Lab 5: Pattern Analysis

Spatial Statistics Tools for Pattern Analysis

This exercise involves using ArcGIS Spatial Statistics tools to analyze the spatial patterns of bike-route densities of the neighborhoods within the Metro urban growth boundaries (UGB). Follow the instructions below and answer all questions at the end of this exercise.

A. Data preparation

1. Start ArcCatalog and create a personal (or file) geodatabase call “lab5” in a folder you have read/write access.
2. Start ArcMap, connect to I:\Students\data\GIS\RLIS\2007_Nov\ESRI Shapefiles\, and add UGB_FILL.lyr and NBO_HOOD.lyr in the BOUNDARY folder and BIKE_RTE.lyr in the TRANSIT folder to the current data frame. You may have to re-connect the layers to their source data is the path is broken (go to layer properties, source tab, set data source). You will find the neighborhood map includes neighborhoods outside UGB and there are several different types of bike routes.
3. Click on the Arctoolbox icon in ArcMap to open the Arctoolbox and select “Analysis Tools -> Extract -> Clip” tool. In the Clip tool dialog window, use the pull-down selecting list to specify “nbo_hood” as the input feature and “ubg_fill” as the clip feature. Navigate to the lab5 geodatabase (lab5.mdb) and set “NBO_HOOD_Clip” in the geodatabase as the output feature class. Leave the Cluster Tolerance field blank. Click OK. When it’s done, the NBO_HOOD_Clip is added to ArcMap. Open the attribute table of NBO_HOOD_Clip and compare the difference with the attribute table of nbo_hood.
4. Next, we want to select the bike routes that are cyclist-friendly. Open the attribute table of bike_rte (i.e., bike_rte.shp), click on the “options” button, and select the “Select by Attributes…” item. Use the SQL query builder to create the following select statement:

"BIKEMODE" = 'Bike lane' OR "BIKEMODE" = 'Regional multi-use path' OR "BIKEMODE" = 'Local multi-use path'

5. Click Apply and close the SQL dialog window. The selected bike routes in ArcMap are high-lighted. Open the Clip tool and specify “bike_rte” as the input feature, “ubg_fill” as the clip feature, and “bike_rte_Clip” as the output feature class. Click OK. When it’s done, the “bike_rte_Clip” is added to ArcMap.
6. Now, we can calculate the bike-route density for the neighborhoods within UGB. This can be done with a spatial join or the identity tool. However, the results from both methods could be different. We use the spatial join method first. Right-click on the NBO_HOOD_Clip layer in ArcMap and select “join…” from the “joins and relates” menu. Select “join data from another layer based on spatial location” for “What do you want to join to this layer?” field and set bike_rte_Clip as the layer to join. Read the descriptions of the default joining method carefully and check the checkbox next to sum. Specify the output as a feature class called “NBO_bikerte_sj” in lab5.mdb. Click OK to finish spatial joining.
7. Open the identity tool in the “Analysis tools -> Overlay” toolset. Specify bike_rte_Clip as the input feature, NBO_HOOD_Clip as the identity feature, and NBO_bikerte_id as the output feature class in your lab5 geodatabase. Use ONLY_FID as the value for JoinAttributes and click OK.
8. Open the summary statistics tool in the “Analysis tools -> Statistics” toolset. Specify NBO_bikerte_id as the input table and NBO_rte_sum as the output table in lab5.mdb. Select Shape_Length as the statistics field. The Shape_Length is added to the table below. Click on the blank cell in the Statistics Type column next to Shape_Lenght and select SUM. Select FID_NBO_HOOD_Clip for the case field. Click OK.
9. After the tool finished, add the NBO_rte_sum table to ArcMap and open it. This table summarizes the total bike-route length for each neighborhood in UGB. Frequency refers to the number of routes (i.e., line features) in each neighborhood polygon. The values in FID_NBO_HOOD_Clip are the OBJECTIDs of the neighborhoods. We can use this field to join the route-length data to the neighborhood polygons.
10. Right-click on the NBO_bikerte_sj layer and select “join…” from the joins and relates menu. Select “join attributes from a table” for “What do you want to join to this layer?” field. Select OBJECTID as the field the join will be based on in the NBO_bikerte_sj table, set NBO_rte_sum as the table to be joined, and select FID_NBO_HOOD_Clip as the field the join will be based on in the NBO_rte_sum table. Click OK.
11. We have finished the calculation of bike-route length in each neighborhood. Notice that the values of route length derived from the two methods are different. We continue to calculate the bike-route densities using only the results derived from the identity method. Exit ArcCatalog before proceeding to the following steps. Open the attribute table of NBO_bikerte_sj. Click on the options button and select “add field….” Enter “route_density” as the new field name and set the field type to “float” (i.e., floating-point numbers). Click OK to add the field to the attribute table of NBO_bikerte_sj. Right-click on the heading row of the “NBO_bikerte_sj.route_density” field and select “Calculate values….” Click YES to the message window popped up and enter the followings in the dialog window.

[NBO_rte_sum.SUM_Shape_Length] / [NBO_bikerte_sj.Shape_Area]

12. Click OK and answer YES to the message window popped up. The route density is calculated.

B. Analysis

1. You can now remove all joins from the NBO_bikerte_sj attribute table. Right-click on the NBO_bikerte_sj layer and select “Remove all joins” from the “joins and relates -> remove join(s)” menu. Use the route_density field to display the NBO_bikerte_sj map in ArcMap. Visually check if there are any conspicuous patterns on the map. We will use several spatial statistics tools to quantify these patterns.
2. Read ArcGIS Desktop Help on topics related to the following tools:

Spatial autocorrelation (Moran’s I)

High/low clustering (Getis-Ord General G)

Cluster/Outlier Analysis with Rendering

Hot Spot Analysis with Rendering

3. Open the “Spatial autocorrelation (Moran’s I)” tool (script) in the “Spatial Statistics Tools -> Analyzing Patterns” tool set. Set the input feature class to NBO_bikerte_sj and the input field to route_density. Use the default values for other fields. Check the display output graphically checkbox and click OK. While the tool is running, make sure that the “close this dialog when completed successfully” checkbox is unchecked. Read the descriptions and results on the graphic output window careful. Note that “0 (zero)” is used for Distance band or Threshold Distance to indicate that there’s no cutoff distance for calculating weights (i.e., all features are used in the calculation).
4. Open the “High/low clustering (Getis-Ord General G)” tool (script) in the “Spatial Statistics Tools -> Analyzing Patterns” tool set. Set the input feature class to NBO_bikerte_sj and the input field to route_density. Check the display output graphically checkbox and click OK. While the tool is running, make sure that the “close this dialog when completed successfully” checkbox is unchecked. Due to a bug in the script, the tool appears to be still running before you close the  graphic output window. Before you close the window, read the descriptions and results on the graphic output window carefully. After examining the graphic output, click close to dismiss the window. Now, the script should print out the statistics in the message window and stop running.
5. Open the “Cluster/Outlier Analysis (Anselin Local Moran’s I” tool in the “Spatial Statistics Tools -> Mapping Clusters” tool set. Set the input feature class to NBO_bikerte_sj, the input field to route_density, the output layer file to “Local_I”, and the output to “Local_I” as a shapefile.
6. Next, we will use the “Hot Spot Analysis (Getis-Ord Gi*)” tool in the “Spatial Statistics Tools -> Mapping Clusters” tool set. Set the input feature class to NBO_bikerte_sj, the input field to route_density, the output to “Local_G” as a shapefile, the Conceptualization of Spatial Relationships as Inverse Distance, and the Distance Band or Threshold Distance to zero.

Questions:

1. What do “AREA” and “Shape_Area” refer to in the attribute table of NBO_HOOD_Clip?
2. There is a record in the NBO_rte_sum table having an FID_NBO_HOOD_Clip value of -1. What does this mean?
3. Explain why the bike-route lengths calculated using a spatial join and the identity tool are different.
4. Did the bike-route densities measured at a neighborhood scale exhibit a clustered pattern? In what ways? What are the Moran’s I, Getis-Ord General G, and their associated Z values?
5. Print maps showing the distributions of Local Moran’s I (i.e., LMiIndex), z scores of Local Moran’s I (i.e., LMiZscore), and the z scores of Getis-Ord Gi* (i.e., GiZScore) of neighborhood bike-route densities within Metro UGB.