What are the best ways to use spatial queries for data analysis?

1 Types of spatial queries

There are two main types of spatial queries: attribute-based and location-based. Attribute-based queries use the non-spatial attributes of the data, such as name, category, or value, to select or filter the features. For example, you can use an attribute-based query to find all the cities with a population greater than 10,000. Location-based queries use the spatial attributes of the data, such as coordinates, shape, or area, to select or filter the features based on their spatial relationships. For example, you can use a location-based query to find all the rivers that intersect with a national park.

2 Benefits of spatial queries

Spatial queries can be very useful in GIS for exploring and visualizing data, finding patterns and trends, solving problems, and creating or modifying existing data. For example, you can highlight or isolate the features that meet your criteria, compare or correlate the spatial and non-spatial attributes of the features, apply spatial logic and reasoning to your data, and perform spatial operations or calculations on the features.

3 Spatial query operators

To perform spatial queries, you need to use spatial query operators, which are keywords or symbols that define the spatial relationship between the features. Intersects returns the features that share any part of their geometry with another feature, while Contains returns the features that completely enclose another feature within their geometry. Within returns the features that are completely enclosed by another feature within its geometry and Touches returns the features that touch another feature at a point or along a line, but do not overlap or cross it. Near returns the features that are within a specified distance from another feature and Disjoint returns the features that do not share any part of their geometry with another feature.

4 Spatial query examples

To illustrate how to use spatial queries for data analysis, let's look at some examples using different types of data and scenarios. For instance, you can use a spatial join to determine the number of schools in each district, or a spatial selection to identify which roads have the highest and lowest risk of accidents. Additionally, you can use a zonal statistics to calculate the average elevation of each watershed and understand its effects on water flow and quality. Each type of query has its own operator; for instance, the contains operator for a spatial join, the intersects operator for a spatial selection, and the watersheds as zones for a zonal statistics. After running each query, you can use an attribute-based query to summarize and compare the results.

5 Spatial query tips

Spatial queries are an incredibly useful and versatile feature of GIS, as they allow you to analyze your data from a spatial perspective. To use them effectively and efficiently, make sure to choose the right type and operator of spatial query for your data and objective. Additionally, check the spatial reference and projection of your data, as well as optimize the performance and speed of your spatial queries by using techniques such as indexing, simplifying, or clipping your data. With spatial queries, you can uncover new insights, answer complex questions, and create meaningful maps and reports. What are some of the spatial queries that you use or want to learn in GIS? Share your thoughts and experiences below!

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?