Turns out there are some pretty big issues with DHS data.

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 at a 5km resolution.

It highlights serious concerns for health and development policymaking:

Air pollution is usually blamed for lung and heart disease.

But new clinical data shows it may also drive diabetes.

Here’s what you need to know: The researchers combined:

• Outpatient clinical records from the Italian Association of Diabetologists (AMD)
• Municipality-level pollution exposure data from ISPRA, Italy’s environmental protection agency

This gave them a unique dataset of pollution and diabetes at the local level.

Changing your map’s resolution can change your conclusions.

It’s called the Support Effect.

And it distorts everything from poverty estimates to climate models.

Here’s how it works: In spatial analysis, “support” refers to the unit of measurement in space.

It could be:
• a point (e.g., GPS location)
• an area (e.g., census tract)
• a pixel (e.g., satellite image cell)

The support determines how and where a variable is measured.

We’ve been measuring HDI at the national level for decades.

But living standards can vary dramatically within a country.

A new dataset finally shows HDI at a much finer scale.

Here’s the breakdown: The first sub-national HDI dataset was actually published in @ScientificData in 2019.

It was put together by @Globaldatalab.

Google DeepMind just released one of the most important tools in geospatial data science.

It’s called AlphaEarth Foundations.

I want to break it down for you in intuitive terms: We have petabytes of satellite images.

But it’s still hard to answer questions like:

• What’s in this image?
• How has it changed?
• What kind of crop or forest is this?

AlphaEarth helps answer these questions, even in places with limited data.

Most countries don't publish official sub-national population data.

Luckily, there are several geospatial population datasets we can use instead.

Here's a list of them (I wish I knew about 5 years ago): 1. WorldPop (@WorldPopProject) provides data on:

• population counts
• population density
• population by age and sex

Data is from 2000-2020 and available at 100m or 1km resolution.

Link: worldpop.org

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.