Fine-Resolution Population Mapping from International Space Station Nighttime Photography and Multisource Social Sensing Data Based on Similarity Matching

A spatiotemporal deep learning approach for pedestrian crash risk prediction based on POI trip characteristics and pedestrian exposure intensity

Pedestrians represent a population of vulnerable road users who are directly exposed to complex traffic conditions, thereby increasing their risk of injury or fatality. This study first constructed a multidimensional indicator to quantify pedestrian exposure, considering factors such as Point of Interest (POI) attributes, POI intensity, traffic volume, and pedestrian walkability. Following risk interpolation and feature engineering, a comprehensive data source for risk prediction was formed. Finally, based on risk factors, the VT-NET deep learning network model was proposed, integrating the algorithmic characteristics of the VGG16 deep convolutional neural network and the Transformer deep learning network. The model involved training non-temporal features and temporal features separately. The training dataset incorporated features such as weather conditions, exposure intensity, socioeconomic factors, and the built environment. By employing different training methods for different types of causative feature variables, the VT-NET model analyzed changes in risk features and separately trained temporal and non-temporal risk variables. It was used to generate spatiotemporal grid-level predictions of crash risk across four spatiotemporal scales. The performance of the VT-NET model was assessed, revealing its efficacy in predicting pedestrian crash risks across the study area. The results indicated that areas with concentrated crash risks are primarily located in the city center and persist for several hours. These high-risk areas dissipate during the late night and early morning hours. High-risk areas were also found to cluster in the city center; this clustering behavior was more prominent during weekends compared to weekdays and coincided with commercial zones, public spaces, and educational and medical facilities.

Dynamic Assessment of the Impact of Flood Disaster on Economy and Population under Extreme Rainstorm Events

In the context of climate change and rapid urbanization, flood disaster loss caused by extreme rainstorm events is becoming more and more serious. An accurate assessment of flood disaster loss has become a key issue. In this study, extreme rainstorm scenarios with 50- and 100-year return periods based on the Chicago rain pattern were designed. The dynamic change process of flood disaster loss was obtained by using a 1D–2D coupled model, Hazard Rating (HR) method, machine learning, and ArcPy script. The results show that under extreme rainstorm events, the direct economic loss and affected population account for about 3% of the total GDP and 16% of the total population, respectively, and built-up land is the main disaster area. In addition, the initial time and the peak time of flood disaster loss increases with an increasing flood hazard degree and decreases with the increase in the return period. The total loss increases with the increase in the return period, and the unit loss decreases with the increase in the return period. Compared with a static assessment, a dynamic assessment can better reveal the development law of flood disaster loss, which has great significance for flood risk management and the mitigation of flood disaster loss.

Using big data for evaluating development outcomes: A systematic map

BACKGROUND

Policy makers need access to reliable data to monitor and evaluate the progress of development outcomes and targets such as sustainable development outcomes (SDGs). However, significant data and evidence gaps remain. Lack of resources, limited capacity within governments and logistical difficulties in collecting data are some of the reasons for the data gaps. Big data-that is digitally generated, passively produced and automatically collected-offers a great potential for answering some of the data needs. Satellite and sensors, mobile phone call detail records, online transactions and search data, and social media are some of the examples of big data. Integrating big data with the traditional household surveys and administrative data can complement data availability, quality, granularity, accuracy and frequency, and help measure development outcomes temporally and spatially in a number of new ways.The study maps different sources of big data onto development outcomes (based on SDGs) to identify current evidence base, use and the gaps. The map provides a visual overview of existing and ongoing studies. This study also discusses the risks, biases and ethical challenges in using big data for measuring and evaluating development outcomes. The study is a valuable resource for evaluators, researchers, funders, policymakers and practitioners in their effort to contributing to evidence informed policy making and in achieving the SDGs.

OBJECTIVES

Identify and appraise rigorous impact evaluations (IEs), systematic reviews and the studies that have innovatively used big data to measure any development outcomes with special reference to difficult contexts.

SEARCH METHODS

A number of general and specialised data bases and reporsitories of organisations were searched using keywords related to big data by an information specialist.

SELECTION CRITERIA

The studies were selected on basis of whether they used big data sources to measure or evaluate development outcomes.

DATA COLLECTION AND ANALYSIS

Data collection was conducted using a data extraction tool and all extracted data was entered into excel and then analysed using Stata. The data analysis involved looking at trends and descriptive statistics only.

MAIN RESULTS

The search yielded over 17,000 records, which we then screened down to 437 studies which became the foundation of our systematic map. We found that overall, there is a sizable and rapidly growing number of measurement studies using big data but a much smaller number of IEs. We also see that the bulk of the big data sources are machine-generated (mostly satellites) represented in the light blue. We find that satellite data was used in over 70% of the measurement studies and in over 80% of the IEs.

AUTHORS' CONCLUSIONS

This map gives us a sense that there is a lot of work being done to develop appropriate measures using big data which could subsequently be used in IEs. Information on costs, ethics, transparency is lacking in the studies and more work is needed in this area to understand the efficacies related to the use of big data. There are a number of outcomes which are not being studied using big data, either due to the lack to applicability such as education or due to lack of awareness about the new methods and data sources. The map points to a number of gaps as well as opportunities where future researchers can conduct research.

Extraction of urban built-up area based on the fusion of night-time light data and point of interest data

The accurate extraction of urban built-up areas is an important prerequisite for urban planning and construction. As a kind of data that can represent urban spatial form, night-time light data has been widely used in the extraction of urban built-up areas. As one of the geographic open-source big data, point of interest (POI) data has a high spatial coupling with night-time light data, so researchers are beginning to explore the fusion of the two data in order to achieve more accurate extraction of urban built-up areas. However, the current research methods and theoretical applications of the fusion of POI data and night-time light data are still insufficient compared with the dramatically changing urban built-up areas, which needed to be further supplemented and deepened. This study proposes a new method to fuse POI data and night-time light data. The results before and after data fusion are compared, and the accuracy of urban built-up area extracted by different data and methods is analysed. The results show that the data fusion can avoid the shortage of single data and effectively improve the extraction accuracy of urban built-up areas, which is greatly helpful to supplement the study of data fusion in urban built-up areas, and also can provide decision-making guidance for urban planning and construction.

The effect of population distribution measures on evaluating spatial accessibility of primary health-care institutions: A case study from China

Improvement of the equality of geographical allocation of limited health-care resources requires an accurate evaluation of spatial accessibility of the facilities. The adoption of appropriate population distribution measures is one of the leading factors affecting such an evaluation. Using primary health-care institutions in Hainan, China as an example, this study aimed to explore the disparities embedded in spatial accessibility evaluations based on six common measures of population distribution, namely community/ village population (VillagePop), average population distribution (AveragePop), population distribution by night-time light intensity (NighttimelightPop) together with the public population databases LandScan, WorldPop and PoiPop for construction of the weights. The enhanced two-step floating catchment area method, two-way analysis of variance (ANOVA), Dunnett test, root mean square error and the mean absolute error were employed to assess and compare spatial accessibilities based on these different population distribution measures. The spatial accessibility of primary health-care institutions in Hainan was found to vary when plotted using the various population distribution measures mentioned. As indicated by the statistical outcomes of both ANOVA and the Dunnett test, using the spatial accessibility calculated by VillagePop as reference, those calculated by AveragePop and PoiPop were found to be significantly different. In addition, the spatial accessibilities calculated by AveragePop and PoiPop demonstrated higher error rates in the identification of underserved areas compared with the reference. Considering the limitations of public population databases, the adoption of night-time light data is highly recommended for estimating population distribution in the absence of high-resolution data.

Mapping the Population Density in Mainland China Using NPP/VIIRS and Points-Of-Interest Data Based on a Random Forests Model

Understanding the spatial distribution of populations at a finer spatial scale has important value for many applications, such as disaster risk rescue operations, business decision-making, and regional planning. In this study, a random forest (RF)-based population density mapping method was proposed in order to generate high-precision population density data with a 100 m × 100 m grid in mainland China in 2015 (hereafter referred to as ‘Popi’). Besides the commonly used elevation, slope, Normalized Vegetation Index (NDVI), land use/land cover, roads, and National Polar Orbiting Partnership/Visible Infrared Imaging Radiometer Suite (NPP/VIIRS), 16,101,762 records of points of interest (POIs) and 2867 county-level censuses were used in order to develop the model. Furthermore, 28,505 township-level censuses (74% of the total number of townships) were collected in order to evaluate the accuracy of the Popi product. The results showed that the utilization of multi-source data (especially the combination of POIs and NPP/VIIRS data) can effectively improve the accuracy of population mapping at a finer scale. The feature importances of the POIs and NPP/VIIRS are 0.49 and 0.14, respectively, which are higher values than those obtained for other natural factors. Compared with the Worldpop population dataset, the Popi data exhibited a higher accuracy. The number of accurately-estimated townships was 19,300 (67.7%) in the Popi product and 16,237 (56.9%) in the Worldpop product. The Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were 14,839 and 7218, respectively, for Popi, and 18,014 and 8572, respectively, for Worldpop. The research method in this paper could provide a reference for the spatialization of other socioeconomic data (such as GDP).

Mapping the Urban Population in Residential Neighborhoods by Integrating Remote Sensing and Crowdsourcing Data

Where urban dwellers live at a fine scale is essential for the planning of services and response to city emergencies. Currently, most existing population mapping approaches considered census data as observational data for specifying models. However, census data usually have low spatial resolution and low frequency. Here, we presented a framework for mapping populations in residential neighborhoods with 30 m spatial resolution with little dependency upon census data. The framework integrated remote sensing and crowdsourcing data. The observational populations and number of households at residential neighborhood scale were obtained from real-time crowdsourcing data instead of census data. We tested our framework in Beijing. We found that (1) the number of households from a real estate trade platform could be a good proxy for accurate observational population. (2) The accuracy of the mapping population in residential neighborhoods was reasonable. The mean absolute percentage error was 47.26% and the R2 was 0.78. (3) Our framework shows great potential in mapping the population in real time. Our findings expand the knowledge in estimating urban population. In addition, the proposed framework and approach provide an effective means to quantify population distribution data for cities, which is particularly important for many of the cities worldwide lacking census data at the residential neighborhood scale.

Improving population mapping using Luojia 1-01 nighttime light image and location-based social media data

Fine-resolution population mapping, which is vital to urban planning, public health, and disaster management, has gained considerable attention in socioeconomic and environmental studies. Although population distribution has been considered highly correlated with urban facilities, the quantitative relationship between the two has yet to be revealed when considering huge heterogeneity. To address this problem, the present study proposed a novel population mapping method by adopting Luojia 1-01 nighttime light imagery, points of interest (POI), and social media check-in data. A grid-based attraction degree (AD) model was built to quantify the possibility of population concentration in each geographic unit with a comprehensive consideration of the distribution and the popularity of facilities. On the basis of kernel density estimation, 16 attraction indexes were extracted by matching POI and check-in data. Multiple variables were used to train a random forest model, through which fine-scale population mapping was conducted in Zhejiang, China. The comparison between demographic and WorldPop data proved the high accuracy of our approach (R² = 0.75 and 0.58). To explore the characteristics of the model further, the most appropriate search distance (650 m) and acquisition time (19:00-08:00) of the check-in data were discussed. The contrast experiment revealed that the model could outperform those from previous studies on rural and suburban areas with a few check-in points and low AD; thus, the mapping error caused by heterogeneity considerably decreased. The results indicated the proposed method has great potential in fine-scale population mapping.

Estimation of soil salt content by combining UAV-borne multispectral sensor and machine learning algorithms

Soil salinization is a global problem closely related to the sustainable development of social economy. Compared with frequently-used satellite-borne sensors, unmanned aerial vehicles (UAVs) equipped with multispectral sensors provide an opportunity to monitor soil salinization with on-demand high spatial and temporal resolution. This study aims to quantitatively estimate soil salt content (SSC) using UAV-borne multispectral imagery, and explore the deep mining of multispectral data. For this purpose, a total of 60 soil samples (0–20 cm) were collected from Shahaoqu Irrigation Area in Inner Mongolia, China. Meanwhile, from the UAV sensor we obtained the multispectral data, based on which 22 spectral covariates (6 spectral bands and 16 spectral indices) were constructed. The sensitive spectral covariates were selected by means of gray relational analysis (GRA), successive projections algorithm (SPA) and variable importance in projection (VIP), and from these selected covariates estimation models were built using back propagation neural network (BPNN) regression, support vector regression (SVR) and random forest (RF) regression, respectively. The performance of the models was assessed by coefficient of determination (R²), root mean squared error (RMSE) and ratio of performance to deviation (RPD). The results showed that the estimation accuracy of the models had been improved markedly using three variable selection methods, and VIP outperformed GRA and GRA outperformed SPA. However, the model accuracy with the three machine learning algorithms turned out to be significantly different: RF > SVR > BPNN. All the 12 SSC estimation models could be used to quantitatively estimate SSC (RPD > 1.4) while the VIP-RF model achieved the highest accuracy (R_c² = 0.835, R_P² = 0.812, RPD = 2.299). The result of this study proved that UAV-borne multispectral sensor is a feasible instrument for SSC estimation, and provided a reference for further similar research.

Improved Estimates of Population Exposure in Low-Elevation Coastal Zones of China

With sea level predicted to rise and the frequency and intensity of coastal flooding expected to increase due to climate change, high-resolution gridded population datasets have been extensively used to estimate the size of vulnerable populations in low-elevation coastal zones (LECZ). China is the most populous country, and populations in its LECZ grew rapidly due to urbanization and remarkable economic growth in coastal areas. In assessing the potential impacts of coastal hazards, the spatial distribution of population exposure in China’s LECZ should be examined. In this study, we propose a combination of multisource remote sensing images, point-of-interest data, and machine learning methods to improve the performance of population disaggregation in coastal China. The resulting population grid map of coastal China for the reference year 2010, with a spatial resolution of 100 × 100 m, is presented and validated. Then, we analyze the distribution of population in LECZ by overlaying the new gridded population data and LECZ footprints. Results showed that the total population exposed in China’s LECZ in 2010 was 158.2 million (random forest prediction) and 160.6 million (Cubist prediction), which account for 12.17% and 12.36% of the national population, respectively. This study also showed the considerable potential in combining geospatial big data for high-resolution population estimation.