Meta is known for Facebook, WhatsApp and Instagram.

But did you know they provide a range of free geospatial datasets for researchers?

These include granular measures of household wealth, population, and network access.

Here's what you need to know about it: 1. Meta provides granular estimates of household wealth for low and middle income countries.

Read more about it here:

https://x.com/yohaniddawela/status/1786359573139329406

It can be a nightmare to find official sub-national shapefiles.

Luckily, there are a number of sources that make the job easier: 1. GADM

The most widely used source of global shapefiles is the Database of Global Administrative Areas (GADM).

For countries like the UK, it provides boundaries down to the admin 4 level.

Link: gadm.org

One of the biggest traps in geospatial analysis?

Ecological Fallacy.

It can turn a map into a misleading story.

Here's what you need to know about it: In simple terms, ecological fallacy is drawing conclusions about individuals from data that were aggregated over areas (e.g. counties, districts, grids).

What happens when a scientist pays $300 for 50 citations?

A study reveals a growing black market of citation mills, and why it threatens the credibility of science. To test the Google Scholar system, researchers created a fake academic and paid $300 to buy 50 citations.

Within 40 days, citations started appearing in Google Scholar, published in journals with impact factors as high as 4.79.

We track poverty too slowly to respond effectively.

But a new open-source tool now lets us map wealth in near real-time using satellite imagery.

Here’s what you need to know: Back in 2016, Stanford's AI Lab pioneered a new method of measuring economic activity using daytime satellite images.

If your regression results look great but perform poorly in some areas, there could be a cause:

Spatial nonstationarity.

It’s one of the most overlooked issues in spatial analysis.

Here’s what it is and how to detect it: Imagine you’re studying how income affects house prices in a city.

You run a regression and get a single coefficient.

E.g., every $1,000 increase in income raises house prices by $5,000.

But is that true everywhere in the city?

Satellite data is being used to detect marine litter from space.

Here's everything you need to know about it: A new paper in @NatureComms describes a novel way of detecting marine litter using satellite images.

They use data from Sentinel-2 to detect ‘litter windrows’.

Geospatial data science is all about having access to good data.

Here are a list of my favourite free geospatial resources: 1. Aiddata

Aiddata has an excellent list of geospatial datasets.

It's all available as a CSV.

So you don't even need to know GIS to access their data.

link: geo.aiddata.org

Air pollution is a massive problem.

However, it's incredibly difficult to work out WHERE it comes from.

A new study has compiled over 34,000 measurements from 66 countries to trace its sources: Researchers developed a blockchain-based isotopic database, compiling 34,815 isotopic fingerprints from 1,890 pollution events across the world.

It covers data from 1957 to 2023.

Rivers are shrinking.

This is happening because water is leaking into the ground.

Here's the breakdown: A new study published in Nature Communications, analyses over 17,900 wells across Brazil.

It aims to understand where rivers may be losing water into aquifers.

Most people assume national household surveys are uniformly high quality.

But a new study finds massive subnational differences in data quality across 35 African countries.

Here's the breakdown: A new study in Nature Communications, analyses geocoded DHS data (RIP😢) at a 5km resolution.

It highlights serious concerns for health and development policymaking:

We're constantly told how countries are run by the elites.

However, mapping the economic elite has been incredibly tough to do.

But now, a new dataset compiled by 70+ researchers provides data on over 3,500 elites in 16 countries: In the 19th century, many countries — particularly in Europe, were run by the aristocracy.

Fast forward to today, most monarchs (if they’re still around), are mainly ceremonial figures.

To do good research, you need good tools.

Here are the (free) tools I can't live without: 𝟭. 𝗧𝗼𝗽𝗶𝗰 𝗜𝗱𝗲𝗮𝘁𝗶𝗼𝗻

The first category of tools I use is for ideating on a topic.

Nothing fancy here.

I use OpenAI models to brainstorm research ideas.

Meta is using Facebook and mobile phone data to produce super-granular household wealth estimates.

Here’s what you need to know about the Relative Wealth Index: The Relative Wealth Index (RWI) is a geospatial measure, which shows wealth disparities within countries.

Main features:

• It's open source
• It measures 'asset-based' wealth
• It covers 135 countries in the world
• Data is provided at 2.4km resolution

In 1969 a husband and wife launched a geospatial company with $1,100.

Today that bootstrapped company makes annual revenue over $1.5 billion.

Here’s the story of ESRI: Jack Dangermond, studied environmental science and landscape architecture at Cal Poly.

In 1969, after a stint at Harvard, together with his wife Laura, he set up a consulting company in California.

They called it the Environmental Systems Research Institute—ESRI.

Measuring agricultural GDP at a pixel level is notoriously challenging.

It requires precise information on crop type and crop yield.

@esa has launched a (free) dataset that provides this information.

Here's what you need to know about it: @esa ESA's WorldCereal project launched a dataset that provides data:

• at 10m resolution
• on farmland
• on seasonal maps of maize and cereals
• on where irrigation is used during different seasons
• on annual maps for where crops are grown temporarily

Let's unpack:

Geospatial foundation models are all the rage these days.

But, are they all hype and no substance?

Let's take a look: If I could sum up geospatial data science in 2024 in just two words, it’d be “foundation models”.

Last year saw the release of numerous geospatial foundation models (GFMs) like:

• NASA and IBM’s Prithvi,
• SpectralGPT
• Satlasnet
• AnySat
• Clay

Google is changing the weather forecasting game.

Plus they've created a Python library to make their model available.

Here's what you need to know about it: Google has developed a new AI-powered weather forecasting model called NeuralGCM.

It combines traditional physics-based prediction methods with machine learning.

Meta is known for Facebook, WhatsApp and Instagram.

But did you know they provide a range of free geospatial datasets for researchers?

These include granular measures of household wealth, population, and network access.

Here's what you need to know about it: 1. Meta provides granular estimates of household wealth for low and middle income countries.

Read more about it here:

https://x.com/yohaniddawela/status/1786359573139329406

We can now easily access over 18,000 geospatial datasets instantly, for free.

Here's everything you need to know about the Geospatial Data Catalog: Finding the right geospatial dataset can be incredibly frustrating.

We sift through Google Earth Engine, Humanitarian Data Exchange, etc, only to come up empty-handed.

We'll often conclude the dataset we need simply doesn’t exist.

Did you know we can access 𝙙𝙖𝙞𝙡𝙮 geospatial climate projections up until 1 Jan 2100?

It covers projections for:
• temperatures
• precipitation
• wind

Plus it's completely free.

Here's what you need to know about it: Daily geospatial scenario-based climate projections are part of the CMIP initiative.

CMIP is a framework for research centres across the world to develop standardised climate projections.