Patterns toolset, click the Incremental Spatial Autocorrelation tool.
3. In the upper right corner of the ‘Incremental Spatial Autocorrelation’ tool, hover over the Help ? button.
4. (Help)

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The Incremental Spatial Autocorrelation tool is going to repeat

Percent Commuting Alone By Car

1. For 'Input Feature Class', use the drop-down menu to select the CensusTracts_MTTW_2014 layer.
2. For 'Input Feature ClassField', use the drop-down menu to select the  CensusTracts_MTTW_2014  layer. the Percent_CarTruckOrVan_droveAlone field.
3. For 'Number of Distance Bands', type 25.
4. For 'Output Report File (optional)', click the Browse folder icon. type ISA_DroveAlone_25.pdf
5. Click Run.

Percent Commuting By Carpool

1. For 'Input Field', use the drop-down menu to select the  the Percent_CarTruckOrVan_droveAlonecarpooled field.
2. Check Generate Report.
3. Click Run.

Percent Commuting By Carpool

1. For 'Number of Distance Bands', type 15.
2. For 'Output Report File (optional)', click the Browse folder icon. type ISA_Carpooled_15.pdf
3. For 'Input Field', use the drop-down menu to select the  Percent_CarTruckOrVan_carpooled  field.
4. Check Generate Report.
5. Click Run.

Percent Commuting By Public Transportation

1. For 'Input Field', use the drop-down menu to select the Percent_PublicTransportation fieldfield.
2. For 'Number of Distance Bands', type 15.
3. For 'Output Report File (optional)', click the Browse folder icon. type ISA_PublicTransit_15.pdfCheck Generate Report.
4. Click Run.

High/Low Clustering (Getis_Ord General G)

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1. At the top of the Geoprocessing pane, click the Back arrow button.
2. Within the Analyzing Patterns toolset, click the High/Low Clustering (Getis_Ord General G) tool.
3. In the upper right corner of the ‘High/Low Clustering’ tool, hover over the Help ? button.
4. (Help)

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1. For 'Input Feature Class', use the drop-down menu to select the CensusTracts_MTTW_2014 layerlayer.
2. For 'Input Field', use the drop-down menu to select the  the Percent_CarTruckOrVan_droveAlone field.
3. Check Generate Report.
4. Click Run.
5. Hover over the Completed with warnings message and click the Report File hyperlink.

Pay attention to the “General G Summary” table, which shows this is a “low-clusters” scenario.

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1. For 'Input Field', use the drop-down menu to select the Percent_CarTruckOrVan_carpooled fieldfield.
2. Check Generate Report.
3. Click Run.
4. Hover over the Completed with warnings message and click the Report File hyperlink.

Pay attention to the “General G Summary” table, which shows this is a “high-clusters” scenario.

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1. For 'Input Field', use the drop-down menu to select the Percent_PublicTransportation field.
2. Check Generate Report.
3. Click Run.
4. Hover over the Completed with warnings message and click the Report File hyperlink.

Pay attention to the “General G Summary” table, which shows this is a “high-clusters” scenario.

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