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Category: Spatial

Using PostGIS in the Terminal

Published 2024-06-03 by Kevin Feasel

Dian M. Fay talks turkey about terminals:

Of late, I’ve been falling down a bunch of geospatial rabbit holes. One thing has remained true in each of them: it’s really hard to debug what you can’t see.

There are ways to visualize these. Some more-integrated SQL development environments like pgAdmin recognize and plot columns of geometry type. There’s also the option of standing up a webserver to render out raster and/or vector tiles with something like Leaflet. Unfortunately, I don’t love either solution. I like psql, vim, and the shell, and I don’t want to do some query testing here and copy others into and out of pgAdmin over and over; I’m actually using Leaflet and vector tiles already, but restarting the whole server just to start debugging a modified query is a bit much in feedback loop time.

Read on for Dian’s recommendations.

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Tips for Dealing with Large Spatial Datasets

Published 2024-04-05 by Kevin Feasel

Rhian Davies consults the map:

I love playing with spatial data. Perhaps because I enjoy exploring the outdoors, or because I spend hours playing Geoguessr, or maybe it’s just because maps are pretty but there’s nothing more fun than tinkering with location data.

However, reading in spatial data, especially large data sets can sometimes be a pain. Here are some simple things to consider when working in spatial data in R and breaking large data sets into more manageable chunks.

Click through for three tips when dealing with spatial data. The code is in R but the tips make sense in any language.

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Building Plausible Random Geographies with PostGIS

Published 2024-02-13 by Kevin Feasel

Dian M Fay has fun building a map:

Squaria is a continent of highly unstable geography defined by a single SQL query (with, as we’ll see, many, many CTEs). Its only consistent properties at the moment are its boxy shape and the two unnervingly straight mountain ranges that cross its breadth and meet on its lower eastern edge. Those mountains are impossible, but today’s topic is fluviation, that is, rivers and riverine lakes; we’ll see about plausible plate tectonics some other time, maybe.

The ever-shifting border of Squaria is defined by a Voronoi diagram within a 100-unit envelope, similar to Paul Ramsey’s random polygon generation. Other shapes are of course easily achievable, and I’m probably going to steal his circular envelope outright in the future, but squares are easy to demo.

Read on to see how Squaria got its geographic features.

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Spatial Data and GIS in DuckDB

Published 2023-04-13 by Kevin Feasel

Mark Litwintschik tries out a new extension to DuckDB:

DuckDB’s Spatial extension is made up of ~7K lines of C++ excluding its dependencies.

There are three major GIS projects this extension uses to provide its functionality. The first is GDAL, a 1.7M-line C++ project that provides file conversion functionality for both raster and vector-based GIS file formats. The second is PROJ, a 150K-line C++ project that provides coordinate transformations. The third is GEOS, a 150K-line C++ project that provides geometry manipulation functionality.

All of these dependency projects were started more than 20 years ago, have been extensively battle-tested and seem to underpin almost every GIS project and service I’ve come across.

In this post, I’ll walk through some example GIS workflows with the DuckDB Spatial extension.

Click through for a dive into what the extension can do.

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Managing Spatial Data in Azure

Published 2021-07-19 by Kevin Feasel

Rolf Tesmer takes us through the different Azure services which offer some ability to work with spatial data:

Every now and then you come across a use-case where you need to do something with spatial data, and you need to do it in the cloud (Azure, of course)! Up until that very point you maybe didn’t know, or perhaps even care, much about the intricacies of spatial data assets, let alone how the heck you were going to store it, process it, and query it, without making a mess of your current data stack.

Well, if you’re that person, then I say welcome to this blog post!

Click through for a fairly lengthy list, including Rolf’s comments on each. Also note the one big omission from the list as far as data platform products.

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Geospatial Analysis with Azure Data Explorer

Published 2020-11-04 by Kevin Feasel

Chris Webb continues along a theme:

Since last week’s blog post about dynamic M parameters generated so much interest, this week I thought I’d give you another example of something cool you can do with them when you’re using Azure Data Explorer (ADX) as a DirectQuery source in Power BI: geospatial analysis.

Let’s say you work for a chain of supermarkets and want to use Power BI see what other competing stores are close to one of your stores.

Read on for the rest of the story.

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Finding Distance Between Cities using SQL Server

Published 2020-07-31 by Kevin Feasel

Hasan Savran wants to find geographic distances with SQL Server:

I wrote about finding distance between two location in my older post. I have been getting question about how to make the search by using data in SQL Server. In this post, I will try to answer all these questions. You do not need to have latest version of SQL Server to do any of these examples. SQL Server has been supporting Geospatial data since 2008.
     First, we need some data. Not just some data, some free spatial data. I want to show you how to find distance between cities in this post, so I need at least names of the cities and their latitude and longitude. I downloaded this data from SimpleMaps website in CSV Format.

Read on for the solution.

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Polygon-Based Spatial Searches with Cosmos DB

Published 2020-05-08 by Kevin Feasel

Hasan Savran continues a series on spatial data in Cosmos DB:

I want to continue to develop our new map application for Azure Cosmos DB. So far, we can run a custom spatial query in Cosmos DB and display the results on a map. I want my users to create a polygon on map and search for data under this polygon. If you are familiar with Zillow, that is how their application lets you look for houses to buy or rent. You select an area, and Zillow application displays all available houses or rental under the area you draw. It is extremely useful and user-friendly search.

Click through to see how Hasan does it, as well as getting around a coordinate ordering problem.

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Displaying Cosmos DB Spatial Data with .NET Core

Published 2020-05-07 by Kevin Feasel

Hasan Savran builds up a quick .NET Core app to retrieve spatial data from Cosmos DB and display it:

Cosmos DB stores geospatial data in GeoJSON format. You can not tell what raw GeoJSON represents because usually all it has is a type and bunch of coordinates. Azure Cosmos DB does not have any UI to help you what GeoJSON data looks like on a map either. Only option you have is a third party tool which might display data on a map or Azure Cosmos DB Jupyter Notebooks.

    I want to run a query in Azure Cosmos DB and see the results on a map. I decided to create a simple UI which displays spatial data on a map. I will show you how to do this step by step. I will use LeafLetJs as a map. It is open source and free! Also, I need to create .NET Core 3.1 web application and use Azure Cosmos DB Emulator for data.

Hasan walks us through the demo and promises to put the code in GitHub later.

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