Regional and demographics data for Uganda.

The datasets - Uganda Lakes, are sourced from the Ugandan Energy Sector GIS Working Group Open Data Site, developed and maintained by the Ugandan Energy Sector GIS Working Group. The Ugandan Energy Sector GIS Working Group’s mission is to develop a high quality GIS for the Energy Sector of Uganda in order to drive informed decision-making. As such, it brings datasets together in one place, organize them, keep them updated, and make public data available to all stakeholders. Link: http://data-energy-gis.opendata.arcgis.com/ The dataset is published on October 23, 2014

The datasets are sourced from the Ugandan Energy Sector GIS Working Group Open Data Site, developed and maintained by the Ugandan Energy Sector GIS Working Group. The Ugandan Energy Sector GIS Working Group’s mission is to develop a high quality GIS for the Energy Sector of Uganda in order to drive informed decision-making. As such, it brings datasets together in one place, organize them, keep them updated, and make public data available to all stakeholders. Link: http://data-energy-gis.opendata.arcgis.com/

The datasets are sourced from the Ugandan Energy Sector GIS Working Group Open Data Site, developed and maintained by the Ugandan Energy Sector GIS Working Group. The Ugandan Energy Sector GIS Working Group’s mission is to develop a high quality GIS for the Energy Sector of Uganda in order to drive informed decision-making. As such, it brings datasets together in one place, organize them, keep them updated, and make public data available to all stakeholders. Link: http://data-energy-gis.opendata.arcgis.com/

Kenyan forest-related data sets and analyses

The datasets are sourced from the Ugandan Energy Sector GIS Working Group Open Data Site, developed and maintained by the Ugandan Energy Sector GIS Working Group. The Ugandan Energy Sector GIS Working Group’s mission is to develop a high quality GIS for the Energy Sector of Uganda in order to drive informed decision-making. As such, it brings datasets together in one place, organize them, keep them updated, and make public data available to all stakeholders. Link: http://data-energy-gis.opendata.arcgis.com/

Climate change, that is a threat to ecosystems and the livelihoods of those that depend on them, is increasingly manifesting as an increased frequency and intensity of severe weather events such as droughts and floods (Déqué et al., 2017). Climate change has created an urgent need for early warning aids or models to enhance the sub-Saharan African health systems ability to prepare for, and cope with escalations in treatment needs of climate sensitive diseases (Nhamo & Muchuru, 2019). This dataset was created from the health and weather data of nine purposively selected study districts in Uganda, whose health and weather data were available for the development of an early warning health model (https://github.com/CHAIUGA/chasa-model) and an accompanying prediction web app (https://github.com/CHAIUGA/chasa-webapp). The districts were selected based on the following criteria: (a) were experiencing climate change and variability, (b) represented different climatologic, and agro-ecological zones, (c) availability of climate information and health information from a health facility within a 40 kilometres radius of a functional weather station. Historical weather data was retrieved from the Uganda National Meteorological Association databases, as monthly averages. The weather variables in this data included: atmospheric pressure, rainfall, solar radiation, humidity, temperature (maximum, minimum and mean), and wind (gusts and average wind speed). The monthly health aggregated data for the period starting September 2018 to December 2019, was retrieved from the National Health Repository (DHIS2) for referral hospitals within the selected districts. Only data for a selection of climate-sensitive disease aggregates was obtained. The dataset contains 436 complete matched disease and weather records. Ethical issues: Both the de-identified aggregate monthly disease diagnosis count data and weather data in this dataset are from national data available to the public on request.

The datasets - Uganda Generation Sites Decommissioned (2016), are sourced from the Ugandan Energy Sector GIS Working Group Open Data Site, developed and maintained by the Ugandan Energy Sector GIS Working Group. The Ugandan Energy Sector GIS Working Group’s mission is to develop a high quality GIS for the Energy Sector of Uganda in order to drive informed decision-making. As such, it brings datasets together in one place, organize them, keep them updated, and make public data available to all stakeholders. Link: http://data-energy-gis.opendata.arcgis.com/

This data asset contains data on a field experiment to improve the accountability of the delivery of public services in Uganda, specifically trash removal.

The datasets are sourced from the Ugandan Energy Sector GIS Working Group Open Data Site, developed and maintained by the Ugandan Energy Sector GIS Working Group. The Ugandan Energy Sector GIS Working Group’s mission is to develop a high quality GIS for the Energy Sector of Uganda in order to drive informed decision-making. As such, it brings datasets together in one place, organize them, keep them updated, and make public data available to all stakeholders. Link: http://data-energy-gis.opendata.arcgis.com/

Techsalerator’s Location Sentiment Data for Uganda

Techsalerator’s Location Sentiment Data for Uganda offers an extensive collection of data that is crucial for businesses, researchers, and technology developers. This dataset provides deep insights into public sentiment across various locations in Uganda, enabling data-driven decision-making for development, marketing, and social research.

For access to the full dataset, contact us at info@techsalerator.com or visit Techsalerator Contact Us.

Techsalerator’s Location Sentiment Data for Uganda

Techsalerator’s Location Sentiment Data for Uganda delivers a comprehensive analysis of public sentiment across urban, rural, and industrial locations. This dataset is essential for businesses, government agencies, and researchers looking to understand the sentiment trends in different regions of Uganda.

Top 5 Key Data Fields

• Location of Data Capture – Identifies the geographic location where sentiment data was collected, enabling location-specific analysis of public perception.
• Sentiment Score – Provides a numerical representation of sentiment, with positive, negative, and neutral classifications, supporting sentiment analysis for public opinion research.
• Demographic Segmentation – Breaks down sentiment by key demographic factors such as age, gender, and occupation to uncover sentiment trends within specific groups.
• Time of Data Capture – Records the exact time and date of sentiment data collection, helping analyze variations in sentiment over different times of day or during specific events.
• Sentiment Source – Categorizes data sources such as social media posts, surveys, and customer feedback, to offer insights into the platform-specific sentiment.

Top 5 Sentiment Trends in Uganda

• Urban vs. Rural Sentiment – Variations in sentiment between urban centers like Kampala and rural areas, often revealing different priorities and perceptions on topics like infrastructure, education, and healthcare.
• Political Sentiment – Public sentiment around political events and figures, with insights into political stability, government policies, and public opinion on elections.
• Economic Sentiment – How Ugandans feel about economic conditions, employment opportunities, inflation, and business growth across different regions.
• Social Issues Sentiment – Public opinion on social issues such as gender equality, healthcare access, education, and human rights.
• Technology Adoption Sentiment – Increasing interest in digital technologies, mobile platforms, and internet access, reflecting sentiment on technological advancements and connectivity.

Top 5 Applications of Location Sentiment Data in Uganda

• Urban Development and Planning – Helps city planners and government bodies design better urban environments based on public sentiment toward infrastructure, traffic, and public services.
• Marketing and Consumer Insights – Brands use sentiment data to tailor marketing campaigns and improve customer engagement by understanding regional preferences and concerns.
• Policy and Governance – Governments and NGOs utilize sentiment data to shape policies that address public concerns and improve governance effectiveness.
• Social Research – Social researchers can analyze regional disparities in public opinion on issues like education, healthcare, and social justice.
• Crisis Management and Response – Sentiment data aids in understanding public reaction to crises like health emergencies or natural disasters, helping improve response strategies.

Accessing Techsalerator’s Location Sentiment Data

To obtain Techsalerator’s Location Sentiment Data for Uganda, contact info@techsalerator.com with your specific requirements. Techsalerator offers customized datasets based on requested fields, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields

• Location of Data Capture
• Sentiment Score
• Demographic Segmentation
• Time of Data Capture
• Sentiment Source
• Topic Categories
• Public Opinion on Government Policies
• Sentiment on Social Issues
• Regional Sentiment Trends
• Contact Information

For deep insights into public sentiment across Uganda, Techsalerator’s dataset is an invaluable resource for businesses, policymakers, and researchers.

Data on households in Uganda from the census

GDP from Public Administration in Uganda decreased to 1176 UGX Billion in the first quarter of 2025 from 1263 UGX Billion in the fourth quarter of 2024. This dataset provides - Uganda Gdp From Public Administration- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Abstract

To facilitate the use of data collected through the high-frequency phone surveys on COVID-19, the Living Standards Measurement Study (LSMS) team has created the harmonized datafiles using two household surveys: 1) the country’ latest face-to-face survey which has become the sample frame for the phone survey, and 2) the country’s high-frequency phone survey on COVID-19.

The LSMS team has extracted and harmonized variables from these surveys, based on the harmonized definitions and ensuring the same variable names. These variables include demography as well as housing, household consumption expenditure, food security, and agriculture. Inevitably, many of the original variables are collected using questions that are asked differently. The harmonized datafiles include the best available variables with harmonized definitions.

Two harmonized datafiles are prepared for each survey. The two datafiles are: 1. HH: This datafile contains household-level variables. The information include basic household characterizes, housing, water and sanitation, asset ownership, consumption expenditure, consumption quintile, food security, livestock ownership. It also contains information on agricultural activities such as crop cultivation, use of organic and inorganic fertilizer, hired labor, use of tractor and crop sales.
2. IND: This datafile contains individual-level variables. It includes basic characteristics of individuals such as age, sex, marital status, disability status, literacy, education and work.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

See “Uganda - National Panel Survey 2019-2020” and “Uganda - High-Frequency Phone Survey on COVID-19 2020-2021” documentations available in the Microdata Library for details.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Uganda National Panel Survey 2019-2020 and Uganda High-Frequency Phone Survey on COVID-19 2020-2021 data were harmonized following the harmonization guidelines (see “Harmonized Datafiles and Variables for High-Frequency Phone Surveys on COVID-19” for more details).

The high-frequency phone survey on COVID-19 has multiple rounds of data collection. When variables are extracted from multiple rounds of the survey, the originating round of the survey is noted with “_rX” in the variable name, where X represents the number of the round. For example, a variable with “_r3” presents that the variable was extracted from Round 3 of the high-frequency phone survey. Round 0 refers to the country’s latest face-to-face survey which has become the sample frame for the high-frequency phone surveys on COVID-19. When the variables are without “_rX”, they were extracted from Round 0.

Response rate

See “Uganda - National Panel Survey 2019-2020” and “Uganda - High-Frequency Phone Survey on COVID-19 2020-2021” documentations available in the Microdata Library for details.

The datasets - Uganda Lakes, are sourced from the Ugandan Energy Sector GIS Working Group Open Data Site, developed and maintained by the Ugandan Energy Sector GIS Working Group.

The Ugandan Energy Sector GIS Working Group’s mission is to develop a high quality GIS for the Energy Sector of Uganda in order to drive informed decision-making. As such, it brings datasets together in one place, organize them, keep them updated, and make public data available to all stakeholders.

Link: http://data-energy-gis.opendata.arcgis.com/

The dataset is published on October 23, 2014

"The Uganda Budget Information website promotes transparency and accountability in the use of public funds by allowing you to access and give feedback on national and local government budgets and performance" (quote from Uganda Budget Information website)

The datasets are sourced from the Ugandan Energy Sector GIS Working Group Open Data Site, developed and maintained by the Ugandan Energy Sector GIS Working Group. The Ugandan Energy Sector GIS Working Group’s mission is to develop a high quality GIS for the Energy Sector of Uganda in order to drive informed decision-making. As such, it brings datasets together in one place, organize them, keep them updated, and make public data available to all stakeholders. Link: http://data-energy-gis.opendata.arcgis.com/

The datasets are sourced from the Ugandan Energy Sector GIS Working Group Open Data Site, developed and maintained by the Ugandan Energy Sector GIS Working Group. The Ugandan Energy Sector GIS Working Group’s mission is to develop a high quality GIS for the Energy Sector of Uganda in order to drive informed decision-making. As such, it brings datasets together in one place, organize them, keep them updated, and make public data available to all stakeholders.

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through integrating census, survey, satellite and GIS data sets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available. Uganda data available from WorldPop here.

This .shp file is a polyline shapefile containing the roads data in Uganda that was used to create the Uganda cost allocation/friction surfaces. The data are a combination (merged) of Open Street Map roads data and MapwithAi project roads data.