Vector Data
When exporting geographic features from one data format or geodatabase to another, a common frustration is the unintentional loss or truncation of attribute data associated with those features. Detailed tabular information containing measurements, descriptions, categorizations, or other metadata may suddenly find columns missing or values blanked out upon import to a new geospatial dataset. This…
Loading Vector Datasets To sum point attributes by polygon in Python, we first need to load vector datasets into GeoPandas data frames. The main vector data types used in geospatial analysis are points, lines, and polygons. Points represent discrete locations, lines represent linear features, and polygons represent enclosed areas. We will load a point layer…
The Core Difference Between the Two Tools Geometry Generator and Geometry by Expression are two powerful spatial analysis tools in ArcGIS Pro. They enable users to create and manipulate geometries dynamically. The core difference lies in their complexity and performance. Geometry Generator offers a visual, non-coding way to create basic geometries from existing features. It…
Finding the Right CRS for Accurate Visualization Coordinate reference systems (CRS) are essential for accurately displaying the locations of vector data layers in QGIS. A CRS defines the coordinate system and projection used to represent geographic data, relating map coordinates to real-world locations. Determining the correct CRS for your vector data is crucial before visualizing…
Identifying Overlay Error Causes Overlay errors during geographic information system (GIS) analysis often stem from fundamental mismatches between the coordinate reference systems, feature geometries, and processing environments of the data layers being integrated. Identifying the root causes of overlay errors is essential for preventing gaps between polygons, inaccurate overlays, and other spatial analysis problems. Misaligned…
Defining Uncertainty in Spatial Data Uncertainty in spatial data refers to the lack of complete knowledge regarding the precise locations and attributes of geographic features. This uncertainty stems from the inherent variability and errors associated with measuring and depicting real-world phenomena. Defining and quantifying uncertainty is essential for properly interpreting spatial datasets and analyses. Key…
What are Sliver Polygons and Why Do They Matter? Sliver polygons, also known as sliver shapes or gaps, refer to small polygon features in geospatial datasets that are often unwanted artifacts of geoprocessing operations. They are typically long and narrow slivers of space between larger, more meaningful polygon features. Common causes of sliver polygons include…
Identifying Polygon Gaps Finding small sliver polygons between adjacent polygons is the first step in identifying gaps in polygon continuity. Configuring topology rules to flag sliver polygons under a minimum area threshold is an automated way to highlight potential gaps. Visual inspection of polygons at different map scales also reveals larger gaps not caught by…
Overview of Machine Learning for Spatial Analysis Geographic Information Systems (GIS) provide powerful capabilities for mapping and analyzing spatial data. Integrating machine learning algorithms with GIS unlocks additional techniques for spatial modeling, prediction, classification, and optimization. Key machine learning tasks that augment GIS include predictive modeling of spatial phenomena, image classification for land use/land cover…
Assessing Dataset Size and Complexity The first step in optimizing centreline extraction is to thoroughly assess the size and complexity of the polygon dataset. This includes determining the total number of polygon features, the spatial extent and bounds, the number of vertices per feature, and any attributes associated with the polygons. For large datasets, statistical…