Analyzing data in ArcGIS involves leveraging a comprehensive suite of geospatial tools to derive meaningful insights, patterns, and relationships from geographic information. It transforms raw data into actionable intelligence, enabling better decision-making across various fields.
Getting Started with Data Analysis in ArcGIS
Before diving into the analytical processes, ensure you have the necessary environment set up and permissions.
Essential Prerequisites
- Organizational Access: Confirm that you are signed in to your ArcGIS organization account. This ensures access to your organization's content, analysis tools, and shared resources.
- Required Privileges: Verify that your account has the necessary privileges to perform analysis. These often include permissions for geoprocessing, content creation, and sometimes specific administrative rights depending on the complexity of the analysis. If you encounter permission errors, contact your ArcGIS administrator.
Core Analysis Workflow in Map Viewer
The primary interface for performing analysis in ArcGIS Online (Map Viewer) offers an intuitive way to access powerful spatial tools.
- Open or Add Data: In Map Viewer, open the map containing the layers you want to analyze or add the layers directly from your content, an organization, or public sources.
- Access Analysis Tools: On the Settings (light) toolbar, locate and click the Analysis button (often represented by an icon resembling a magnifying glass or a statistical chart).
- Choose Your Analysis: From the Analysis pane that appears, you will have various options to select the specific analysis tool or category you need. These tools are categorized to help you find the right function for your data.
Types of Spatial Data Analysis in ArcGIS
ArcGIS offers a wide array of tools categorized by the type of question they answer. Understanding these categories helps in choosing the most appropriate method for your data.
1. Summarize Data
These tools aggregate information about features or their attributes, helping to understand distributions, counts, and statistics.
- Aggregate Points: Summarizes points within polygons or bins, calculating statistics for those points.
- Example: Counting the number of customers within each sales territory.
- Summarize Within: Calculates statistics (count, sum, mean, min, max, std dev) for attributes of features that fall inside areas of another layer.
- Example: Calculating the total population within identified flood zones.
- Summarize Attributes: Calculates statistics for fields in a layer.
- Example: Finding the average income across all census tracts.
2. Find Locations
These tools identify locations that meet a set of criteria you define, often based on proximity or attribute values.
- Find Existing Locations: Selects features in an input layer that satisfy a specific query or attribute expression.
- Example: Identifying all vacant parcels from a property layer.
- Derive New Locations: Creates new features from existing ones based on a set of criteria.
- Example: Finding ideal locations for a new store based on proximity to customers and distance from competitors.
3. Analyze Patterns
Pattern analysis tools help in understanding spatial distributions, clustering, and trends in your data.
- Find Hot Spots: Identifies statistically significant spatial clusters of high values (hot spots) or low values (cold spots).
- Example: Pinpointing areas with a high concentration of crime incidents.
- Find Clusters: Locates clusters of point features within noise.
- Example: Identifying areas with a higher-than-expected number of disease outbreaks.
4. Use Proximity
Proximity tools measure and analyze distances between features, essential for site selection, impact assessment, and resource allocation.
- Buffer: Creates areas at a specified distance around features.
- Example: Delineating a 500-meter buffer around a proposed highway to assess noise impact.
- Create Drive-Time Areas: Generates areas reachable within a specified travel time or distance.
- Example: Determining which areas are within a 15-minute drive of emergency services.
- Find Nearest: Identifies the closest features in one layer to features in another layer.
- Example: Finding the nearest fire station to each residential property.
5. Manage Data
These tools are crucial for preparing, cleaning, and transforming your data for analysis or visualization.
- Join Features: Appends attributes from one layer to another based on spatial, temporal, or attribute relationships.
- Example: Joining demographic data to administrative boundary polygons.
- Dissolve Boundaries: Merges adjacent polygons or lines that have the same attribute value into a single feature.
- Example: Combining several adjacent land parcels owned by the same entity.
- Extract Data: Copies features or records from a larger dataset into a new layer or file.
- Example: Exporting a subset of properties from a regional database for a specific project area.
Common Analysis Tool Categories and Examples
| Analysis Category | Description | Example Use Case | Key Tools |
|---|---|---|---|
| Proximity | Measures and analyzes distances between features. | Identifying service areas for facilities or impact zones. | Buffer, Create Drive-Time Areas, Find Nearest |
| Overlay | Combines features from multiple layers based on their spatial intersection. | Assessing land suitability by combining soil type, elevation, and zoning data. | Intersect, Union, Erase, Clip (often found in desktop versions or specific "Combine Layers" tools in online) |
| Pattern | Identifies spatial clusters, outliers, and trends. | Locating crime hotspots or areas with high incidence of disease. | Find Hot Spots, Find Clusters |
| Summarization | Aggregates data to understand distributions and statistics. | Counting trees in different park zones or average housing prices per district. | Aggregate Points, Summarize Within, Summarize Attributes |
| Location | Identifies or derives features based on specified criteria. | Finding suitable sites for new infrastructure or selecting specific data subsets. | Find Existing Locations, Derive New Locations |
Practical Insights and Solutions
- Iterative Process: Data analysis is often an iterative process. You might perform an initial analysis, review the results, adjust parameters, and run it again.
- Understanding Data Types: Be aware of your data types (point, line, polygon) as different tools apply to different geometries.
- Credit Consumption: In ArcGIS Online, many analysis tools consume "credits." Be mindful of this, especially for complex or large-scale analyses. You can often estimate credit usage before running a tool.
- Desktop vs. Online: While ArcGIS Online's Map Viewer provides robust analysis capabilities, desktop applications like ArcGIS Pro offer a more extensive and powerful set of geoprocessing tools, often used for more complex or custom workflows, including scripting with Python and ArcPy.
- Document Your Process: Keep a record of the analysis steps, tools used, and parameters. This is vital for reproducibility and collaboration.
Interpreting Results
Once an analysis tool completes, the output is typically a new layer added to your map. It's crucial to:
- Visualize the Output: Explore the new layer spatially to see the patterns or results.
- Examine Attributes: Check the attribute table of the output layer for new fields containing summary statistics, counts, or other derived values.
- Validate: Compare the results against your initial assumptions or known information to ensure they make logical sense.
By systematically applying these analysis techniques, ArcGIS empowers users to extract valuable geographic intelligence, facilitating informed decisions across diverse applications from urban planning to environmental management.
