IPUMS DHS Blog Posts – IPUMS DHS Spatial Analysis and Health Research Hub

Latest posts

Access to Healthcare Part 2: The 2SFCA Model

Health outcomes and healthcare utilization rates are influenced by spatial accessibility to health, especially in rural areas of developing countries. This second post in a series of three describes one of the most effective ways to measure spatial accessibility: the Two-Step Floating Catchment Area (2SFCA) Model.

Access to Healthcare Part 1: How it is Measured

Many people in developing countries experience poor health as a result of poor spatial access to clinics and hospitals. Our understanding of how to improve global health is limited by our ability to measure healthcare accessibility.

Vegetation and Land Cover Part 3: Land Cover/Land Use

An almost overwhelming number of vegetative and land cover data types exist. This post describes and compares sources of land cover data, as the final installment of a series of three.

Devon Kristiansen

Vegetation and Land Cover Part 2: Environmental Moisture

An almost overwhelming number of vegetative and land cover data types exist. This second post of three describes a few common indices representing vegetative or soil moisture, with an emphasis on agricultural productivity.

Devon Kristiansen

Vegetation and Land Cover Part 1: Vegetative Indices

An almost overwhelming number of vegetative and land cover data types exist. As the first post in a series of three, this blog gives a brief primer into vegetative indices and which ones might be most appropriate for different research goals.

Devon Kristiansen

Multilevel Dietary Diversity using DHS data

A follow-up post on using a comparable measure of dietary diversity in a multilevel model including NDVI contextual information in the model.

Devon Kristiansen

Using Historical and Projected Climate Data to Forecast Child Malnutrition

In the final post in our series on future estimation we create a model to predict child malnutrition and plug in projected climate scenario data to predict a future level of child malnutrition.

Rebecca Luttinen, Jessie Pinchoff

Climate scenario chart

Harnessing CHC-CMIP6 Climate Scenario Data to Explore the Future

Returning to our series on future estimation, we provide an overview of a data source for future climate scenarios.

Rebecca Luttinen, Jessie Pinchoff

Measuring Dietary Diversity using DHS data

An introduction to constructing comparable dietary diversity indicators. This post breaks away from the series on future conditions, with the first post of two on dietary diversity.

Devon Kristiansen, Rebecca Luttinen

Photo of a family at sunset

Estimating the now and predicting the future: Fertility rate estimation and population projection

Use the DHS to estimate fertility levels and project future population levels

Rebecca Luttinen, Elizabeth Heger Boyle

US temprature map

Anticipating the Future: Important terminology

Part 1 in our series exploring climate and population projections in health research

Molly Brown, Rebecca Luttinen

VIIRS for NDVI data

From MODIS to VIIRS: The Latest Source for NDVI Data

Update your NDVI workflows with NASA’s newest instruments

Finn Roberts, Rebecca Luttinen

Qualitative Methods

Incorporating Qualitative Methods into Spatial Health Research

Spatiotemporal research is enhanced when guided by local knowledge, perspective, and expertise.

QGIS for Spatial Data Analysis

Using QGIS for Spatial Data Analysis

Standalone GIS software can provide an interactive alternative to code-based analysis workflows

Using Leaflet for Exploratory Data Analysis: Part 2

Exploring customization options for leaflet maps

Using Leaflet for Exploratory Data Analysis: Part 1

An introduction to interactive mapping using the leaflet package

Measuring Population Density

An introduction to gridded data sources used to measure human presence across the globe

Aggregation Methods for NDVI Data

We use data from two regions to illustrate approaches to processing NDVI data

Seasonal NDVI, Kenya

An Iterative Workflow for Loading NDVI Data in R

Use purrr and terra to efficiently integrate NDVI data across multiple spatial extents

Subseasonal NDVI max

Introducing NDVI as a Tool for Population Health Research

Satellite imagery can be used in conjunction with place-specific research methods to assess food production and its impacts

Incorporating Environmental and Reproductive History Data

Understanding the challenges when using longitudinal data with DHS surveys

Sunnee Billingsley

Heatwave model coefficients

Time-specific temperature exposures for DHS survey respondents

Fine-tune an analysis by incorporating temporal information from a DHS survey with longitudinal environmental data

Proportion of days above threshold

Flexible Workflows with CHIRTS Temperature Data

Generalize your code to more easily aggregate time-varying environmental data

Demystifying Spatially Harmonized Geography in IPUMS DHS

A demonstration of data and techniques to handle shifting geographic boundaries in longitudinal research

Sula Sarkar, Devon Kristiansen, Miriam King

Dark sky over fields

Droplets of Insights for Integrating DHS and Rainfall Data

Some factors to consider when obtaining and preparing precipitation data for use with DHS surveys

Audrey Dorélien, Molly Brown

Average rainfall within clusters

Attaching CHIRPS Precipitation Data to DHS Surveys

Remotely sensed precipitation data can provide important context for understanding the health outcomes reported in the DHS

Finn Roberts, Matt Gunther

Developing Robust Conceptual Models in Research on Extreme Weather and Health

Introducing the theoretical foundations for effective research on weather extremes and health

Elizabeth Heger Boyle, Kathryn Grace, Audrey Dorélien, Devon Kristiansen

Obtaining Data from IPUMS DHS

Identify and download data from the IPUMS DHS website and load it into R

Finn Roberts, Matt Gunther

An Introduction to R

Getting started with R and RStudio

Finn Roberts, Matt Gunther