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 ingegrating cencus, survey, satellite and GIS datasets 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. REGION: Africa 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.

The population of the world, allocated to 1 arcsecond blocks. This refines CIESIN’s Gridded Population of the World project, using machine learning models on high-resolution worldwide Digital Globe satellite imagery.

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 ingegrating cencus, survey, satellite and GIS datasets 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. REGION: Africa 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. Bangladesh data available from WorldPop here.

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Bangladesh: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

Digital polygon dataset of Population Distribution per District of Chittagong Hill Tracts of Bangladesh. This dataset is basic vector layer based on LGED Administrative Base Map and Bangladesh Bureau of Statistics (BBS) 2001.

In this analysis we have combined several data sources around the floods in Bangladesh in August 2017.

Visualization

• See attached map for a map visualization of this analysis.
• See http://bit.ly/2uFezkY for a more interactive visualization in Carto.

Situation

Currently, in Bangladesh many water level measuring stations measure water levels that are above danger levels. This sets in triggers in motion for the partnership of the 510 Data Intitiative and the Red Cross Climate Centre to get into action.

Indicators and sources

In the attached map, we combined several sources:

• Locations of waterlevel stations and their respective excess water levels (cms above danger level) at 14/08/2017 (Source: http://www.ffwc.gov.bd/index.php/googlemap?id=20)
• Population density in Bangladesh to quickly see where many people live in comaprison to these higher water-level stations. (Source: http://www.worldpop.org.uk/data/summary/?doi=10.5258/SOTON/WP00018 >> the People per hectare 2015 UN-adjusted totals file is used.)
• Vulnerability Index: we constructed a Vulnerability Index (0-10) based on two sources. First poverty incidence was collected from Worldpop (Source: http://www.worldpop.org.uk/data/summary/?doi=10.5258/SOTON/WP00020 >> The estimated likelihood of living below $2.50/day). Second, we used a Deprivation Index which is estimated in the report Lagging District Reports 2015 (Source: http://www.plancomm.gov.bd/wp-content/uploads/2015/02/15_Lagging-Regions-Study.pdf > Appendices > Table 20), which combines many socio-economic variables into one Deprivation Index through PCA analysis.

Detailed methodology Vulnerability

• The above-mentioned poverty source file is on a raster level. This raster level poverty was transformed to admin-4 level geographic areas (source: https://data.humdata.org/dataset/bangladesh-admin-level-4-boundaries), by taking a population-weighted average. (Source population also Worldpop).
• The district-level PCA components from abovementioned reports were matched to the geodata based on district names, and thus joined to the admin-4 level areas, which now contain a poverty value as well as Deprivation Index value. Note that all admin-4 areas within one district (admin-2) obviously all have the same value. The poverty rates do differ between all admin-4 areas.
• Lastly, both variables were transformed to a 0-10 score (linearly), and a geomean was taken to calculate the final index of the two. A geomean (as opposed to an arithmetic mean) is often used in calculating composite risk indices, for example in the widely used INFORM-framework (www.inform-index.org).

This interactive map of Bangladesh highlights the project locations of the Integrated Agricultural Productivity Project (IAPP) and PRAN. Bangladesh is divided into seven administrative divisions, which are broken down into 64 districts, and further divided into 485 upazilas. This map overlays sub-national poverty data, demographic indicators, and other information relevant to the program. IAPP will target the districts of Rangpur, Kurigram, Lalmonirhat, and Nilfamari in the north and the districts of Barisal, Patuakhali, Barguna and Jhalokathi in the south. The project is expected to increase the productivity of major crops like cereals and pulses, increase the productivity of fish and livestock, increase the availability of certified seed, increase the irrigated area, and the income of farmers in all 54 upazilas in these eight districts. The project areas were selected for their high rates of poverty, food insecurity, and their vulnerability to natural shocks such as tidal surge in the south, and flash flood and drought in the north. GAFSP is financing the expansion of food processing and manufacturing capacity of Natore Agro Limited from PRAN group. PRAN group is the largest food and nutrition company in Bangladesh, with more than 40,000 employees and over 200 different products. The enhancement of operations is creating new jobs (over 1,200 expected), in a region severely affected by unemployment and is increasing the opportunities for local producers as raw material suppliers for the company. Data Sources: PRAN Project LocationSource: GAFSP Documents. IAPP Project Areas

Source: Project Appraisal Document (PAD). Poverty Incidence (Proportion of population below the poverty line) (2010): Proportion of the population living on less than US$1.25 a day, measured at 2005 international prices, adjusted for purchasing power parity (PPP).Source: Bangladesh Bureau of Statistics. “HIES Survey 2010 Chapter 6.” Malnutrition (Proportion of underweight children under 5 years) (2011): Prevalence of severely underweight children is the percentage of children under age 5 whose weight-for-age is more than 3 standard deviations below the median for the international reference population ages 0-59 months.Source: Measure DHS. “Bangladesh Demographic and Health Survey 2011. Preliminary Report.” Total Population (2011): Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship, except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin.Source: Bangladesh Bureau of Statistics. “Population and Housing Census 2011. Preliminary Results.” Population Density (2011): Population divided by land area in square kilometers.Source: Bangladesh Bureau of Statistics. “Population and Housing Census 2011. Preliminary Results.” Irrigated Area (2009/10): Total irrigated area in hectares.Source: Bangladesh Bureau of Statistics. 2010 Yearbook of Agricultural Statistics of Bangladesh. Potato Production (2009-10 and 2010-11): Total production in tons by variety and total production in tons per hectare by variety.Source: Bangladesh Bureau of Statistics. “2012 Yearbook of Agricultural Statistics of Bangladesh.” Boro Rice (2009-10 and 2010-11): Total production in tons by variety and total production in tons per hectare by variety.Source: Bangladesh Bureau of Statistics. “2012 Yearbook of Agricultural Statistics of Bangladesh.” Bangladesh Soil Salinity (2009): Saline soils, salinity boundary, and coastlines.

Source: Soil Resource Development Institute SRMAF Project – Bangladesh Ministry of Agriculture. “Saline Soils in Bangladesh 2010.”The maps displayed on this website are for reference only. The boundaries, colors, denominations and any other information shown on these maps do not imply, on the part of GAFSP (and the World Bank Group), any judgment on the legal status of any territory, or any endorsement or acceptance of such boundaries.

A database was generated of estimates of geographically referenced carbon densities of forest vegetation in tropical Southeast Asia for 1980. A geographic information system (GIS) was used to incorporate spatial databases of climatic, edaphic, and geomorphological indices and vegetation to estimate potential (i.e., in the absence of human intervention and natural disturbance) carbon densities of forests. The resulting map was then modified to estimate actual 1980 carbon density as a function of population density and climatic zone. The database covers the following 13 countries: Bangladesh, Brunei, Cambodia (Campuchea), India, Indonesia, Laos, Malaysia, Myanmar (Burma), Nepal, the Philippines, Sri Lanka, Thailand, and Vietnam. For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/epubs/ndp/ndp068/ndp068.html

Bangladesh is a South Asian country located at the crossroads of the Indochina and Indo-Himalayan subregions, making it a country of rich faunal diversity. Bangladesh's high population density paired with rapid habitat alteration leaving only 6% of its natural habitats threatens its faunal diversity. Over 1,455 bat species live on earth, providing immense ecological services to maintain biodiversity. The paucity of bat research in Bangladesh and the lack of comprehensive work has led us to set the goal of checking how many species are present in Bangladesh, and the possibility of bat species yet to have occurred. Here we compiled species occurrence data on the bats of Bangladesh and states in neighboring countries (India – states are West Bengal, Sikkim, Meghalaya, Assam, Tripura, Mizoram; Myanmar – states are Chin, Rakhine) from the museums (American Museum of Natural History, Smithsonian National Museum of Natural History, Natural History Museum at United Kingdom, Field Museum of Natural History, Hungarian Natural History Museum, and Royal Ontario Museum), Global Biodiversity Information Facility, and literature, and constructed distribution maps for each species. The maps depicted both the fine-scale and coarse-scale distribution of the species. We confirmed 31 species are occurring in Bangladesh – among them, 22 species are confirmed with the voucher specimen, 15 species are associated with the preserved tissues, and one is confirmed with the morphometric data and key characteristics. Based on the species occurrence in the states of India and Myanmar, along with the habitat preference, an additional 83 species are yet to have occurred in Bangladesh. Among them, 38 species are categorized as Highly Probable, 33 species are Probable, and 10 species are Possible. We recommend bat surveys are urgent in Bangladesh using all complementary capture techniques that will contribute to voucher specimen collections and confirm the presence of bats. In addition, echolocation calls of bats can help establish call libraries.

Abstract

Following an outbreak of violence on 25 August 2017 in Rakhine State, Myanmar, a new massive influx of Rohingya refugees to Cox’s Bazar, Bangladesh started in late August 2017. Most of the Rohingya refugees settled in Ukhia and Teknaf Upazilas of Cox’s Bazar, a district bordering Myanmar identified as the main entry area for border crossings.

This dataset presents the result of the NPM Round 12 Site Assessment exercise, which collected information related to the Rohingya refugee population distribution and needs during the months of August, September and October 2018.

• The data collection for NPM baseline survey was conducted between 9 August and 4 September 2018: it provides an update about the population distribution and movements.
• The data collection for NPM Site Assessment survey was conducted between 23 September and 10 October 2018: in addition to an update about the population figures, this includes a multi-sectoral needs assessment.

The full maps and GIS packages by camp produced based on NPM Baseline and Site Assessment 12 are available at the links below:

• Please click here to access the data by camp as of September/October 2018.
• Please click here to access the data by camp as of August 2018.

Rohingya refugee population distribution by para in Teknaf upazila. - Please click here.

Geographic coverage

Bangladesh

Kind of data

Observation data/ratings [obs]

Summary of datasets and their specific characteristics utilized in this study. The table outlines the spatial, temporal, and thematic attributes of each dataset employed for analyzing the vulnerability and impacts of cyclones in the central coastal districts of Bangladesh. These datasets encompass satellite imagery, administrative boundaries, demographic data, and historical cyclone records, providing a comprehensive foundation for geospatial analysis and risk assessment.

The coastal areas of Bangladesh are recognized as a major South Asian center for cyclone landfall. This research develops a comprehensive tropical cyclone mapping strategy utilizing the Fuzzy Analytical Hierarchy Process (FAHP) and geospatial techniques to analyze the vulnerability distribution in the central coastal regions of Bangladesh. Eighteen spatial features, categorized into physical, social, and mitigation capacity criteria, were assessed to evaluate vulnerability. The output indicates that the southern peripheral districts- Bhola, Borgona, and Patuakhali are more vulnerable to tropical cyclones due to factors such as historical cyclone tracks, proximity to the coastline, low elevation, gentle slopes, high population density (including vulnerable groups such as females, the disabled, and agricultural workers), poor socioeconomic status, and land covers (crops and vegetations) prone to damage. Mitigation measures in these areas, including cyclone warnings, embankments, and access to shelters and road networks, are found to be inadequate. Validation through ROC and AUC confirms the accuracy of vulnerability maps. These findings offer critical insights for policymakers, local NGOs, and local administrators to enhance cyclone preparedness and develop targeted mitigation strategies to reduce vulnerability in coastal Bangladesh.