Yohan Iddawela Profile picture
Sharing insights from the intersection of geospatial data science and economics | PhD in Economic Geography from @lsenews | Data Scientist @adb_hq
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Jun 11 9 tweets 3 min read
Inequality isn't just about income and wealth.

It's also related to environmental factors.

Here's how poorer people are more impacted by heat-related deaths: Image A new study from @natcities has found significant environmental inequalities.

Vulnerable residents in major European cities were found to receive inadequate ‘green cooling’ compared to wealthier residents. Image
Jun 6 10 tweets 5 min read
One of the best use cases for geospatial data is examining environmental factors.

Here's a list of my favourite geospatial environmental datasets: Image 1. CO2 Emissions

@EU_ScienceHub's EDGAR database provides global geospatial data on:

• CO2 emissions
• GHG emissions
• pollutants data

Link: edgar.jrc.ec.europa.eu/emissions_data…
Jun 5 12 tweets 4 min read
Mapping informal settlements is incredibly tough.

Now we can use (free) remote sensing data to map these places and deliver aid.

Here's the breakdown: Image One of the biggest issues of using remote sensing data to map informal settlements is its COST.

It can cost a lot of $$ to purchase very high-resolution images, that provide enough detail to identify informal settlements.
May 29 13 tweets 3 min read
NASA has created a new foundation model for geospatial data.

It can provide granular weather forecasts, and air pollution forecasts.

Here's what you need to know about it: Image The foundation model—called Aurora—was announced this month.

But before going into the details of what it can do, what the heck is a foundation model? Image
May 17 10 tweets 4 min read
The best way to accurately predict where deforestation will occur?

By identifying 'Ghost Roads'.

200 people spent 7,000 hours identifying them.

Here's what you need to know about it: Image A recent study in @nature has examined the identification of 'ghost roads' as a leading indicator for deforestation.

The research was conducted by Image
May 13 19 tweets 5 min read
Want to know how to downscale nightlights data from ~500m resolution to ~30m resolution?

Here are the exact steps and codebase in R:

#rstats Instead of using AI, we can upscale nightlights data using:

• daytime satellite data (Landsat-8 or Sentinel-2); and
• @openstreetmap data on roads. Image
May 12 7 tweets 2 min read
Free geospatial datasets from Meta.

PLUS: the history of R, how to download historical OpenStreetMap data without code, and more.

Here's everything I wrote about this week: Image The history of #rstats.

From the University of Auckland, to one of the most popular statistical programming languages:

May 9 12 tweets 5 min read
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: Image 1. Meta provides granular estimates of household wealth for low and middle income countries.

Read more about it here:
May 3 13 tweets 4 min read
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: Image 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
May 1 13 tweets 4 min read
Did you know we can access 𝙙𝙖𝙞𝙡𝙮 geospatial climate forecasts up until 1 Jan 2100?

It covers forecasts for:
• temperatures
• precipitation
• snowfall

Plus it's completely free.

Here's what you need to know about it: Image Daily geospatial climate forecasts are part of the CMIP initiative.

CMIP is a framework for research centres across the world to develop standardised climate forecasts. Image
Apr 29 16 tweets 5 min read
Measuring agricultural GDP at a pixel level is notoriously challenging.

It requires precise information on crop type and crop yield.

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

Here's what you need to know about it: Image @esa Last year 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: Image
Apr 19 18 tweets 5 min read
The most efficient way to process nightlights data?

Using Google Earth Engine.

But using it with R can be tough.

So here's a detailed guide on how to do it:

#rstats Image By the end of this post, you'll know the steps required to create the following graph of monthly luminosity data in R using Google Earth Engine.

GEE is a fantastic resource—the computations are done on the cloud, so you won't be limited by your computer's hardware. Image
Apr 8 13 tweets 6 min read
High-resolution satellite images can be insanely expensive to buy.

So here's a list of free datasets you can access.

These datasets can be used to build foundation models, super-resolution models, or for segmentation. Source: Maxar The most commonly used free multi-spectral satellite images are from:

• Sentinel 2
• Landsat-8

However, Sentinel 2 has ~10m resolution (for RGB), while Landsat-8 is ~30m (for RGB).

But what free high-resolution datasets exist to train foundation models on? Image
Apr 6 9 tweets 3 min read
My recommended R packages for geospatial analyses.

PLUS: how to get €50k (free) to launch a geospatial business, recommended geospatial courses, top websites for free geospatial data, and more.

Here's everything I wrote about this week: Image An introduction to U-Nets: one of the most important models in the geospatial space

Apr 5 12 tweets 6 min read
Geospatial data science is all about having access to good data.

Here are a list of my favourite free geospatial resources: Image 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
Mar 28 13 tweets 3 min read
To be an exceptional geospatial data scientist, you need true competitive advantages.

Here are 10 (learnable) competitive advantages to cultivate: Image 1. Blending interests

It's hard to be in the top 0.01% of data scientists.

But it's easier to be in the top 0.01% of people who intersect data science, with other skills (e.g. political science, design, and data engineering).

Blending skills helps you stand out.
Mar 26 9 tweets 3 min read
To be a good geospatial data scientist, you need good tools.

My tool of choice is R.

Here are the packages I can't live without:

#rstats Source: WZB Data Science Blog 𝟭. 𝗣𝗮𝗰𝗸𝗮𝗴𝗲𝘀 𝘁𝗼 𝗱𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝗴𝗲𝗼𝘀𝗽𝗮𝘁𝗶𝗮𝗹 𝗱𝗮𝘁𝗮

• 𝗿𝗴𝗲𝗲𝗱𝗶𝗺: provides the easiest way to download data directly from Google Earth Engine as GeoTiffs.

• 𝗿𝗴𝗲𝗲: an R wrapper for Google Earth Engine, so you can run GEE commands in R. Image
Mar 22 23 tweets 6 min read
For decades, this curve summed up the mainstream understanding of inequality within a country.

However, recently, this curve has been the source of a fierce debate.

Here's the breakdown: Image The Kuznets Curve, was an idea proposed by the American economist, Simon Kuznets.

According to this, as an economy developed (and income per capita grew), inequality would first increase, before steadily decreasing. Image
Mar 15 15 tweets 5 min read
A major issue for econ studies is the continual revision of key variables like GDP.

Regressions that previously worked lose their results with new revisions.

To address this, we need to access previous versions of key datasets.

Here's how to do it: The World Bank updates historical GDP data for several reasons:

• Adjusting weights for changing industrial composition of economies
• Statistical methods adjusted by governments
• Correcting calculation errors
• Eliminating breaks in series
Mar 11 14 tweets 6 min read
Most countries don't provide granular data on economic activity.

Luckily, there are a range of geospatial datasets we can use as proxies.

Here's a list of the best (free) resources for estimating sub-national economic activity: Image I've previously discussed geospatial datasets that provide GDP estimates at a granular level.

Instead, in this post I'm providing proxies for economic activity.

If you're interested in the granular GDP estimates you can check this post out:
Feb 29 9 tweets 3 min read
Many of us have set a goal of mastering GIS in 2024.

But how many will *actually* follow through?

Here are my top three geospatial resources to help with achieving your goals: Image One of the most common questions I'm asked is which resources I can recommend to go from beginner to advanced in geospatial data science.

I don't want to bombard you with hundreds of resources.

Instead, here are my top three: