This is a review and expansion of the Global Intelligence Report from Citrini Research which lays out a scenario for what happens when AI genuinely replaces white collar work at scale. It also covers my expectations for the future based on the current trends around the market. I’ve been a software engineer long enough to know that the question isn’t whether AI changes my industry; it’s how fast. This post is me thinking out loud about that.
Hey folks. I moved from Wellington to Switzerland a few years ago now and a big part of the move was an application called Pix4D Mapper which I really liked. It was the beginning of the drone revolution, before all the modern warfare stuff and it was just a really nice time to like flying, geospatial and maps.
I find myself wrapping up a QGIS plugin project that started as a pile of loose Python scripts. Its been roughly two years and the project is now closing out (for me at least) so I wanted to write about some of the interesting complexities that came with it. QGIS has something like 200,000 weekly active users; its arguably the most important open-source geospatial application ever built. I didn’t set out to build a plugin for it; I just needed to stop forgetting which script to run next.
The age of AI has changed how we plan as much as how we write. Prior to AI we already had a problem with people setting bad goals. With AI, those same people have an increased capacity to act on them. I once heard someone describe intelligence as analogous to the engine of a car. A 100 IQ engine will arrive more slowly to its destination than a 140 IQ engine, but with enough grit it will still arrive. The real question is the destination. I’m old enough now to have seen quite a few 140 IQ engines drive very fast in the wrong direction.
I find myself, a geographer, running the change management processes in a Tech company. While it seems like a .yaml chart code problem to people with a CS degree; moving containers in cloud infra seem more too me like a transportation network problem.
Defining delivery requirements in the Geospatial domain; and my favourite bug discovery. Communication protocols enable systems to communicate information; these definitions are API’s; Application Programming Interface.
This is a QA log, kind of like a wanna be dev log. A bit less technical and fueled by butterflies and hope. At the HxDR team we’ve been digital twinning for a while. That is the process of combining multi-scale capture data to make a clone of reality.
I was lucky enought ot attend Geopython this year; the brain child of Martin Christen’s, a professor at fhnw. Within the Geospatial domain Python is the goto language but its an increasingly popular language too.
SQL is a standard language for interacting with DB’s and I don’t think we discuss it enough in the Spatial community. It’s a de facto language for interacting with DB’s which is platform agnostic. There are some great courses which I’ve dipped in and out of but the best is to just use the tools to do something useful.
One of the most popular, or even the most popular formats of 3d point cloud data is LAS. I’ve written about it before here, but in this example let’s take it further, let’s write some data. I’ve got some data with questionable srs info to start and I’m trying to work out exactly the issue.
This year’s HackZurich challenge was to take information about a train shunting station in which locomotives and wagons come together on a network and create some kind of routing solution.
Regulations almost always lag behind technological innovations, and that is certainly true in the Drone space. Finally enough airports have been closed in Europe to make it necessary for the EU, Iceland, Norway, Switzerland and the UK to make some drone licensing rules.
In order to create and maintain any kind of system you need to be able to perform operations on your data. GraphQL solves some very specific problems to do with how you perform CRUD operations. However it is pretty important to understand some of the limitations with restful routing before graphQL makes any sense at all.
You can think of Bitcoin as both a currency and a bank which exists on the web and was invented / discovered by Satoshi Nakamoto in 2009. Since then governments have come to grips with the idea of a digital currency and it seems that with the collection of taxes and official regulation, crypto and particularly bitcoin (BTC) are here to stay at least for the moment.
GDAL or the Geospatial Data Abstraction Library is awesome. Its one of the foundations of geospatial software and mastering it can really help with daily work processes.
Originally this thing was two separate libraries, GDAL for Raster Data manipulation, ORG for vector manipulation. This separation between the two libraries still exists but the two are now bundled together.
I’m currently working on a cloud product which is utilizing the AWS cloud; so it was a great time to study for and take the AWS Cloud Practitioner Exam. Here are some of the details on how I prepared for the exam; but none of the details from the actual exam, just logistics.
This was a fun little voluntary project organized by Danield Huffman from somethingaboutmaps.com to produce a series of project cards.I thought it would be a nice opportunity to share some of my working notes to see if my process makes sense and just to put it out there for others to use.
Coordinates without a CRS are meaningless numbers. So if we consider a CRS or SRS as a tech stack it has been in development for some time; and its starts with
Shapefiles are an old file format, originally developed by ESRI, which have become a common way of working with Geospatial data; much to the chagrin of ESRI who have ever since been trying to migrate to a Geodatabase format.
We have a highly detailed geojson file containing multipart polygons which represent the coverage of our data from the content program.This data needs to be fast to load but it is 320MB, all text. This is largely caused by each vertex in the polygon having a lot of xyz data. In cartographic applications we often run feature simplification to reduce the size of such data. Below in the blue line is what we have and the red line is what we want.
Fundamentally point cloud data is just a lot of points needing to be represented in real or cartesian space. As a colleague recently pointed out to me, computer science is just moving information from one place to another. So the format in which we move data can become very significant and really understanding the schema can be enormously valuable.
Let’s look at some imagery collected from DJI’s P4M setup released last year. It’s being sold as a full agricultural solution. The Phantom 4 Multispectral (P4M) is shipping with one RGB camera and five narrow band sensors, including red edge and near infrared.
We really don’t want to break things that work, it’s super frustrating. We test to make sure that any changes we make do not break another piece of the code in our applications. Of course manual or exploratory tests are important but the dream is to get this stuff fully automated. (more…)
Calculating a collection route to optimally cover an area leaves you with two problems. Communication to the operators who need to be able to drive the route. Communication from the drivers who need to make changes.
I have a problem collecting rubbish and recycling saturation data. I’ve tried developing on existing ESRI apps to collect data and store it in the cloud. Its failed every time. These approaches failed because no one likes doing data entry on a phone or tablet, the guys who drive the truck. A clipboard is still more reliable than a smartphone but that cannot collect the street or time stamp. But who doesn’t like hitting an old arcade button right? I’m trying to develop a data collection method that’s manual but digital, easy and cheap.(more…)
Using GPS points to map out a city we get a new dimension to our maps. Using the increasingly ubiquitous GPS data available it occurs to me we have a new way to cartographically visualize infrastructure. With the combination of millions of GPS points we can build up an great view of the city.(more…)
Moving people from a cheap option to a more environmentally friendly one is tough in a commercial setting. Of the target list we managed to convert 36% of the companies with some interest in Recycling. (more…)
A cities public transport network relies on having sheltered dry places to wait for the next bus; especially in a student city. Damage to this infrastructure affects the whole service. (more…)
City water networks are inspected regularly. CCTV footage helps confirm replacement scheduling. These are algorithmically generated, and are not always that reliable. Lets make some information visible.
Pokie machines… a questionable social vice or good fun. Regardless of your point of view, they are certainly profitable. Palmerston North City and Ashhurst spent $4,623,896.06 on them in 2017. That’s $57.70 per person in a city of 80,000. (more…)
When mapping at a local scale we’re usually trying to show some sort of targeted data. This makes context important, but not so much that it takes away from the data your trying to show. (more…)
Looking over the invoices for council I found $##redacted (large sum of money, larger than I was ultimately aloud to publish on the infographic or my blog )being spent on illegal dumping every year. That is an impressive sum of money for a small city. So I’m producing a map to show the annual cost and distribution of illegal dumping in the City; while telling the story of how long it take the council to pick the trash up. The data has been collected using Survey123 and Workforce and visualized in ArcGIS pro. (more…)
A lot of manual work can be avoided by using scripts to automate the processes. However, you inevitably end up with a lot of scripts to automate different pieces of a process. (more…)
Maps have an amazing ability to focus a discussion. As a discussions becomes bigger less people can see the map and the conversation breaks down. This is actually a really big problem. (more…)
Since Roman times city planning has included the provisioning of Water. Moving forwards a couple of thousand years has taken us away from the obvious aqueduct to an immensely complex underground network of pipe’s servicing the 3 water system; Wastewater, Storm water and drinking Water.
We often find ourselves collecting and maintaining data. For GIS applications we use this data to answer questions about the world around us. Quite often the sheer quantity of data is overwhelming and the more we collect the harder it is to give a simple answer.
This project is going to use Hexagon bins for the Motutapu Restoration project data. If it’s successful I will be able to report on disparate data sources by binning it into a hexagon grid across the island. This will allow me to report temporally for the islands data records using one feature service, a hexagon grid.
In the immediate aftermath of the Kaikoura earthquake a well practiced response went into effect. Within a few days information about the event increased exponentially as the various agencies began reporting. The tide of information was overwhelming. Organisation such as Environment Canterbury began requesting GIS support to help make the relevant information visible to people who could use it. I was lucky enough to be selected to go down and help out at the Environment Canterbury offices. Here is the process as I experienced it. (more…)
Richard Buckminster Fuller was an american inventor credited with, among other things, the Dymaxion Mapping Projection or sometimes known as the Fuller projection. It was designed to represent the earth as an island, and to emphasis humanitarian efforts across the globe, or as Buckminster Fuller would have said, “on Spaceship Earth”. I am ultimately writing about ‘Bucky’, as he became known, due to his contributions to the mapping community with the Fuller projection. The backstory is sad yet inspiring. (more…)
The foundation of government is land, around which is wrapped the economy, legal system and elections. Land in New Zealand has a legal title, an address and a set area recorded by a surveyor known as a parcel. I believe there is some confusion here and it’s important to be clear when so much depends on land. Parcels and Legal Titles do not have addresses.(more…)
Let’s talk about giving an address X Y coordinates. If you need to geocode something it’s probably going to be in a .csv .xlxs or .xls format. This post will talk you through adding a location and putting it on a map.
GeoJSON data is available from ramm.com about our cities bridges. To make it available for our engineers it needs to loaded to a local database. There are lots of metrics stored there too which can change daily, so we need to do this every night. Let’s automate it with python!
As a GIS professional your workflow very likely includes the acquisition of data to solve problems. In this post I’m going to be laying out a guide to Satellite geospatial data. Hopefully this will make finding data you don’t work with everyday a little easier.
This is an introductory tutorial for making facility maps using GIS. I’ll be working with ArcGIS Pro, the recipe applies equally well for QGIS or ArcMap. (more…)
Workforce takes operations from ad hoc paper based system or a disconnected collection of GIS features to a systematic flexible and programmable to do list. At its heart there is a focus on keeping work loads simple. That means seeing only the information you need to perform the tasks relevant to you. (more…)
3 Step Guide to Publishing Hard Copy Cartography in ArcGIS Pro
Like almost every other GIS professional I’ve been producing hardcopy maps using the trusty workhorse of GIS, ArcMap for quite a while now; with the occasional foray on it’s slightly more hipster cousin QGIS. Over the last couple of years ESRI have been developing their cloud GIS and a new Desktop App called ArcGIS Pro.(more…)
Since Roman times city planning has included the provisioning of Water. Moving forwards a couple of thousand years has taken us away from the obvious aqueduct to an immensely complex underground network of pipe’s servicing the 3 water system; Wastewater, Storm water and drinking Water.
Anything Mapping 