SQL – geoMusings

BigQuery and Koop

2021-03-22 by Bill Dollins

As I continued my experimentation with BigQuery, I found myself wanting to more easily use it with my regular GIS tool set. BigQuery has a lot of powerful analytic capability, but the SQL console is intimidating for the casual user and the GeoViewer tool is fairly limited. As I began digging deeper in my previous … Read more

Routing with BigQuery and ArcGIS Platform APIs

2021-03-11 by Bill Dollins

This post is a continuation of last month’s post about analyzing location change with BigQuery. At the end of that post, I was already thinking of ways to extend the analysis and visualization. I decided to take the opportunity to explore Esri’s recently-announced ArcGIS Platform APIs. These APIs are the same that have been available via an AGOL subscription or an ELA, but they are now presented in a consumption-based model, similar to Google or Mapbox APIs, that allow you to make use of them without having to make a larger up-front commitment to the rest of the ArcGIS stack. Esri’s basemaps and their location services have always been high-quality, so it’s nice to see them available under a more accessible model.

Decided to use the Esri routing API to visualize possible routes between the various locations of the “Sample Asset” from my last post. I chose to build a very simple Node API to access the BigQuery data and use that API from a simple page that calls the Esri API and displays the output on a Leaflet map. The first thing I needed to do was add a little extra SQL in BigQuery to return coordinates in a format consumable by the Esri API. The raw API expects coordinates delimited as such:

-76.519989228,38.303696474000006;-76.695443826137989,38.376038894414251;-76.635015354340467,38.29745667728772;-76.519989228,38.303696474000006;-76.695443826137989,38.376038894414251;-76.635015354340467,38.29745667728772;-76.519989228,38.303696474000006;-76.495959193,38.236694621

Analyzing Location Change Over Time in PostGIS

2021-05-052021-02-27 by Bill Dollins

Following up on my previous post, I decided to attempt the same analysis in PostgreSQL. The analysis doesn’t make use of any spatial logic itself (yet), but I consider this a PostGIS post because it is using PostGIS geometries.

A simple FME workspace to move my data to PostGIS.

In the past, I have noticed that BigQuery SQL is very reminiscent of that of PostgreSQL, which has helped me ramp my productivity with BigQuery. In the case of the LAG function as used here, that reminiscence is an exact copy. So, again, PostGIS comes through.

Attribute Transfer in PostGIS Using Spatial SQL

2020-04-062020-04-06 by Bill Dollins

Data conflation is a meat-and-potatoes task in most GIS workflows. There are numerous reasons one might need to get data from one data set into another. Perhaps you want to attach a new geometry type to existing attributes or a table. Or maybe you need to pull attributes from one or more data sets into a single, “master” version. I have seen this latter use case referred to as “attribute transfer.” In an interactive, desktop setting, this can be tedious, but it’s a task at which spatial SQL excels.

Here is a simple example that uses just one line of spatial SQL (or two lines if you need to add the column) to do the heavy lifting. First, some table setting. This example takes the number of confirmed COVID-19 cases from the Johns Hopkins university county-level data (a point data set) and transfers it to a polygon data set of the US counties. There’s one caveat at the end of this post.

Working with Materialized Views in PostGIS

2017-06-06 by Bill Dollins

It’s been a few months since I’ve posted, owing mainly to getting my feet under me at Spatial Networks. About a month after I started, the company re-merged with Fulcrum, which had previously been spun off as a separate company. As a result, I’ve gotten to know the Fulcrum engineering team and have gotten to peer under the hood of the product.

Of course, Spatial Networks is also a data company. What had originally attracted me was the opportunity to help streamline the delivery of their data products, and this remains a pressing issue. This has kept me elbow-deep in PostGIS, and has led me to delve into using materialized views more than I have before.

What is a materialized view? If you are familiar with relational databases, then you are familiar with views, which are saved queries that are stored in the database. Similar to tables, you can select data from a view; but, rather than directly selecting physically stored data, you are executing the SQL that defines the view, which will stitch together data at the time of execution.

Triggered Notifications Using PostGIS

2011-08-01 by Bill Dollins

My project work the last few months has kept me away from a lot of my favorite open-source tools and I was starting to get hives. Specifically, it had been a while since I had worked with PostgreSQL and PostGIS and I was missing the experience, so I dreamed up something to do.

I do a lot of work implementing situational awareness systems for my customers and one common requirement is automated notification of events. I decided that I wanted to roll a completely FOSS approach to sending an SMS notification based upon the results of a spatial query. This post will discuss the basic wiring to make it all work. I’ll probably add more advanced features in subsequent posts but I’ll be sticking to the basics for now.