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This repository contains the implementation of our KDD2019 paper: Predicting Economic Development Using Geolocated Wikipedia Articles.

Here, we present the code for parsing a Wikipedia xml dump into its constituent geolocated articles, converting them to human readable format, training a Doc2Vec model over them, and building and training a model using the Doc2Vec embeddings to predict poverty via (1) the 10 closest articles and (2) the articles as well as Nightlights imagery.

We will be updating and cleaning the code for distribution and can field concerns or requests at or

Predicting Economic Development Using Geolocated Wikipedia Articles

Downloading and Parsing Geolocated Articles

The files for downloading and parsing geolocated articles are located in


Here is a discription of what each file does: Loads the xml articles and extracts the text and hyperlinks from each one. A new array is built of these articles. All articles that are also contained in the coordinate element array are also included here as well, and they also retain their coordinate tag as the element at index 3, as (title, text, hyperlink, coordinate) tuples. All other articles are stored as (title, text, hyperlink, None) tuples. This file extracts the raw category tag from all articles' infoboxes and separates the articles' body texts from their infobox texts. After, it parses the infobox texts into a key-value dictionary to render the infoboxes searchable. It then stores the new [title, full text, hyperlinks, coordinates, raw category, curated category, infobox dictionary] lists in a database, organized by curated category. The file containes code fragments from older category extraction methods that have since been abandoned. This file parses the large xml file that all wikipedia articles are stored in into .txt files storing all articles beginning with a certain letter, all File: articles, all Template: articles, and all Category: articles. Loads the arrays of xml articles, extracts each one that contains a geolocation tag (in the form of coordinates), and builds a new array of these articles structured as (title, body text, hyperlinks, coordinates) tuples; the body text of the article is also cleaned up by replacing xml code abbreviations with their human-readable counterparts; saves the arrays in the 'coordinate_articles' directory.

Training Doc2Vec on Geolocated Articles

We use gensim doc2vec packege for training the Doc2Vec model. To train the Doc2Vec model on geolocated articles, run:

python Doc2Vec/Doc2Vec_modules/

Socioeconomic Data

Our ground truth dataset consists of data on asset ownership from the Demographic and Health Surveys DHS and Afrobarometer. DHS is a set of nationally representative household surveys conducted periodically in most developing countries, spanning 31 African countries. Afrobarometer (Round 6) is a survey across 36 countries in Africa, assessing various aspects of welfare, education, and infrastructure quality.

Nightlight Imagery Dataset

We use high-resolution night-time imagery from VIIRS [Elvidge et al., 2017]. Each of the imagery is of shape (255, 255). For each point in the AWI data that we train on, we obtain a nightlights image centered on its coordinate. The size of these images was set to (5 km × 5 km) due to the (maximum) 5km noise that each location has.


We discuss the model architecture in detail in section 3 of the paper. We provide the code for Nightlight Only Model, Wikipedia Embedding Model and Multi-modal Model in


Nightlight Only Model

To train the Wikipedia embedding model, run

python models/

Wikipedia Embedding Model

See section 3.2 for more detail.

To train the Wikipedia embedding model, run

python models/

The model will evaluate the results on Ghana, Malawi, Nigeria, Tanzania, and Uganda.

Wikipedia Nightlight Multi-modal Model

See section 3.3 for more detail.

To train the multi-modal model, run

python models/

The model will evaluate the results on Ghana, Malawi, Nigeria, Tanzania, and Uganda.


When paired with nightlights satellite imagery, our method outperforms all previously published benchmarks for this prediction task, indicating the potential of Wikipedia to inform both research in the social sciences and future policy decisions.

Here is an example: On the left is the ground truth values of 1074 data points averaged on Admin 2 Level for Tanzania. On the right is the Wikipedia Embedding Model predicted values averaged on Admin 2 level for a model trained on Ghana and tested on Tanzania (r2 = 0.52). The resulting predictions show that the model is capable of generalizing across national boundaries.

If you use this repository, please cite our paper:

 author = {Sheehan, Evan and Meng, Chenlin and Tan, Matthew and Uzkent, Burak and Jean, Neal and Burke, Marshall and Lobell, David and Ermon, Stefano},
 title = {Predicting Economic Development Using Geolocated Wikipedia Articles},
 booktitle = {Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
 series = {KDD '19},
 year = {2019},
 isbn = {978-1-4503-6201-6},
 location = {Anchorage, AK, USA},
 pages = {2698--2706},
 numpages = {9},
 url = {},
 doi = {10.1145/3292500.3330784},
 acmid = {3330784},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {computational sustainability, deep learning, remote sensing},


Implementation of Geolocated Articles Processing and Poverty Mapping - [KDD19]



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