The population density in Bangladesh reached its highest in 2020, amounting to approximately 1.27 thousand people per square kilometer. The South Asian country was the tenth most densely populated country in the world in 2019. Within the Asia Pacific region, Bangladesh’s population density was only exceeded by Macao, Singapore, Hong Kong, and the Maldives. Overall, Asia had the highest population density in the world in 2018.

Population growth in Bangladesh

In 1971, Bangladesh gained its independence from Pakistan. Bangladesh’s birth rate and mortality rate had declined significantly in the past years with a life expectancy of 72.59 years in 2019. In general, the population in Bangladesh had been growing at a slow pace, slightly fluctuating around an annual rate of one percent. This growth was forecasted to continue, although it was estimated to halve by 2040. As of today, Dhaka is the largest city in Bangladesh.

Population density explained

According to the source, “population density is the mid-year population divided by land area in square kilometers.” Further, “population is based on the de facto definition of population, which counts all residents.” Bangladesh’s population reached an estimated number of 164.69 million inhabitants in 2020. In 2018, the country’s land area amounted 130.2 thousand square kilometers.

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. Data and Resources TIFF Bangladesh - Population density (2015) DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid...

Bangladesh BD: Population Density: People per Square Km data was reported at 1,301.259 Person/sq km in 2022. This records an increase from the previous number of 1,287.999 Person/sq km for 2021. Bangladesh BD: Population Density: People per Square Km data is updated yearly, averaging 882.459 Person/sq km from Dec 1961 (Median) to 2022, with 62 observations. The data reached an all-time high of 1,301.259 Person/sq km in 2022 and a record low of 409.544 Person/sq km in 1961. Bangladesh BD: Population Density: People per Square Km data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bangladesh – Table BD.World Bank.WDI: Population and Urbanization Statistics. Population density is midyear population divided by land area in square kilometers. 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. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.;Food and Agriculture Organization and World Bank population estimates.;Weighted average;

Actual value and historical data chart for Bangladesh Population Density People Per Sq Km

Bangladesh BD: Population Density: Inhabitants per sq km data was reported at 1,301.260 Person in 2022. This records an increase from the previous number of 1,288.000 Person for 2021. Bangladesh BD: Population Density: Inhabitants per sq km data is updated yearly, averaging 1,124.730 Person from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 1,301.260 Person in 2022 and a record low of 857.600 Person in 1990. Bangladesh BD: Population Density: Inhabitants per sq km data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Bangladesh – Table BD.OECD.GGI: Social: Demography: Non OECD Member: Annual.

Bangladesh is largely ethnically homogeneous, and its name derives from the Bengali ethno-linguistic group which comprises 98% of the population. The Chittagong Hill Tracts, Sylhet, Mymensingh and North Bengal divisions are home to diverse indigenous peoples. There are many dialects of Bengali spoken throughout the region. The dialect spoken by those in Chittagong and Sylhet are particularly distinctive. In 2013 the population was estimated at 160 million. About 87% of Bangladeshis are Muslims, followed by Hindus (12%), Buddhists (1%) and Christians (0.5%).

Bangladesh has the highest population density in the world, excluding a handful of city-states and small countries with populations under 10m, such as Malta and Hong Kong.

Most of the demographic statistics below are from the Bangladesh Bureau of Statistics, World Bank and CIA World Fackbook, unless otherwise indicated.

In 1800, the population of the area of modern-day Bangladesh was estimated to be just over 19 million, a figure which would rise steadily throughout the 19th century, reaching over 26 million by 1900. At the time, Bangladesh was the eastern part of the Bengal region in the British Raj, and had the most-concentrated Muslim population in the subcontinent's east. At the turn of the 20th century, the British colonial administration believed that east Bengal was economically lagging behind the west, and Bengal was partitioned in 1905 as a means of improving the region's development. East Bengal then became the only Muslim-majority state in the eastern Raj, which led to socioeconomic tensions between the Hindu upper classes and the general population. Bengal Famine During the Second World War, over 2.5 million men from across the British Raj enlisted in the British Army and their involvement was fundamental to the war effort. The war, however, had devastating consequences for the Bengal region, as the famine of 1943-1944 resulted in the deaths of up to three million people (with over two thirds thought to have been in the east) due to starvation and malnutrition-related disease. As the population boomed in the 1930s, East Bengal's mismanaged and underdeveloped agricultural sector could not sustain this growth; by 1942, food shortages spread across the region, millions began migrating in search of food and work, and colonial mismanagement exacerbated this further. On the brink of famine in early-1943, authorities in India called for aid and permission to redirect their own resources from the war effort to combat the famine, however these were mostly rejected by authorities in London. While the exact extent of each of these factors on causing the famine remains a topic of debate, the general consensus is that the British War Cabinet's refusal to send food or aid was the most decisive. Food shortages did not dissipate until late 1943, however famine deaths persisted for another year. Partition to independence Following the war, the movement for Indian independence reached its final stages as the process of British decolonization began. Unrest between the Raj's Muslim and Hindu populations led to the creation of two separate states in1947; the Muslim-majority regions became East Pakistan (now Bangladesh) and West Pakistan (now Pakistan), separated by the Hindu-majority India. Although East Pakistan's population was larger, power lay with the military in the west, and authorities grew increasingly suppressive and neglectful of the eastern province in the following years. This reached a tipping point when authorities failed to respond adequately to the Bhola cyclone in 1970, which claimed over half a million lives in the Bengal region, and again when they failed to respect the results of the 1970 election, in which the Bengal party Awami League won the majority of seats. Bangladeshi independence was claimed the following March, leading to a brutal war between East and West Pakistan that claimed between 1.5 and three million deaths in just nine months. The war also saw over half of the country displaced, widespread atrocities, and the systematic rape of hundreds of thousands of women. As the war spilled over into India, their forces joined on the side of Bangladesh, and Pakistan was defeated two weeks later. An additional famine in 1974 claimed the lives of several hundred thousand people, meaning that the early 1970s was one of the most devastating periods in the country's history. Independent Bangladesh In the first decades of independence, Bangladesh's political hierarchy was particularly unstable and two of its presidents were assassinated in military coups. Since transitioning to parliamentary democracy in the 1990s, things have become comparatively stable, although political turmoil, violence, and corruption are persistent challenges. As Bangladesh continues to modernize and industrialize, living standards have increased and individual wealth has risen. Service industries have emerged to facilitate the demands of Bangladesh's developing economy, while manufacturing industries, particularly textiles, remain strong. Declining fertility rates have seen natural population growth fall in recent years, although the influx of Myanmar's Rohingya population due to the displacement crisis has seen upwards of one million refugees arrive in the country since 2017. In 2020, it is estimated that Bangladesh has a population of approximately 165 million people.

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.

Exploring City Population Data of Bangladesh

The dataset contains comprehensive information about various cities in Bangladesh, including their population statistics across different years. Analyzing this dataset offers valuable insights into the demographic trends, urban development, and population dynamics within Bangladesh.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1937611%2F35afefa82648f6e253e1fb63ffc8cf7d%2FOIG2.jpeg?generation=1708434102648245&alt=media" alt="">

Dataset Overview:

Source: The data was scraped from the webpage https://www.citypopulation.de/en/bangladesh/cities/ & https://en.wikipedia.org/wiki/Districts_of_Bangladesh **Content: **The dataset contains information about cities in Bangladesh, including their names, population, and other relevant demographic data. **Format: **The data is presented in a tabular format within an HTML table on the webpage.

• Cities: The dataset encompasses a diverse range of cities across Bangladesh, representing different regions, sizes, and administrative statuses.
• Population Trends: Population data is provided for multiple years, spanning from 1991 to 2022. This allows for a longitudinal analysis of population growth, migration patterns, and urbanization trends over time.
• Geographical Information: In addition to population statistics, the dataset includes details about the geographical area of each city in square kilometers, providing context for population density and spatial distribution.
• City Status: The dataset categorizes cities based on their administrative status, such as urban, rural, or special administrative regions, offering insights into the urban-rural divide and administrative structures within Bangladesh.
• Native Names: Native or local names of cities are included, reflecting the linguistic and cultural diversity of Bangladesh.
• Administrative Divisions: Information about the division to which each city belongs is included, offering insights into the administrative structure of Bangladesh.

Fields: The dataset likely includes fields such as:

1. Name: The official name of the city as recognized by administrative authorities.
2. Abbr.: The abbreviation or short form of the city name, often used for convenience or in informal contexts.
3. Division: The administrative division to which the city belongs.
4. Established: The status or classification of the city, indicating whether it is urban, rural, or possibly a special administrative region.
5. Native: The native or local name of the city, which may differ from the official name and is often used by residents.
6. Area (km2): The total land area of the city in square kilometers, providing information about its geographical size.
7. Population_1991: The population of the city as recorded in the year 1991, serving as a historical reference point for demographic changes.
8. Population_2001: The population of the city as recorded in the year 2001, allowing for comparison with earlier and later population data.
9. Population_2011: The population of the city as recorded in the year 2011, providing insight into population trends over time.
10. Population_2022: The population of the city as recorded in the year 2022, offering recent demographic information for analysis and decision-making.

These columns collectively offer a comprehensive view of the cities in Bangladesh, encompassing their names, status, native names, geographical dimensions, and population dynamics across multiple years.

Objective:

The objective of exploring this dataset is to gain a deeper understanding of the population dynamics and urban development patterns within Bangladesh. By analyzing population trends, demographic shifts, and geographical distributions, stakeholders can make informed decisions regarding infrastructure development, resource allocation, and urban planning initiatives.

Analytical Approach:

Analyzing the dataset may involve various analytical techniques, including:

• Descriptive Statistics: Calculating summary statistics such as mean, median, and standard deviation to understand the distribution of population, area, and population density among cities.

• Time Series Analysis: Examining population trends over time to identify growth rates, patterns, and fluctuations.

• Spatial Mapping: Visualizing population density and distribution across different regions of Bangladesh using maps and geographical information systems (GIS).

• Division-wise Analysis: Comparing population dynamics and urbanization trends across different administrative divisions to understand regional variations and disparities.

By employing these analytical approaches, stakeholders can derive meaningful insights from the dataset to support evidence-based decision-making and policy formulation.

Here I upload my all research data of COVID-19 outbreak in Bangladesh from 8th March to 30th July cumulative confirmed cases by districts wise. Here also population density by districts wise

The data set is a 2015 heat wave hazard, exposure and vulnerability data set in Dhaka, Bangladesh, with a spatial resolution of 30m and a temporal resolution of yearly. Heat wave hazard is an index to measure the severity of heat wave event, which is expressed by surface temperature; heat wave exposure refers to the degree that human, livelihood and economy may be adversely affected, which is expressed by nighttime lighting data, and population density. The population older than 65 and younger than 5 years old constitute vulnerable groups; heat wave vulnerability is a measure of increased / reduced risk in the environment. The distance from road / hospital and ambulance station / water body, NDVI, impervious layer and slum area are used to represent the vulnerability of high temperature heat wave. The data set has been proved by experts, which can provide support for regional high temperature heat wave risk assessment.

this graph was created in R:

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driven primarily by high birth rates in developing countries and advancements in healthcare. According to the United Nations, the global population surpassed 8 billion in 2023, marking a critical milestone in human history. This growth, however, is unevenly distributed across continents and countries, leading to varied population densities and urban pressures.

Surface area and population density play vital roles in shaping the demographic and economic landscape of each country. For instance, countries with large land masses such as Russia, Canada, and Australia have low population densities despite their significant populations, as vast portions of their land are sparsely populated or uninhabitable. Conversely, nations like Bangladesh and South Korea exhibit extremely high population densities due to smaller land areas combined with large populations.

Population density, measured as the number of people per square kilometer, affects resource availability, environmental sustainability, and quality of life. High-density areas face greater challenges in housing, infrastructure, and environmental management, often experiencing increased pollution and resource strain. In contrast, low-density areas may struggle with underdeveloped infrastructure and limited access to services due to the dispersed population.

Urbanization trends are another important aspect of these dynamics. As people migrate to cities seeking better economic opportunities, urban areas grow more densely populated, amplifying the need for efficient land use and sustainable urban planning. The UN reports that over half of the world’s population currently resides in urban areas, with this figure expected to rise to nearly 70% by 2050. This shift requires nations to balance population growth and density with sustainable development strategies to ensure a higher quality of life and environmental stewardship for future generations.

Through an understanding of population size, surface area, and density, policymakers can better address challenges related to urban development, rural depopulation, and resource allocation, supporting a balanced approach to population management and economic development.

Local weather influences the transmission of the dengue virus. Most studies analyzing the relationship between dengue and climate are based on relatively coarse aggregate measures such as mean temperature. Here, we include both mean temperature and daily fluctuations in temperature in modelling dengue transmission in Dhaka, the capital of Bangladesh. We used a negative binomial generalized linear model, adjusted for rainfall, anomalies in sea surface temperature (an index for El Niño-Southern Oscillation), population density, the number of dengue cases in the previous month, and the long term temporal trend in dengue incidence. In addition to the significant associations of mean temperature and temperature fluctuation with dengue incidence, we found interaction of mean and temperature fluctuation significantly influences disease transmission at a lag of one month. High mean temperature with low fluctuation increases dengue incidence one month later. Besides temperature, dengue incidence was also influenced by sea surface temperature anomalies in the current and previous month, presumably as a consequence of concomitant anomalies in the annual rainfall cycle. Population density exerted a significant positive influence on dengue incidence indicating increasing risk of dengue in over-populated Dhaka. Understanding these complex relationships between climate, population, and dengue incidence will help inform outbreak prediction and control.

n1: total number of animals sighted and marked on the first samplen2: total number of animals sighted on the second samplem: number of marked animals on the first sample that were re-sighted on the second sampleN is the estimated total population of dogs using Chapman estimates with 95% confidence interval (see Eqs 1 and 2 in the text).*Total estimated population is the sum of the estimate in each wardEstimation of the number of free-roaming dogs and dog density in Dhaka City, Bangladesh during January and March 2011.

this graph was created in OurDataWorld:

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Population growth is one of the most important topics we cover at Our World in Data.

For most of human history, the global population was a tiny fraction of what it is today. Over the last few centuries, the human population has gone through an extraordinary change. In 1800, there were one billion people. Today there are more than 8 billion of us.

But after a period of very fast population growth, demographers expect the world population to peak by the end of this century.

On this page, you will find all of our data, charts, and writing on changes in population growth. This includes how populations are distributed worldwide, how this has changed, and what demographers expect for the future. Geographical maps show us where the world's landmasses are; not where people are. That means they don't always give us an accurate picture of how global living standards are changing.

One way to understand the distribution of people worldwide is to redraw the world map – not based on the area but according to population.

This is shown here as a population cartogram: a geographical presentation of the world where the size of countries is not drawn according to the distribution of land but by the distribution of people. It’s shown for the year 2018.

As the population size rather than the territory is shown in this map, you can see some significant differences when you compare it to the standard geographical map we’re most familiar with.

Small countries with a high population density increase in size in this cartogram relative to the world maps we are used to – look at Bangladesh, Taiwan, or the Netherlands. Large countries with a small population shrink in size – look for Canada, Mongolia, Australia, or Russia.

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.

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

BackgroundThe Educational Development Index (EDI) is a critical tool for assessing and tracking the progress of education systems from local to national, and even global scales and needs to be chosen for every layer of the subnational boundaries to secure the basic human rights of the people. In reality, there are significant variations within the consecutive time breaks and the geographical boundaries that need to be examined. The authors aim to examine how the EDI relates to various spatiotemporal variables.Methods and MaterialsThis research is based on secondary data on literacy rates (EDI) from 64 districts of Bangladesh and 6 relevant variables over the period 2001 to 2021. The optimal model for the data was identified from Bayesian spatial-temporal modeling (Linear, Analysis of Variance (ANOVA), Autoregressive (AR1), and AR2) and the Markov Chain Monte Carlo (MCMC) method used to generate data about the prior and posterior realizations. To select the best model different model selection and validation criteria such as the Deviance Information Criterion (DIC), Watanabe-Akaike information criterion (WAIC), and Root Mean Square Error (RMSE) were employed in this study.ResultsThe ‘AR1’ model is a ‘temporal model’ performed better than others. Significant spatial (=0.994) and temporal (=0.347) variations were identified for the suited model. Of the factors considered for model fitting, the health index, income index, expected years of schooling, population density, and dependency ratio are found to be important components of educational development in Bangladesh.ConclusionThe variation in the spatial domain can be used to identify the districts to improve the educational index controlling responsible factors by the policymakers.

Description This dataset contains detailed real estate listings from the Chittagong Division, Bangladesh, collected in August 2025. It includes key property attributes such as location, price, size, number of bedrooms, number of bathrooms, and additional features. The data is clean, structured, and ready for analysis, making it ideal for machine learning, market trend analysis, and investment research.

Dataset Highlights 1. Region: Chittagong Division, Bangladesh 2. Date Range: August 2025 3. Data Type: Tabular (XLSX format)

Fields Included:

• Property Location (city, area)
• Price (in BDT)
• Area (sq. ft.)
• Property Type (apartment, house, commercial)
• Bedrooms & Bathrooms count
• Additional property features

Possible Use Cases

1. Price Prediction Models: Build regression or machine learning models to forecast property values.
2. Market Trend Analysis: Identify emerging real estate trends in Chittagong.
3. Geospatial Insights: Map property distribution and pricing by location. 4.Comparative Studies: Compare Chittagong’s market with other regions in Bangladesh.

Why This Dataset is Valuable The Bangladeshi real estate market is rapidly growing, and the Chittagong Division is one of its most active hubs. Having structured, up-to-date, and region-specific property data enables analysts, developers, and researchers to make data-driven decisions with confidence.

Column Descriptions sku – Unique identifier for each property listing in the dataset.

price_value – Total listed price of the property (in Bangladeshi Taka).

category – Main property category, such as residential, commercial, or land.

subcategories – Specific property type within the main category (e.g., apartment, house, shop, plot).

floor_area_sqft – Floor area of the property in square feet (sq. ft.).

bedrooms – Number of bedrooms in the property (blank if not applicable, e.g., commercial plots).

bathrooms – Number of bathrooms in the property (blank if not applicable).

occupancy_status – Current occupancy state of the property, such as vacant or occupied.

geo_point – Combined latitude and longitude coordinates in the format longitude,latitude.

link_url – Direct link to the property listing on the source platform.

title – Short headline or title from the property listing.

address – Street name, neighborhood, or locality of the property.

description – Detailed property description as provided by the listing source.

longitude – Longitude coordinate of the property’s location.

latitude – Latitude coordinate of the property’s location.

price_per_sqft – Price of the property per square foot, calculated as price_value / floor_area_sqft.

invalid_data_flag – Data quality indicator:

0 = Valid entry

1 = Potentially invalid or incomplete entry

area_zone – Classified zone or region within Chittagong Division where the property is located.

nearest_hospital – Name of the closest hospital to the property.

dist_to_hospital_km – Distance from the property to the nearest hospital, in kilometers.

nearest_school – Name of the closest school to the property.

dist_to_school_km – Distance from the property to the nearest school, in kilometers.

nearest_shopping – Name of the closest shopping center, plaza, or market.

dist_to_shopping_km – Distance from the property to the nearest shopping area, in kilometers.

nearest_station – Name of the nearest public transportation hub (bus terminal or train station).

dist_to_station_km – Distance from the property to the nearest station, in kilometers.

Special Scoring Fields walkability_score – Measures how pedestrian-friendly the property location is (0–1 scale):

0 = Poor walkability (very few amenities within walking distance)

0.5 = Moderate walkability (some amenities nearby)

1 = Excellent walkability (most amenities within walking distance)

population_density_band – Classification of the surrounding area’s population density:

Low = Sparse population, more open space

Medium = Balanced population density

High = Densely populated, urbanized area

competitive_price_score – Indicates how competitive the property’s price is compared to similar listings:

0 = Above market average (less competitive)

1 = At or below market average (more competitive)

popularity_score – Reflects public interest in the property based on engagement signals (0–1 scale):

0 = Low interest

0.5 = Moderate interest

1 = High interest

lead_hotness_score – Predicts the likelihood of generating buyer leads (0–1 scale):

Values closer to 0 = Low chance of generating leads

Values closer to 1 = High chance of generating leads

On the basis of the global tropical cyclone track dataset, the global disaster events and losses dataset, the global tide level observation dataset and DEM data, coastline distribution data, land cover information, population and other related data of the Belt and Road, indicators related to the vulnerability of storm surge in each unit are extracted and calculated using 100 meter grid as evaluation unit, such as population density, land cover type, etc. The comprehensive index of storm surge vulnerability is constructed, and the vulnerability index of storm surge is obtained by using the weighted method. Finally, the storm surge vulnerability index is normalized to 0-1, which can be used to evaluate the vulnerability level of storm surge in each assessment unit. The key nodes data set only contains 11 nodes which have risks (Chittagong port, Bangladesh; Kyaukpyu Port, Myanmar; Kolkata, India; Yangon Port, Myanmar; Karachi, Pakistan; Dhaka, Bangladesh; Mumbai, India; Hambantota Port, Sri Lanka; Bangkok, Thailand; China-Myanmar Oil and Gas Pipeline; Jakarta-Bandung High-speed Railway).