Geospatial Datasets for Assessing Vulnerability of Bangladesh to Climate Change and Extremes
The present dataset provides necessary indicators of the climate change vulnerability of Bangladesh in raster form. Geospatial databases have been created in Geographic Information System (GIS) environment mainly from two types of raw data; socioeconomic data from the Bangladesh Bureau of Statistics (BBS) and biophysical maps from various government and non-government agencies. Socioeconomic data have been transformed into a raster database through the Inverse Distance Weighted (IDW) interpolation method in GIS. On the other hand, biophysical maps have been directly recreated as GIS feature classes and eventually, the biophysical raster database has been produced. 30 socioeconomic indicators have been considered, which has been obtained from the Bangladesh Bureau of Statistics. All socioeconomic data were incorporated into the GIS database to generate maps. However, the units of some variables have been adopted directly from BBS, some have been normalized based on population, and some have been adopted as percentages. 12 biophysical system indicators have also been classiﬁed based on the collected information from different sources and literature. Biophysical maps are mainly classified in relative scales according to the intensity. These geospatial datasets have been analyzed to assess the spatial vulnerability of Bangladesh to climate change and extremes. The analysis has resulted in a climate change vulnerability map of Bangladesh with recognized hotspots, significant vulnerability factors, and adaptation measures to reduce the level of vulnerability.
Steps to reproduce
Digitization, Database Creation, and Raster Conversion: Since spatial assessment of vulnerability is adopted in the present study, the base map of Bangladesh is firstly digitized along with its all-district centers. The socioeconomic data, collected from different BBS publications, are transformed into desired units of measurement and then incorporated into the GIS database. As the dataset is presented based on districts, the GIS database is also created basing district centers. On the other hand, all the biophysical data were collected from different published maps which were not corresponding to the district map of Bangladesh. Therefore, a new GIS database was created for biophysical indicators followed by the incorporation of a quantified scale derived from those reference maps. For the suitability of spatial analysis, all vector maps from created databases, both from socioeconomic and biophysical, were converted to raster datasets using ArcMap’s conversion toolbox. However, for GIS analysis, ArcGIS 10.5 desktop version is used in all over the present study. Normalization of the Indicators: Normalization is important for multivariate statistical analysis as some variables have a large range of variance and some of them have a small range of variance. To avoid the inﬂuence of one variable to other variables, the dataset has been normalized. For the normalization of raster datasets in ArcMap 10.5, a raster calculator was used which is a widely used tool under Map Algebra of the Arc toolbox. For each raster dataset, the following expression was used; (“x” - “x”.minimum)/(“x”.maximum – “x”.minimum) Where, x = Raster name. All of the created normalized raster data are then stored in a new database for further analysis.