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).

Percent and corresponding 95% confidence interval of OVC caregivers in different categories (ie, wealth quintiles) of household socioeconomic status (SES) at baseline and at follow-up, disaggregated by gender.

Frequency distribution of respondents at baseline and follow-up.

Baseline characteristics of OVC caregivers who were members and non-members of WORTH Yetu at the follow-up.

Abstract: This repository contains the full dataset and model implementation for the analysis of voting patterns in Romania's 2024 presidential elections, focusing on the relationship between territorial economic structures and electoral preferences. The models estimate vote dominance at LAU level using sectoral, demographic, and regional predictors, including spatial autoregression. Particular attention is given to the overrepresentation of Bucharest in national-level FDI statistics, which is corrected through a GDP-based imputation model. For reproducibility, the repository includes: Cleaned and structured input data (LAU, NUTS3), all modelling scripts in R, Tableau maps for visual analysis and public presentation.File DescriptionsLAU.csvThis dataset contains the local-level electoral and socio-economic data for all Romanian LAU2 units used in the spatial and statistical analyses. The file is used as the base for all models and includes identifiers for merging with the shapefile or spatial weights. It includes:- Electoral results by presidential candidate (2024, simulated),- Dominant vote type per locality,- Sectoral employment categories,- Demographic variables (ethnicity, education, age),- Regional and metropolitan classifications,- Weights for modelling.NUTS3.csvThis dataset provides county-level economic indicators (GDP and FDI) over the period 2011–2022. The file supports the construction of regional indicators such as FDI-to-GDP ratios and export structure. Notably, the file includes both original and corrected values of FDI for Bucharest, following the imputation procedure described in the model script.model.RThis R script contains the full modelling pipeline. The script includes both a model variant with Bucharest excluded and an alternative version using corrected FDI values, confirming the robustness of coefficients across specifications. It includes:- Pre-processing of LAU and NUTS3 data,- Imputation of Bucharest FDI using a linear model on GDP,- Survey-weighted logistic regression models for vote dominance per candidate,- Multinomial and hierarchical logistic models,- Seemingly Unrelated Regressions (SUR),- Spatial error models (SEM),- Principal Component Analysis on SEM residuals,- Latent dominance prediction using softmax transformation,- Export of predicted latent vote maps.Maps.twbxThis Tableau workbook contains all final cartographic representations.The workbook uses a consistent colour palette based on candidate-typified economic structures (industry, services, agriculture, shrinking).- Choropleth maps of dominant vote by candidate,- Gradients reflecting latent probabilities from spatial models,- Maps of residuals and ideological factor scores (PCA-derived),- Sectoral economic geographies per county and per locality,- Overlay of dominant vote and sectoral transformation types.

The purpose of this study was to investigate the impact of SEZ on indigenous peoples’ socioeconomic status and local development in the study area. A quantitative approach to analyzing the socioeconomics of treatment and control groups. A structured questionnaire was designed and a field survey was undertaken to collect primary data from respondents. This study used Principal Component Analysis (PCA) to create a socioeconomic index for two groups: those who sold their agricultural land and those who did not sell, and a two-sample independent t-test was used to determine the influence of SEZ on socioeconomic and local development. The results showed that the compensation amount for the acquired land not only improved the socio-economic living conditions of the indigenous population in short run, but also transformed their type of employment from agriculture to labor work, increased health expenditure, increased household wealth and minor changes in education expenditure and construction effected new houses, most of which is used for child marriage, vehicle purchased and dowary expenses in the special economic zone. This unproductive spending increases in the short term, which in the long run will convert skeikonicity into deprivation. Previous studies focused only on the geopolitics behind the geo-economy and the challenges and success factors for SEZs in Pakistan. This study is unique as it is the first attempt that uses statistical and economic tools to identify the positive and negative impacts of SEZ on the local development in the area. It makes an academic contribution to the literature to improve the knowledge of the effects of these special economic zones on the local development in any area.