Modeling population density using land cover data

Assessing spatiotemporal population density dynamics from 2000 to 2020 in megacities using urban and rural morphologies

Rapid urbanization has resulted in the substantial population growth in metropolitan areas. However, existing research on population change of the cities predominantly draws on grid statistical data at the administrative level, overlooking the intra-urban variegation of population change. Particularly, there is a lack of attention given to the spatio-temporal change of population across different urban forms and functions. This paper therefore fills in the lacuna by clarifying the spatio-temporal characteristics of population growth in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 2000 to 2020 through the methods of local climate zone (LCZ) scheme and urban-rural gradients. The results showed that: (1) High population density was observed in the compact high-rise (LCZ 1) areas, with a noticeable decline along urban-rural gradients. (2) The city centers of GBA experienced the most significant population growth, while certain urban fringes and rural areas witnessed significant population shrinkage. (3) The rate of growth tended to slow down after 2010, but the uneven development of population-based urbanization was also noticeable, as urbanization and industrialization varied across different LCZ types and cities in GBA. This paper therefore contributes to a deeper understanding of population change and urbanization by clarifying their spatio-temporal contingences at landscape level.

Tracing the future of epidemics: Coincident niche distribution of host animals and disease incidence revealed climate-correlated risk shifts of main zoonotic diseases in China

Climate has critical roles in the origin, pathogenesis and transmission of infectious zoonotic diseases. However, large-scale epidemiologic trend and specific response pattern of zoonotic diseases under future climate scenarios are poorly understood. Here, we projected the distribution shifts of transmission risks of main zoonotic diseases under climate change in China. First, we shaped the global habitat distribution of main host animals for three representative zoonotic diseases (2, 6, and 12 hosts for dengue, hemorrhagic fever, and plague, respectively) with 253,049 occurrence records using maximum entropy (Maxent) modeling. Meanwhile, we predicted the risk distribution of the above three diseases with 197,098 disease incidence records from 2004 to 2017 in China using an integrated Maxent modeling approach. The comparative analysis showed that there exist highly coincident niche distributions between habitat distribution of hosts and risk distribution of diseases, indicating that the integrated Maxent modeling is accurate and effective for predicting the potential risk of zoonotic diseases. On this basis, we further projected the current and future transmission risks of 11 main zoonotic diseases under four representative concentration pathways (RCPs) (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) in 2050 and 2070 in China using the above integrated Maxent modeling with 1,001,416 disease incidence records. We found that Central China, Southeast China, and South China are concentrated regions with high transmission risks for main zoonotic diseases. More specifically, zoonotic diseases had diverse shift patterns of transmission risks including increase, decrease, and unstable. Further correlation analysis indicated that these patterns of shifts were highly correlated with global warming and precipitation increase. Our results revealed how specific zoonotic diseases respond in a changing climate, thereby calling for effective administration and prevention strategies. Furthermore, these results will shed light on guiding future epidemiologic prediction of emerging infectious diseases under global climate change.

Spatial distribution of rural population using mixed geographically weighted regression: Evidence from Jiangxi Province in China

On the basis of the spatial panel data of 2000, 2005, 2010, and 2015, this study uses a mixed geographically weighted regression model to explore the spatial distribution characteristics and influencing factors of the rural (permanent) population in Jiangxi Province, China. Results show that residents in the county area have a significant spatial positive autocorrelation, especially in the lake and mountain areas and the global Moran’ I index is more than 0.05. The influence of social and economic factors presents spatial homogeneity. The effect of urbanization and per capita disposable income is negative, whereas that of agricultural output value and rural electricity consumption is positive. The influence of climate factors presents spatial heterogeneity. The influence coefficient of rainfall in 2015 ranges from [-0.061, 0.133], which has a negative effect on the southwest mountain areas and a positive effect on the northeast lake areas., The influence coefficient of temperature in 2015 ranges from [-0.110, 0.094], which has a positive effect on the southwest mountain areas and a negative effect on the northeast lake areas. The influence coefficients of wind speed and relative humidity range from [-0.090, 0.153] and [-0.069, 0.130] in 2015 respectively, which further reinforce this effect. Therefore, scholars should pay attention to the universal adaptability of economic and social factors. Moreover, they should consider the spatial difference of climatic factors to promote urbanization following the local conditions. Finally, policymakers and concerned non-governmental institutions should fully understand the sensitivity of the rural population in underdeveloped mountain areas to climate factors to promote their rational distribution.

Spatial distribution of rural population from a climate perspective: Evidence from Jiangxi Province in China

The research on rural population distribution from a climate perspective is rare. Therefore, this study adopts this perspective and uses the ordinary least squares and spatial econometric models to explore the spatial distribution characteristics of the rural population in the Poyang Lake ecological economic zone. Results show that (1) a significant spatial autocorrelation is present in the distribution of rural population, and a spatial correlation exists between the population distribution and climatic factors, (2) the influence of climatic factors on the distribution of rural population in the Poyang Lake ecological economic zone is greater than that of economic factors, and (3) the annual average sunshine and annual average rainfall have a significant negative effect on the distribution of the regional rural population, which is contrary to the expectations., so we then analyze this negative effect on the regional rural population distribution. It is found that (1) the influence of climate factors on the distribution of rural population in lake area is far more than that of economic factors, and more consideration should be given to the influence of climate factors on the population distribution in the lake area, (2) different geographical capital and natural resource endowment, the influence of climate on micro-regional population distribution may be different from the general law, (3) the spatial measurement model which takes spatial dependence into account can reveal the influence of climate on rural population distribution more accurately.

Modeling Spatiotemporal Population Changes by Integrating DMSP-OLS and NPP-VIIRS Nighttime Light Data in Chongqing, China

The sustained growth of non-farm wages has led to large-scale migration of rural population to cities in China, especially in mountainous areas. It is of great significance to study the spatial and temporal pattern of population migration mentioned above for guiding population spatial optimization and the effective supply of public services in the mountainous areas. Here, we determined the spatiotemporal evolution of population in the Chongqing municipality of China from 2000–2018 by employing multi-period spatial distribution data, including nighttime light (NTL) data from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). There was a power function relationship between the two datasets at the pixel scale, with a mean relative error of NTL integration of 8.19%, 4.78% less than achieved by a previous study at the provincial scale. The spatial simulations of population distribution achieved a mean relative error of 26.98%, improved the simulation accuracy for mountainous population by nearly 20% and confirmed the feasibility of this method in Chongqing. During the study period, the spatial distribution of Chongqing’s population has increased in the west and decreased in the east, while also increased in low-altitude areas and decreased in medium-high altitude areas. Population agglomeration was common in all of districts and counties and the population density of central urban areas and its surrounding areas significantly increased, while that of non-urban areas such as northeast Chongqing significantly decreased.

A research framework of land use transition in Suzhou City coupled with land use structure and landscape multifunctionality

Under the premise of facing land-use sustainable development goals, clarifying the process and trend of regional land-use transition (LUT) is of considerable significance to the direction of national land-use optimization in the future. Suzhou City is not only an economically developed area in China but also a leading area of economic transformation and development, which embodies the changing process of regional development path since China's reform and opening up. This paper constructed an integrated research framework of micro-individual land use structure and macro-mixed landscape multifunctionality. It used spatial analysis technology to deeply analyze the LUT process of Suzhou, and quantified change characteristics of land use structure and function in Suzhou from 2000 to 2015. For structure, Suzhou has undergone a large-scale transition during the study period, mainly from farmland to construction land, in which transition speed and degree are at a high level until the trend slows down after 2010. For function, the number of high values of landscape multifunctionality gradually increases. Still, the scope of high-value areas progressively reduces by urban expansion constraints; the multifunctionality around urban expansion area gradually weakens. Besides, forest land, grassland, and other ecological land have the most significant number of land use functions. The comprehensive transition of land use structure and function can give a summary as a circle-layer dynamic change process of urban development. Transition hotspots can be divided into five specific regions of land management and finally realize comprehensive development zoning of urban and rural areas at the township level. LUT research framework based on structure-function coupling will provide ideas for land management mode transformation and contribute to sustainable land spatial planning strategy formulation.

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.

The Random Forest-Based Method of Fine-Resolution Population Spatialization by Using the International Space Station Nighttime Photography and Social Sensing Data

Despite the importance of high-resolution population distribution in urban planning, disaster prevention and response, region economic development, and improvement of urban habitant environment, traditional urban investigations mainly focused on large-scale population spatialization by using coarse-resolution nighttime light (NTL) while few efforts were made to fine-resolution population mapping. To address problems of generating small-scale population distribution, this paper proposed a method based on the Random Forest Regression model to spatialize a 25 m population from the International Space Station (ISS) photography and urban function zones generated from social sensing data—point-of-interest (POI). There were three main steps, namely HSL (hue saturation lightness) transformation and saturation calibration of ISS, generating functional-zone maps based on point-of-interest, and spatializing population based on the Random Forest model. After accuracy assessments by comparing with WorldPop, the proposed method was validated as a qualified method to generate fine-resolution population spatial maps. In the discussion, this paper suggested that without help of auxiliary data, NTL cannot be directly employed as a population indicator at small scale. The Variable Importance Measure of the RF model confirmed the correlation between features and population and further demonstrated that urban functions performed better than LULC (Land Use and Land Cover) in small-scale population mapping. Urban height was also shown to improve the performance of population disaggregation due to its compensation of building volume. To sum up, this proposed method showed great potential to disaggregate fine-resolution population and other urban socio-economic attributes.

Spatial landscape model to characterize biological diversity using R statistical computing environment

Due to urbanization and population growth, the degradation of natural forests and associated biodiversity are now widely recognized as a global environmental concern. Hence, there is an urgent need for rapid assessment and monitoring of biodiversity on priority using state-of-art tools and technologies. The main purpose of this research article is to develop and implement a new methodological approach to characterize biological diversity using spatial model developed during the study viz. Spatial Biodiversity Model (SBM). The developed model is scale, resolution and location independent solution for spatial biodiversity richness modelling. The platform-independent computation model is based on parallel computation. The biodiversity model based on open-source software has been implemented on R statistical computing platform. It provides information on high disturbance and high biological richness areas through different landscape indices and site specific information (e.g. forest fragmentation (FR), disturbance index (DI) etc.). The model has been developed based on the case study of Indian landscape; however it can be implemented in any part of the world. As a case study, SBM has been tested for Uttarakhand state in India. Inputs for landscape ecology are derived through multi-criteria decision making (MCDM) techniques in an interactive command line environment. MCDM with sensitivity analysis in spatial domain has been carried out to illustrate the model stability and robustness. Furthermore, spatial regression analysis has been made for the validation of the output.

Integration of small world networks with multi-agent systems for simulating epidemic spatiotemporal transmission

This study proposes an integrated model based on small world network (SWN) and multi-agent system (MAS) for simulating epidemic spatiotemporal transmission. In this model, MAS represents the process of spatiotemporal interactions among individuals, and SWN describes the social relation network among agents. The model is composed of agent attribute definitions, agent movement rules, neighborhoods, construction of social relation network among agents and state transition rules. The construction of social relation network and agent state transition rules is essential for implementing the proposed model. The decay effects of infection "memory", distance and social relation between agents are introduced into the model, which are unavailable in traditional models. The proposed model is used to simulate the transmission process of flu in Guangzhou City based on the swarm software platform. The integration model has better performance than the traditional SEIR model and the pure MAS based epidemic model. This model has been applied to the simulation of the transmission of epidemics in real geographical environment. The simulation can provide useful information for the understanding, prediction and control of the transmission of epidemics.

Modelling the effect of urbanization on the transmission of an infectious disease

This paper models the impact of urbanization on infectious disease transmission by integrating a CA land use development model, population projection matrix model and CA epidemic model in S-Plus. The innovative feature of this model lies in both its explicit treatment of spatial land use development, demographic changes, infectious disease transmission and their combination in a dynamic, stochastic model. Heuristically-defined transition rules in cellular automata (CA) were used to capture the processes of both land use development with urban sprawl and infectious disease transmission. A population surface model and dwelling distribution surface were used to bridge the gap between urbanization and infectious disease transmission. A case study is presented involving modelling influenza transmission in Southampton, a dynamically evolving city in the UK. The simulation results for Southampton over a 30-year period show that the pattern of the average number of infection cases per day can depend on land use and demographic changes. The modelling framework presents a useful tool that may be of use in planning applications.