Strategies For Handling Large-Scale Geospatial Data Cleaning In Qgis

The Core Problem of Large-Scale Geospatial Data Cleaning

Cleaning large geospatial datasets in QGIS poses multiple challenges. As dataset sizes grow into the millions of features, processing times for geoprocessing tasks become unfeasibly long. Data quality issues like duplicate points, topological errors, and invalid geometries multiply at scale. And the complexity of data validation and correction workflows surpasses the capabilities of manual editing.

To tackle these problems, GIS analysts need robust strategies for assessing data quality, detecting errors algorithmically, handling duplicates, fixing topology, and optimizing QGIS. Combining targeted data cleaning scripts with configured caching and processing power can enable efficient analysis even with massive datasets.

Assessing Data Quality Issues

The first step in any geospatial data cleaning initiative should be a systematic assessment of data quality issues. This involves both quantitative analysis – looking at statistical summaries to identify outliers – and qualitative inspection to categorize the types of errors present.

Analyzing distributions of numeric attributes can uncover outliers – extreme values indicating corrupted sensor measurements or incorrect classifications. Plots like histograms and boxplots visualize the spread of fields like elevation, temperature, population. Groups of outliers suggest systemic data quality problems.

Visual inspection of samples can reveal artifacts like gaps in coverage, misaligned features, duplicate points, and topology errors. Categorizing these issues guides the choice of appropriate cleaning algorithms and parameters during correction.

Common Errors from Sensor Failures

Remote sensing platforms like satellites, planes, and drones are prone to sensor issues that get embedded in derived geospatial products. Satellite image rasters can contain streaks or blocks of aberrant pixel values from temporary sensor malfunctions. Aerial lidar surveys suffer from poorly calibrated intensities, causing bright and dark stripes in point clouds.

These sensor glitches manifest as clusters of outlier elevations or spectral values relative to their neighbors. They fail to conform to the spatial autocorrelation that naturally arises in geographic phenomena. Targeted filters and interpolation can automatically suppress these measurement errors while preserving overall trends.

Outlier Detection Strategies

Identifying outlier values is a prerequisite for handling erroneous measurements from sensor failures. Statistical approaches like Tukey’s method label points as outliers if they fall outside 1.5 times the interquartile range (IQR) above the third quartile or below the first quartile. While this works well for normal distributions, geospatial data often follows different clustered, skewed, or long-tailed patterns. More adaptive strategies are required.

One approach is to analyze the spatial distribution in addition to the attribute values. Local Moran’s I metrics detect outliers by measuring the correlation of a feature’s attributes with its neighbors. High positive correlations indicate clustered points. High negative correlations flag potential outliers.

Machine learning offers additional ways to profile expected distributions and identify anomalies. Isolation Forest algorithms model random splits of normal data, flagging points requiring few splits as outliers. Local Outlier Factor analysis scores outliers based on local density comparisons.

Filtering Techniques to Remove Noise

Sensor errors and other data collection artifacts often result in noise – spurious values inconsistent with surrounding measurements. Examples include lone outlier pixels in remote sensing rasters, Z-value spikes in point cloud data, jumping POIs in GPS traces.

Filtering techniques like median filters, low-pass filters and spline smoothing can suppress noise and outliers. These operate by replacing values with aggregated statistics from the neighboring points or pixels within defined windows. Appropriate window sizes are critical – excessively wide windows over-smooth important transitions.

Interpolation such as inverse distance weighting (IDW) uses weighted combinations of values at nearby measured locations to predict values at unmeasured spots, filling in gaps and smoothing noise. Kriging optimization considers spatial autocorrelation in the weighting model. Compare filtered and interpolated results to avoid over-smoothing.

Handling Duplicate Features

Duplicate features commonly arise in geospatial datasets sourced from multiple providers, derived from error-prone collection processes, or combined across batches using different methodologies. Duplicate points, lines and polygons waste storage, slow processing, and can confound analysis.

Eliminating exact duplicates based on identical geometries and attributes is straightforward but pipelines often produce near-duplicates with slight inconsistencies. More advanced deduplication algorithms employ statistical learning and geographic heuristics to identify probable duplicates among close candidates.

Deduplication Algorithms

Exact duplicate detection simply matches features where all attributes and coordinate pairs perfectly agree. To allow tolerance for numerical rounding errors, near-duplicates may also be flagged when geometric distances and attribute differences fall below set thresholds.

Machine learning deduplication tools like Google’s DataSet and Facebook’s Pytorch-BigGraph learn blocking rules to selectively compare pairs likely to duplicate based on partial key matches. Supervised classifiers can then integrate string similarities, spatial proximity, temporal alignment and attribute differences to predict which pairs to merge or suppress.

Merge Rules for Overlapping Polygons

Layers containing overlapping polygon boundaries often benefit from merging into a set of non-overlapping polygons. This eliminates redundant areas and topological invalidities. Appropriate merge rules should be designed to preserve significant boundaries and attributes.

A common automated approach dissolves all boundaries between areas sharing the same class or category attributes. This technique should be applied cautiously when geographic distinctions matter – county subdivisions, sales territories, etc. Topological relationships and containment hierarchies can inform selective merge prioritization.

Interactive region merging tools allow GIS users to manually select which areas to merge or split while reviewing attribute tradeoffs. Operation history logging helps reproduce merge rationales.

Strategies for Handling Topological Inconsistencies

Geospatial data representation relies on topological relationships encoding expected spatial interactions between points, lines and polygons. Examples include lines properly connecting at intersections, points snapped to lines and boundaries, polygons correctly contained within each other.

Real-world GIS data often violates these rules due to conflicting measurements, processing artifacts, misalignments, and gaps in coverage. Fixing topology ensures correct geospatial queries, overlay operations, routing, and mapping output.

Identifying and Fixing Geometries with Invalid Topology

The first step in resolving topology problems is validation – algorithmically detecting geometries violating topological rules. All major GIS platforms provide validation tools flagging self-intersections, unclosed rings, undershoots/overshoots, and proximity threshold violations.

Next, programs like QGIS’s Topology Checker guide interactive fixing of invalid geometries. Automated repair tools can also snap points to lines, split crossing lines, reshape self-intersecting rings, fix undershoots, and force overlaps to meet minimum distances.

Batch topology correction scripts iterate validation and automated repair until achieving a clean topology. Test different buffering thresholds and snapping tolerances to balance performance and quality.

Tools to Validate and Correct Topological Relationships

In addition to geometry errors, data often suffers from missing topological relationships – for example roads not properly connected at intersections, buildings not lining up with parcel boundaries. This limits routing capability and muddles containment semantics.

Custom topological relationship builders leverage spatial queries to reconstruct expected Point-Line, Line-Line and Polygon-Polygon interactions. Look for gaps between features within small buffers, then apply snapping, extending, splitting, or aggregation to restore valid connections.

Review the resulting data both visually and by exporting topological relationship tables. Analyze for missing connections signaling additional data quality issues needing rectification through merging and conflation.

Optimizing QGIS for Large Datasets

Despite ongoing performance improvements, QGIS still struggles with large datasets in the millions of features. But strategic configuration and scripting can achieve dramatic speedups through maximizing cache usage, minimizing I/O, and balancing memory vs CPU loads.

Increasing Processing Power for Faster Geoprocessing

The single most impactful optimization for QGIS is increasing available processing power. While the software can utilize multiple CPU cores itself, further gains come from distributing work across more systems. Save processing time by offloading intensive geoprocessing onto added worker machines, dedicated GPUs, container clusters, or cloud computing resources.

Consider tools like GeoMesa, GeoTrellis, GeoWave, Elastic for deploying high performance geospatial processing and querying capabilities across distributed systems. Integrate via plugins, custom Python scripts or external processes running in parallel.

Configuring Data Caching for Responsiveness

With large datasets, long wait times when panning, zooming and querying layers often arise from disk I/O. Enabling caching to keep frequently accessed data in memory can dramatically boost responsiveness.

For vector data, set up spatialite or geopackage layers with QGIS project config options to load features, geometries and attributes into cache. This avoids per-request filtering queries and decompression.

For rasters, use virtual rasters with on-disk caching in formats like MapServer’s VRT. This mosaics rasters dynamically into a single virtual layer optimized for accelerated readout. Fine tune cache sizes based on available RAM.

Example Scripts for Automated Data Cleaning

Manually cleaning individual geometries and attributes does not scale for massive geospatial datasets with millions of features. Automating correction workflows via Python scripting provides an efficient solution by programmatically assessing and fixing batches of data.

Script for Batch Removal of Duplicate Features

This script connects to a PostGIS database, analyzes a vector layer for likely duplicate features, prompts the user to select duplicates to remove, deletes those features from the layer, and writes the results back to the database.

It employs a custom deduplication function utilizing spatial clustering, temporal alignment and attribute similarity heuristics to score candidate duplicates without expensive brute force comparison of all pairs. An interactive module displays potential duplicates for manual review.

Python Code to Find and Fix Invalid Geometries

This script iterates through a dataset correcting geometric invalidities that violate topological consistency. It includes a validate_geometry function that flags issues like self-intersections in polygons. An auto_repair function then snaps vertices to eliminate undershoots, reshapes rings to fix unwanted self-intersections, and separates features with irreparable geometry problems.

By wrapping validation and auto-repair in a loop, the script repeats correction attempts until achieving a clean topology. Customizable snapping tolerances balance performance with quality. Invalid geometries needing manual rebuilding are exported for later review.