Lecture_6_Spatial_Data_Processing

Lecture 6: Spatial Data Processing

1. Spatial Data Infrastructure

• Components of spatial data infrastructure include:

  • File Geodatabases

  • Geoprocessing tools and techniques

  • Map structures (animations, interactive maps, layouts, design)

2. Spatial Data Techniques

• Data Mining: Analyzing large datasets for valuable information.

• Proximity Analysis: Evaluating the distance between geographic features.

• Digitizing: Creating digital maps from raw data.

• Network Analysis: Understanding relationships and flows in networks.

• 3D GIS: Utilizing three-dimensional space for GIS applications.

3. Analyzing Spatial Data

• Geocoding: Converting addresses into geographic coordinates.

• Spatial Regression: Analyzing spatial relationships between variables.

• Spatial Analysis: Comprehensive examination of spatial data.

• Raster Analysis: Working with grid-based datasets.

4. Attribute Extraction

4.1 Attribute Query Extraction

• Extract specific areas of interest, such as county tracts.

• Example: Selecting tracts by County FIPS ID (e.g., Cook County = 031).

• Export selected tracts to a new feature class or shapefile.

4.2 Exporting Selected Features

• Steps for exporting:

  • Right-click on the selected features and choose 'Export Data'.

  • Ensure the coordinate system matches the source data.

5. Feature Location Extraction

5.1 Selecting by Location

• Unique GIS function to identify spatial relationships:

  • Can select features based on proximity or inclusion.

• Example: Selecting Chicago from municipality layers.

6. Location Proximities

6.1 Points Near Polygons

• Useful for health studies (e.g., polluting companies near water).

6.2 Points Near Points

• School proximity to polluting companies.

6.3 Polygons Intersecting Lines

• Determine affected neighborhoods by construction projects.

6.4 Complete Containment

• Identify buildings within zoning areas.

7. Geoprocessing Tools Overview

• Operations manipulate data and require input datasets.

• Produce output datasets after analysis.

7.1 Common Geoprocessing Tools

• Types:

  • Analysis Tools: Clip, Intersect, Union.

  • Data Management Tools: Generalization (Dissolve).

• Tools Access: Geoprocessing menu, ArcToolbox.

8. Geoprocessing Tool Functions

8.1 Clip vs Select by Location

• Clip: Produces clean edges for feature subsets.

• Select by Location: Better for specifying data for geocoding.

8.2 Dissolve

• Combines adjacent polygons into larger ones using common field values.

• Sums relevant data while removing interior lines.

9. Merging Datasets

9.1 Append vs Merge

• Append: Adds features to an existing dataset, ensuring type compatibility.

• Merge: Combines datasets into a single output while preserving layers.

10. Union and Intersect Operations

10.1 Union

• Overlays two polygon layers; combines attributes of both.

• Includes all polygons from inputs regardless of overlap.

10.2 Intersect

• Computes intersection of features, retaining overlapping portions only.

11. Model Builder

• Functions to automate the stringing together of geoprocessing tools.

11.1 Building a Model Example

• Steps to create neighborhoods from census tracts include:

  • Joining crosswalk tables, dissolving tracts, and managing joins to ensure reusability.

Summary

• Key areas of study include:

  • Attribute extraction

  • Feature location extraction

  • Location proximities

  • Geoprocessing tools

  • Model Builder for automating GIS tasks.