Latest Contributions by RyanRuthart1

The three methods (contiguity edges, edges + corner, k-nn) may be using similar neighborhoods for analysis. Depending on the layout of the polygons contiguity edges and edges + corner can produce very similar neighborhoods if there are few polygons that are only connected by a corner. Using edges + corners can have a big impact if you have a regular spaced grid (fishnet) as you double the neighborhood from 4 edges to 8 with edges + corners. In this example polygon data set the majority of polygons are connected by edges You can try to assess the number of neighbors each polygon is getting by using the Polygon Neighbors Tool it will create  a table with a row for each polygon and its neighboring polygons. Using the Summary Statistics tool with a case field (group field) by the src_OBJECTID and counting the nbr_OBJECTID will produce a table with the number of neighbours for each polygon. If you create a bar chart using the number of neighbors you can see the distribution of number of neighbors. If the average distribution of number of neighbors is similar to the number of neighbors used in the analysis, it could help explain the similar results. Table from Polygon Neighbors Tool Results from Summary Statistics Tool Chart showing distribution of number of neighbors ... View more

I really like working with Pandas, I find it very intuitive to learn and powerful. I also use numpy as required but it is has a much steeper learning curve. Pandas has great grouping and aggregating functions that should make it easy to do aggregations and summary statistics. The pandas dataframe has some built in basic plotting but matplotlib allows for a lot more customization. I use a search cursor to pull all the data into a pandas data frame. I don't think you will run into any memory limits based on the size of file you described. import arcpy import pandas as pd fields_list = ['field1', 'field2'] input_fc = r'c:\temp\temp.gdb\input_fc' feature_data = [] with arcpy.da.SearchCursor(input_fc, fields_list) as cursor:     for row in cursor:         feature_data.append(list(row)) df = pd.DataFrame(feature_data, columns=fields_list)‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍ ... View more

Spatial autocorrelation evaluates a set of features and an associated attribute to determine whether the pattern expressed is clustered, dispersed, or random. If the feature does not have any neighbors within the distance band it cannot compare that features values to its neighbors. As you increase the distance threshold the lone features may eventually get neighbors, but it will also increase the number of neighbors for features which are close together.   By using generate spatial weights matrix (Generate Spatial Weights Matrix—ArcGIS Pro | ArcGIS Desktop ) you have more control over what features are considered as neighbors. You can use the Conceptualization of Spatial Relationships - K nearest neighbors option so that every feature is given the same number of neighbors and no feature will have 0 neighbors. The spatial weights matrix can be used as input to the Spatial Autocorrelation tool (Spatial Autocorrelation (Global Moran's I)—ArcGIS Pro | ArcGIS Desktop ) but not the incremental version (which increases the distance to evaluate its effect).   There may be distance bands for which every feature has neighbors, that is indicated in the tool messages output. You can also identify the features with no neighbors to further investigate the impact they have in your data set. ... View more

In ArcGIS Pro we do not have a tool for fuzzy c-means clustering method. Spatially Constrained Multivariate Clustering can return frequency based probabilities of cluster membership if you use the Permutations to Calculate Membership Probabilities setting. Density Based Clustering (http://pro.arcgis.com/en/pro-app/tool-reference/spatial-statistics/densitybasedclustering.htm) using the HDBScan Clustering Method will also return frequency based Membership Probabilities. ... View more

Hello Huanglei Pan, In ArcMap 10.6 the tool is called Grouping Analysis (Grouping Analysis—Help | ArcGIS Desktop) In ArcGIS Pro we have split Grouping Analysis into non spatial (Multivariate Clustering—ArcGIS Pro | ArcGIS Desktop )  and spatial (Spatially Constrained Multivariate Clustering—ArcGIS Pro | ArcGIS Desktop) versions. I hope this helps, Ryan ... View more

Hello, Since the work flow you describe is looking at clusters of points the 'Density-based Clustering' tool could be used to generate clusters. The results of this tool contain an ID for each cluster. Running the 'Mean Center' tool using the Cluster ID as the case field will create a point for the centers of each cluster detected using density-based clustering. Let me know if that helps! ... View more