Best Practices For Intersecting Grid And Zone Shapefiles In Arcgis

Defining the Problem: Misaligned Geometries and Invalid Topologies

When working with spatial data in ArcGIS, a common task is to intersect two datasets – often a grid and zones – to perform spatial analysis. However, attempting to intersect geometries that do not properly align or contain invalid overlaps will result in errors or inadequate results.

Misaligned geometries occur when two datasets are not projected into the same coordinate reference system (CRS) or coordinate system. For example, attempting to intersect a grid in a geographic CRS with zones in a projected CRS will fail because the geometries do not line up spatially. This misalignment must be corrected before intersection can properly occur.

Invalid topologies result when geospatial features improperly overlap one another. For example, if two adjacent zones share a boundary but one zone extends slightly over the boundary into the other zone, an invalid overlap is present. Attempting intersection with invalid overlaps will also fail or create further topological issues in the output.

Therefore, successfully intersecting grid and zone geometries requires addressing any misalignments through proper projection and resolving any invalid overlaps or geometries through topology-cleaning methods beforehand. This article outlines best practices for preparing, intersecting, and assessing grid and zone data to achieve valid and optimal results.

Preparing the Datasets: Projecting to a Shared Coordinate System

When intersecting two spatial datasets, the cardinal rule is ensuring both datasets are projected into the same shared coordinate reference system (CRS). This allows the geometries to be properly aligned when overlaid and intersected.

Determine the most appropriate shared CRS for your analysis. Often an appropriate projected CRS is chosen that matches the region or country the data covers. Alternatively, a geographic CRS like WGS 1984 may be appropriate for global scale analysis.

Use the Project tool in ArcGIS to permanently project both the grid and zones to match the desired shared CRS. Confirm the projected CRS for each dataset matches in the feature class properties before proceeding.

Visually inspect the overlaid grids and zones after projection to ensure proper alignment with no gaps, overlaps, or misalignments along boundaries. Some manual editing may still be needed to achieve clean topology.

Choosing an appropriate shared CRS and then permanently projecting both datasets ensures geometries are aligned prior to intersection, avoiding topological errors and setting up the process for success.

Troubleshooting Overlap Issues: Identifying and Correcting Invalid Overlaps

Even after projecting to a share CRS, geospatial datasets often still contain invalid topological overlaps that can cause intersection errors. Such overlaps must be identified and corrected prior to intersecting grids and zones.

Use the Topology toolbar and tools in ArcGIS to build a topology and validate overlaps and gaps in the overlaid datasets. Any invalid overlaps found must be addressed for clean topology.

For zone overlaps, use edge-matching techniques like aligning boundaries or trimming overlap areas to eliminate the overlap. For grids, use the Reshape tool to align cell edges and vertices between overlapping grid cells.

In some cases, overlaps may be intentional or needed for the analysis. Use data management tools like Union to resolve overlaps by merging overlapping features instead. Document intended overlaps for clarity.

Visually inspect datasets after resolving topology issues to confirm all overlaps have been addressed properly with aligned boundaries. Clean topology with no errors allows grids and zones to cleanly intersect.

Performing the Intersect: Choosing the Right Tool and Settings

With the grids and zones properly prepared through projection and topology-cleaning, the datasets are ready for intersection. Choosing the right intersect tool and settings ensures accurate merged geometries.

The Intersect tool allows intersecting feature classes and integrating attributes from both inputs. If attribute transfer is not needed, the Clip tool can also intersect based solely on geometries.

Configure intersection settings like topological rules, output type, and attribute handling appropriately. Typically, boundary rules like “boundary of the intersect features” work best for grid/zone intersections.

Additionally, set processing extents that fully encompass both datasets’ original extents. Finally, document tool settings used to support reproducibility and updates.

Assess resulting geometries from intersection. Expect to see zone polygons subdivided along grid lines and grid cells clipped to zone boundaries with attributes appended from both sources.

Assessing and Improving Results: Evaluating and Enhancing the Intersected Output

Carefully evaluate the geometries resulting from the grid/zone intersection to determine if further improvements are needed for analysis and applications.

If geometric precision loss is evident, rebuild topology for the intersect dataset using automated and manual quality assurance tools. Simplify geometries if needed to reduce vertex density.

Correct invalid attributes using field calculations and edits if misaligned CRS or topology issues caused stray values. Reproject to the shared CRS if needed to correct lingering alignment issues.

Generate statistics on feature area, length, and perimeter relative to source grids/zones to measure and validate intersection quality. Address any major deviations through additional topology-cleaning.

Output assessment ensures the intersected geometries and attributes meet the needs of downstream spatial analysis workflows and applications relying on the integrated grid/zone dataset.

Automating the Workflow: Scripting the Intersect Process for Batch Processing

Applying the intersect workflow manually for a single grid and zone dataset provides quality results but can be time consuming. Automating through Python scripting dramatically accelerates batch intersect processing.

Script tool sequence using ArcPy to iteratively project dataset pairs to a common CRS, clean overlaps, perform intersect operations, and calculate quality statistics through input/output parameter sets.

Further optimize performance by integrating script tools into model builders to facilitate sharing workflows across the GIS organization and server automation for scalable processing.

Consider a database-centric architecture to utilize enterprise geodatabase topology and intersect capabilities for transactional integrity and job management. Output logs for troubleshooting and metrics.

Automation makes batch intersect processing, updating, and reprocessing feasible while increasing efficiency for analysts. Scripts allow stability and consistency across data products spanning countries, regions, or global scale.