A Novel Method for Simulating Urban Population Potential Based on Urban Patches: A Case Study in Jiangsu Province, China

A district-level susceptibility and vulnerability assessment of the COVID-19 pandemic's footprint in India

In this study, we trace the COVID-19 pandemic's footprint across India's districts. We identify its primary epicentres and the outbreak's imprint in India's hinterlands in four separate time-steps, signifying the different lockdown stages. We also identify hotspots and predict areas where the pandemic may spread next. Significant clusters in the country's western and northern parts pose risk, along with the threat of rising numbers in the east. We also perform epidemiological and socioeconomic susceptibility and vulnerability analyses, identifying resident populations that may be physiologically weaker, leading to a high incidence of cases and pinpoint regions that may report high fatalities due to ambient poor demographic and health-related factors. Districts with a high share of urban population and high population density face elevated COVID-19 risks. Aspirational districts have a higher magnitude of transmission and fatality. Discerning such locations can allow targeted resource allocation to combat the pandemic's next phase in India.

Modeling Population Density using a New Index Derived from Multi-Sensor Image Data

The detailed information about the spatial distribution of the population is crucial for analyzing economic growth, environmental change, and natural disaster damage. Using the nighttime light (NTL) imagery for population estimation has been a topic of interest in recent decades. However, the effectiveness of NTL data in population estimation has been impeded by some limitations such as the blooming effect and underestimation in rural regions. To overcome these limitations, we combine the NPP-VIIRS day/night band (DNB) data with normalized difference vegetation index (NDVI) and land surface temperature (LST) data derived from the moderate resolution imaging spectroradiometer (MODIS) onboard the Terra satellite, to create a new vegetation temperature light population index (VTLPI). A statistical model is developed to predict 250m grid-level population density based on the proposed VTLPI and the least square regression approach. After that, a case study is implemented using the data of Sichuan Province, China in 2015, and the results indicates that the VTLPI-estimated population density outperformed the results from other two methods based on nighttime light imagery or human settlement index, and the three publicized population products, LandScan, WorldPop, and GPW. When using the census data as reference, the mean relative error and median absolute relative error on a township level are 0.29 and 0.12, respectively, and the root-mean-square error is 212 persons/km2. The results show that our VTLPI-based model can achieve a better estimation of population density in rural areas and urban suburbs and characterize more spatial variations at 250m grid level both in both urban and rural areas. The resultant population density offers better population exposure data for assessing natural disaster risk and loss as well as other related applications.

Dasymetric mapping of urban population in China based on radiance corrected DMSP-OLS nighttime light and land cover data

High spatial resolution urban population dataset is increasingly required for sustainable urban planning and management. Dasymetric mapping is an effective approach to create such dataset. However, the created gridded total population datasets usually have limitation for urban analysis in developing countries as they usually underestimate urban population because of the strong urban-rural difference. In this study, we aimed to create a dataset of gridded urban population with 1 km resolution in China in year 2000 and 2010. We proposed an index of urban nighttime light (UNTL) by integrating radiance corrected DMSP nighttime light (RcNTL) and urban land, which is then used as weight to disaggregate county-level urban population. The validation using township population in Beijing as references shows reasonable accuracy with a mean relative error of 38% and a R² of 68%. Using only two widely available datasets (RcNTL and urban land), the proposed method is simple and computing efficient compared with methods using multiple geospatial data (e.g., land use and land cover, distance to city center, slope) and that combined with remote sensing imagery. As the used two auxiliary datasets are accessible globally, the method has great potential to produce similar urban population dataset for other developing countries where fine scale census population datasets are scarce. The produced urban population dataset is valuable for enriching our understanding of the urbanization process and designing sustainable urban planning and management strategies in China.

Research on Grid Size Suitability of Gridded Population Distribution in Urban Area: A Case Study in Urban Area of Xuanzhou District, China

The research on the grid size suitability is important to provide improvement in accuracies of gridded population distribution. It contributes to reveal the actual spatial distribution of population. However, currently little research has been done in this area. Many well-modeled gridded population dataset are basically built at a single grid scale. If the grid cell size is not appropriate, it will result in spatial information loss or data redundancy. Therefore, in order to capture the desired spatial variation of population within the area of interest, it is necessary to conduct research on grid size suitability. This study summarized three expressed levels to analyze grid size suitability, which include location expressed level, numeric information expressed level, and spatial relationship expressed level. This study elaborated the reasons for choosing the five indexes to explore expression suitability. These five indexes are consistency measure, shape index rate, standard deviation of population density, patches diversity index, and the average local variance. The suitable grid size was determined by constructing grid size-indicator value curves and suitable grid size scheme. Results revealed that the three expressed levels on 10m grid scale are satisfying. And the population distribution raster data with 10m grid size provide excellent accuracy without loss. The 10m grid size is recommended as the appropriate scale for generating a high-quality gridded population distribution in our study area. Based on this preliminary study, it indicates the five indexes are coordinated with each other and reasonable and effective to assess grid size suitability. We also suggest choosing these five indexes in three perspectives of expressed level to carry out the research on grid size suitability of gridded population distribution.