Grouping And Processing Geospatial Data In Arcgis With Python

Loading Geospatial Datasets into Geodatabases

Geodatabases in ArcGIS provide an efficient way to store, query, and manage geospatial data for analysis. When working with Python, we can use the arcpy module to load various dataset types into geodatabases, including shapefiles, CSV files, and imagery. The CreateFeatureclass_management tool creates new empty feature classes with defined geometry types and spatial references. We can then use insertion cursors to populate the feature classes by looping through source features and adding rows. For raster data, the CopyRaster_management function imports imagery into raster dataset containers. Using geodatabases to consolidate data improves data organization and allows leveraging advanced storage functionality for performance gains.

Key Concepts

  • Create geodatabases to consolidate and manage geospatial data
  • Load vector data like shapefiles into feature classes using cursors
  • Import rasters into geodatabases with CopyRaster_management
  • Define coordinate systems and geometry types when creating feature classes

Sample Python Script

import arcpy

# Create a file geodatabase
gdb_path = r"C:\Project\Data.gdb"
arcpy.CreateFileGDB_management(out_folder_path, "Data.gdb")

# Define parameters for feature class  
sr = arcpy.SpatialReference(32611) # Coordinate system 
geometry_type = "POLYGON" # Geometry type

# Create feature class
arcpy.CreateFeatureclass_management(gdb_path, "Landuse", geometry_type, 
# Insert cursor to populate rows 
cur = arcpy.da.InsertCursor(gdb_path + r"\Landuse", ["SHAPE@","LANDUSE"]) 
for row in landuse_shapes:
    # Add the shape and attributes 
    cur.insertRow([row[0], row[1])  
del cur

# Load raster
arcpy.CopyRaster_management(raster_path, gdb_path)

print("Geospatial data loaded into geodatabase")

Exploring Attribute Relationships in Feature Classes

Analyzing the attribute data associated with geospatial features allows discovering useful relationships to support visualization and spatial analysis. Python provides several techniques in ArcGIS to explore attributes. We can view summary statistics for fields using the GetCount_management, GetDefaultFieldInfo_management and ListFields functions. Summary statistics like minimum, maximum and mean help understand attribute distributions. Checking field properties with ListFields also helps identify data types and relational constraints. Using search cursors and field mappings, we can select subsets of attributes and print rows to explore values. Analyzing field relationships this way facilitates appropriate symbology and joining related data for visualization and analysis.

Key Concepts

  • Explore attribute statistics to understand value distributions
  • Identify field properties like data types and relational integrity rules
  • Retrieve and print attribute data rows to directly explore values
  • Discover relationships between fields in a feature class

Sample Python Script

import arcpy

fc = r"C:\Data\Roads.shp" 

# Summary statistics
count = arcpy.GetCount_management(fc) 
print("Feature Count:", count[0])

fields = arcpy.ListFields(fc)
for field in fields:
   print("Field:{0}, Type:{1}".format(, field.type))
# Print first 10 rows 
cursor = arcpy.da.SearchCursor(fc, ["StreetName", "Type", "SpeedLimit"])
for i, row in enumerate(cursor):
    if i < 10: 
print("Attribute exploration finished")  

Performing Spatial Joins to Connect Features

Spatial joins allow appending attributes from one feature layer to another based on spatial relationships. For example, joining census block group polygons to customer addresses based on locations within the polygons. Spatial joins not only transfer attributes but also create derived attributes like area and length calculations. In Python, the SpatialJoin_analysis function performs spatial joins between target and join features, supporting one-to-one, one-to-many and many-to-many joins. Specifying "JOIN_ONE_TO_ONE" ensures a single match while "JOIN_ONE_TO_MANY" allows multiple matches between input features. The intersect, contains and within predicates define the spatial relationship logic. Spatial joins facilitate comprehensive analysis by combining attributes over features based on proximity and locations.

Key Concepts

  • Append attributes from one layer to another based on spatial relationships
  • Supports one-to-one, one-to-many and many-to-many joins
  • Specify intersect, contains or within as the join predicate
  • Derived attributes provide additional statistics like area and length

Sample Python Script

import arcpy

target_features = r"C:\Data\Addresses.shp"
join_features = r"C:\Data\BlockGroups.shp"  

output_fc = r"C:\Project\Joined.shp"  

# Spatial join with one-to-many relationship
arcpy.SpatialJoin_analysis(target_features, join_features, 
print("Spatial join completed between addresses and block groups")

Using Feature Layers to Symbolize and Visualize

Feature layers in ArcGIS provide capabilities to dynamically visualize and symbolize geospatial data for analysis. In Python, the MakeFeatureLayer_management function creates feature layers that reference data sources instead of copying features. We can then apply symbology using the ApplySymbologyFromLayer_management method by referring to existing layers containing the desired symbols. For categorical data like land-use types, unique value renderers assign symbols based on field values. We can also visualize gradients with colored ramps based on numeric attributes like elevation or population density. Feature layers facilitate quickly iterating to apply multiple symbolization schemes for visualization without duplicating data sources.

Key Concepts

  • Create dynamic feature layers from data sources
  • Apply symbology from existing layers with categorized and graduated styles
  • Modify renderer properties to adjust class breaks, color ramps etc.
  • Feature layers improve visualization performance compared to static drawing files

Sample Python Script

import arcpy

input_features = r"C:\Project\Landuse.shp" 

# Create feature layer
output_layer = arcpy.MakeFeatureLayer_management(input_features, "Landuse_Layer")

# Apply unique value renderer 
sym_layer = r"C:\Project\Renderers\Landuse.lyr"   
arcpy.ApplySymbologyFromLayer_management(output_layer, sym_layer)

# Update class breaks
renderer = output_layer.renderer 
renderer.classBreakValues = [1, 2, 3, 4, 5]  

print("Applied unique value renderer to landuse feature layer")

Creating Groups with Spatial Queries

Spatial queries in Python help subdivide features into groups based on location and attribute constraints. Using select by location functions like SelectLayerByLocation_management, we can select features that intersect, completely contain or are within other features. These selections can be saved as new layers using CopyFeatures_management. Attributes queries with SelectLayerByAttribute_management filter features by constraints on field values. Combining spatial and attribute constraints allows powerful grouping logic, like selecting residential buildings located within school districts. Groups created this way can feed into geoprocessing tools for specialized analysis. Spatial queries provide flexibility to organize features spatially and thematically for targeted analytic workflows.

Key Concepts

  • Subset features spatially with select by location tools
  • Filter features by attributes with select by attribute functions
  • Combine spatial and attribute constraints
  • Save output selections as new feature classes

Sample Python Script

import arcpy 

buildings = r"C:\Data\Buildings.shp" 
schools = r"C:\Data\Schools.shp"
districts = r"C:\Data\Districts.shp"  

# Spatial constraint: buildings within 400m of schools
# Attribute constraint: residential buildings   
query = """ "BLDG_TYPE" = 'Residence' """                             
arcpy.SelectLayerByAttribute_management(buildings, "SUBSET_SELECTION", query)

# Save output selection
output_folder = r"C:\Project\Groups"                                  
arcpy.CopyFeatures_management(buildings, output_folder + r"\Residences_by_Schools") 

print("Created subset of residential buildings within 400m of schools")   

Processing Geometry with Geoprocessing Tools

In Python, ArcGIS provides extensive geoprocessing functionality to construct, transform and analyze geometry. Key geometry tools include buffer, clip, merge, simplify, smooth and intersect. Buffering creates polygons surrounding input features at specified distances. Clipping cuts features using polygon boundaries. Merge appends multiple feature classes into a single new output. Simplify reduces vertices while preserving essential shape characteristics. Smooth reshapes features to appear more organic by smoothing corners. Intersect computes the geometric intersection between multiple input layers. Combine these tools into workflows that prepare, construct and transform geometry to meet specific analytic needs involving proximity analysis, field calculations and 3D modeling.

Key Concepts

  • Buffer: Generate polygons surrounding input features
  • Clip: Cut features to the boundary of a clip polygon
  • Merge: Append multiple features into a single class
  • Simplify: Reduce number of vertices while preserving shape
  • Smooth: Reshape features with smoothed corners
  • Intersect: Compute geometric intersection between layers

Sample Python Script

import arcpy

roads = r"C:\Data\Roads.shp"
parcels = r"C:\Data\Parcels.shp"
output_folder = r"C:\Project\Processing"

# Buffer roads by 100 meters 
arcpy.Buffer_analysis(roads, output_folder + r"\Roads_Buffer.shp",
                      buffer_distance_or_field="100 Meters")  

# Clip parcels by city boundary                      
city_limit = r"C:\Data\CityLimit.shp"  
arcpy.Clip_analysis(parcels, city_limit, 
                    output_folder + r"\Parcels_Clip.shp")
# Simplify road geometry
simplified_roads = output_folder + r"\Roads_Simplified.shp"                  
arcpy.SimplifyLine_cartography(roads, simplified_roads,
                                tolerance="10 Meters")
print("Geoprocessing tools applied to road and parcel layers")                    

Automating Workflows with ModelBuilder and Python

ModelBuilder in ArcGIS provides a visual programming environment to link geoprocessing tools into workflows. Models structure repeatable sequences of geospatial data preparation, analysis and mapping tools. Python script tools add advanced logic and computation to models through inline code. Common patterns include iterating over feature layers to run bulk geoprocessing, updating parameters using Python variables and integrating custom functions. Models encapsulate preprocessing, analysis and visualization processes that can execute automatically as tools. Embedding Python enables further customization for dynamic values, progress reporting and error handling. Automated models improve consistency for geospatial workflows, enhance sharing of processes and reduce manual repetition.

Key Concepts

  • Build reusable workflows to automate geospatial processes
  • Chain together sequences of geoprocessing and mapping tools
  • Add Python script tools to enable advanced logic and computation
  • Expose user-defined parameters for interactive execution
  • Share workflows as tools to standardize geospatial workflows

Sample Python Script Tool

import arcpy
import os 

# Input Parameters
input_folder = arcpy.GetParameterAsText(0)
output_name = arcpy.GetParameterAsText(1) 

# Paths  
input_features = os.path.join(input_folder, "StudyArea.shp") 
output_folder = os.path.dirname(input_folder) 

# Analysis  
output_buffer = os.path.join(output_folder, output_name + ".shp")
arcpy.Buffer_analysis(input_features, output_buffer, "500 Meters")

print("Created 500 meter buffer output " + output_name)

Case Study: Analyzing Urban Growth Patterns

This case study demonstrates a Python workflow in ArcGIS to characterize urban growth patterns over time. The process integrates geoprocessing tools to isolate new urban areas, key attribute joins to link parcel records and spatial queries to extract relevant trends. The workflow first buffers historical urban boundaries to create areas of influence, then clips recent parcel data to focus on buffer zones. Joining clipped parcels to attribute tables with construction dates allows selecting recently developed units. Finally, summarizing parcel counts and density by type within cities shows leading growth locations and patterns. This automated analysis highlights growth hot spots, helping planners target infrastructure needs.

Key Concepts

  • Walkthrough of geospatial analysis workflow in Python
  • Integrate geoprocessing tools: buffer, clip, select, summarize
  • Join attribute data to enable effective spatial queries
  • Automate analytic processes using Python and ModelBuilder

Python Script Tool

import arcpy
import os

# Inputs 
city_features = arcpy.GetParameterAsText(0) 
parcel_features = arcpy.GetParameterAsText(1)
table_join = arcpy.GetParameterAsText(2)  

# Analysis settings
distance = "1 Mile"  

# Output folder
out_folder = arcpy.GetParameterAsText(3)
output_name = arcpy.GetParameterAsText(4)  

# Process
# Step 1: Buffer cities to create areas of influence  
city_buffer = arcpy.Buffer_analysis(city_features, os.path.join(out_folder, output_name + "_CityBuffer"))

# Step 2: Clip parcels to buffered city areas
parcel_clip = arcpy.Clip_analysis(parcel_features, city_buffer, os.path.join(out_folder, output_name + "_ParcelsClip"))  

# Step 3: Join construction date attribute data  
arcpy.JoinField_management(parcel_clip, "APN", table_join, "APN")  

# Step 4: Select recent parcels and examine type patterns 
arcpy.SelectLayerByAttribute_management(parcel_clip, "NEW_SELECTION", """"YearBuilt" > 2015""")
summary = arcpy.Frequency_analysis(parcel_clip, os.path.join(out_folder, output_name + "_Summary"))

print("Urban growth workflow completed")  

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