Analysis using GeoAnalytics Tools

Analysis using GeoAnalytics Tools is run using distributed processing across multiple ArcGIS GeoAnalytics Server machines and cores. GeoAnalytics Tools and standard feature analysis tools in ArcGIS Enterprise have different parameters and capabilities. To learn more about these differences, see Feature analysis tool differences.

Examples

Choosing where to invest in new public transportation infrastructure can be a complicated problem requiring the visualization of many types of data. You have access to multiple datasets representing existing infrastructure throughout the United States, as well as several layers of census data. You can use Build Multi-Variable Grid to summarize all of this data into a single dataset, with which you can visualize the distance to the nearest existing infrastructure, the mean surrounding population, and the variance of surrounding income for every location in the United States.

Usage notes

Variables can be calculated for point, line, or area features.

For each variable, you must specify the variable type and provide a title. This title will be the name of the field in the result layer that contains the values for each bin. Each variable type that can be calculated using Build Multi-Variable Grid must be configured with a unique set of parameters. These parameters are listed below.

For the Summary of Nearby and Summary of Intersecting options, you can calculate the following statistics on numeric fields:

• Count—Calculates the number of nonnull values. It can be used on numeric fields or strings. The count of [null, 0, 2] is 2.
• Sum—The sum of numeric values in a field. The sum of [null, null, 3] is 3.
• Mean—The mean of numeric values. The mean of [0, 2, null] is 1.
• Min—The minimum value of a numeric field. The minimum of [0, 2, null] is 0.
• Max—The maximum value of a numeric field. The maximum value of [0, 2, null] is 2.
• Range—The range of a numeric field. This is calculated as the minimum values subtracted from the maximum value. The range of [0, null, 1] is 1. The range of [null, 4] is 0.
• Variance—The variance of a numeric field in a track. The variance of [1] is null. The variance of [null, 1,0,1,1] is 0.25.
• Standard deviation—The standard deviation of a numeric field. The standard deviation of [1] is null. The standard deviation of [null, 1,0,1,1] is 0.5.

• Count—The number of nonnull strings.
• Any—This statistic is a random sample of a string value in the specified field.

Bin size specifies how large the bins of your multivariable grid are. If you choose to generate hexagonal bins, the size is the height of each hexagon and the width of the resulting hexagon will be 2 times the height divided by the square root of 3. If you choose to generate square bins, the bin size is the height of the square, which is equal to the width.

GeoAnalytics Tools analysis using binning (hexagon or square) with a specified geographic coordinate system will automatically use a projected coordinate system based on the extent of the data. To learn more about setting your coordinate system for analysis, see Use the analysis environments for GeoAnalytics Tools in Map Viewer.

If Use current map extent is checked, only the features that are visible within the current map extent will be analyzed. If it's not checked, all input features in the input layer will be analyzed, even if they are outside the current map extent.

Limitations

Build Multi-Variable Grid does not work with tables. Input layers must be points, lines, or areas.

How Build Multi-Variable Grid works

The following describe how the Build Multi-Variable Grid tool works:

Distance calculations

All distances in the Build Multi-Variable Grid tool are measured from the center of bins. For both square and hexagonal bins, the center is located at half the bin height and half the bin width, as shown below.

For Distance to Nearest and Attribute of Nearest, the distance to polygon or line features is measured using the closest point in the feature to the bin of interest. Distances to point features are measured using the point location.

Summary calculations

For Summary of Nearby, features are included in summary statistic calculations if any part falls within a circle surrounding the bin center with a radius determined by the Summarize features within parameter. For Summary of Intersecting, features are included in calculations if any part falls within the boundaries of a bin area (no circular radius is used).

ArcGIS API for Python example

The Build Multi-Variable Grid tool is available through ArcGIS API for Python.

# Import the required ArcGIS API for Python modules
import arcgis
from arcgis.gis import GIS
from arcgis.geoanalytics import summarize_data
from arcgis.features import FeatureLayer

# Connect to your ArcGIS Enterprise portal and confirm that GeoAnalytics is supported
portal = GIS("https://myportal.domain.com/portal", "gis_publisher", "my_password", verify_cert=False)
if not portal.geoanalytics.is_supported():
    print("Quitting, GeoAnalytics is not supported")
    exit(1)   

# Find the big data file share dataset you'll use for analysis
search_result = portal.content.search("", "Big Data File Share")

# Look through the search results for a big data file share with the matching name
bdfs_search = next(x for x in search_result if x.title == "bigDataFileShares_CityData")

# Look through the big data file share for roads
roads = next(x for x in bdfs_search.layers if x.properties.name == "roads")

# Look through the big data file share for intersections
intersections = next(x for x in bdfs_search.layers if x.properties.name == "intersections")

# Find a feature layer named "Demographics" in your ArcGIS Enterprise portal
demographics_search_result = portal.content.search("Demographics", "Feature Layer")
demographics_layer = demographics_search_result[0].layers[0]

inputs = [road, intersections, demographics_layer]
variables = [
    {
        "layer":0,
        "variables":[
            {
                "type":"DistanceToNearest",
                "outFieldName":"DistToRoad",
                "searchDistance":20,
                "searchDistanceUnit":"Kilometers",
                "filter":"Rural = 'false'"
            }
        ]
    },
 {
        "layer":1,
        "variables":[
            {
                "type":"AttributeOfNearest",
                "outFieldName":"intersection",
                "attributeField":"intersection_name",
                "searchDistance":50,
                "searchDistanceUnit":"Kilometers"
            }
        ]
    },
 {
        "layer":2,
        "variables":[
            {
                "type":"AttributeSummaryOfRelated,
                "outFieldName":"MeanPopAge",
                "statisticType":"Mean",
                "statisticField":"Age",
                "searchDistance":50,
                "searchDistanceUnit":"Kilometers"
            },
           {
                "type":"AttributeSummaryOfRelated,
                "outFieldName":"VarIncome",
                "statisticType":"Variance",
                "statisticField":"Income",
                "searchDistance":50,
                "searchDistanceUnit":"Kilometers"

            }
        ]
    }
]

# Set the tool environments
arcgis.env.verbose = True
arcgis.env.defaultAggregations = True

# Run the Build Multi-Variable Grid tool
output = summarize_data.build_multivariable_grid(input_layers = inputs, 
       																																										variable_calculations = variables,
                                                 bin_size = 10,
                                                 bin_unit = "Kilometers",
                                                 bin_type = "Hexagon",
																																																	output_name = "CityPlanningGrid")

# Visualize the tool results if you are running Python in a Jupyter Notebook
processed_map = portal.map('City, State', 10)
processed_map.add_layer(output)
processed_map

Similar tools

Use Build Multi-Variable Grid to summarize multiple layers into a single grid of polygons, either for visualization or for later use in training prediction or classification models. Other tools may be useful in solving similar but slightly different problems.