How to predict poverty from nightlight data?

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Abderrahmane Boudribila

Dlzar Al Kez

Predicting poverty using nightlight data is a well-established method in remote sensing and machine learning research. Here are some steps you can take to work with nightlight data:

Finding Nightlight Data: To locate nightlight data for your specific city and country, you can use the Visible Infrared Imaging Radiometer Suite (VIIRS) dataset or the Defense Meteorological Satellite Program - Operational Linescan System (DMSP-OLS) dataset, both of which are widely used in poverty mapping research. You can obtain these datasets from various sources, including the NASA Earthdata Search website or the Google Earth Engine platform.

Labelling Nightlight Data: Once you have obtained the nightlight data, you can use the poverty data for your specific city to label the nightlight data. For example, you can use the poverty rate for each geographic unit (e.g., census tract, village, or district) in your study area to label the corresponding nightlight intensity values. You can then use this labelled data to train a machine learning model.

Setting Input and Output for Machine Learning: To use nightlight data as input for the machine learning model, you can extract various statistical measures from the nightlight data, such as the mean, standard deviation, and maximum intensity value. You can also extract spatial features from the nightlight data, such as the distance to the nearest road or the distance to the nearest urban center. You can then use these features as input to the machine learning model.

The output of the machine learning model should be the poverty rate for each geographic unit in your study area. You can train the model to predict the poverty rate based on the input features derived from the nightlight data.

Setting a Nightlight Intensity Threshold: To set a nightlight intensity threshold for your study area, you can use various methods, including visual inspection, histogram analysis, or statistical analysis. For example, you can set a threshold based on the nightlight intensity values that correspond to the lowest 10% of poverty rates in your study area. You can then use this threshold to separate the nightlight data into two classes: "poor" and "non-poor."

Zrar Khalid Abdul

Dear Abderrahmane Boudribila .. your answer is really helpful. I have a question regarding poverty rate. how can I find out the poverty rate? based on which variable, poverty rate should be formulated?

Thank you very much Abderrahmane Boudribila your comments are really helpful.

You're welcome! I'm glad my comments were helpful to you

There are different ways to measure poverty, and the choice of poverty measure may depend on the context and the purpose of your study. Here are some commonly used poverty measures:

Income-based measures: One of the most common measures of poverty is based on income. This measure compares a household's income to a poverty threshold, which is typically set at a level that is considered the minimum income needed to meet basic needs. The poverty rate is the percentage of the population below the poverty threshold.

Consumption-based measures: In some cases, income may not accurately reflect a household's standard of living. In such cases, consumption-based measures of poverty can be used, which measure the household's expenditure on goods and services.

Multidimensional measures: Poverty is a complex phenomenon that cannot be fully captured by a single measure. Multidimensional measures of poverty take into account multiple dimensions of poverty, such as health, education, and access to basic services.

To determine the poverty rate for a specific area, you would need data on the relevant variables. For income-based measures, you would need data on household income, while for consumption-based measures, you would need data on household expenditures. For multidimensional measures, you would need data on the relevant dimensions of poverty. This data can be obtained from various sources, such as national statistical agencies, surveys, or administrative records.

Thank you so much again for your valuable comments.

dear Abderrahmane Boudribila . Thanks so much for your reply.