Dasymetric Mapping Research Articles

Improved Population Mapping for China Using the 3D Building, Nighttime Light, Points-of-Interest, and Land Use/Cover Data within a Multiscale Geographically Weighted Regression Model

Large-scale gridded population product datasets have become crucial sources of information for sustainable development initiatives. However, mainstream modeling approaches (e.g., dasymetric mapping based on Multiple Linear Regression or Random Forest Regression) do not consider the heterogeneity and multiscale characteristics of the spatial relationships between influencing factors and populations, which may seriously degrade the accuracy of the prediction results in some areas. This issue may be even more severe in large-scale gridded population products. Furthermore, the lack of detailed 3D human settlement data likewise poses a significant challenge to the accuracy of these data products. The emergence of the unprecedented Global Human Settlement Layer (GHSL) data package offers a possible solution to this long-standing challenge. Therefore, this study proposes a new Gridded Population Mapping (GPM) method that utilizes the Multiscale Geographically Weighted Regression (MGWR) model in conjunction with GHSL-3D Building, POI, nighttime light, and land use/cover datasets to disaggregate population data for third-level administrative units (districts and counties) in mainland China into 100 m grid cells. Compared to the WorldPop product, the new population map reduces the mean absolute error at the fourth-level administrative units (townships and streets) by 35%, 51%, and 13% in three test regions. The proposed mapping approach is poised to become a crucial reference for generating next-generation global demographic maps.

Mapping Population Densities and Waste Management Systems in Mombasa County: A Literature Review of GIS and Remote Sensing Techniques

Mombasa County is one of the five counties along the coastline of Kenya. Currently, Mombasa County has a human population of 1.2 million people, and the population is rapidly increasing through rural-urban immigration and natural births. However, the inadequacy of well-established waste management systems on the mainland and coastlines of the County is a threat to the well-being of the local residents. Poorly disposed of wastes both on land and at sea can lead to health problems not only for humans and terrestrial animals but also for aquatic animals such as fish, with the latter being one of the main sources of affordable proteins for poor coastal communities. This paper aimed to conduct a review of the spatial human population density of Mombasa County and the geospatial location of waste dumping sites and their proximity to settled areas. This literature review synthesized existing research on the application of Geographic Information Systems (GIS) and Remote Sensing techniques in mapping population densities and assessing waste management systems. The review began by examining studies that investigate population distribution patterns in urban and rural areas, utilizing GIS to analyze demographic data and satellite imagery. It explored methodologies used to estimate population densities, including dasymetric mapping, spatial interpolation, and land use classification techniques. Key findings were that limited studies have utilized GIS technologies to assess the population in Kenya. GRASP and Random Forest (RF) were the main techniques previously used to assess population densities in the rural Taita Hills area and along the coastal region. The population has exponentially increased in Mombasa County since the 1950s and is projected to increase further. Additionally, waste management in Mombasa County is majorly controlled by the county government. Ten geo-tagged waste collection spots were identified during the review, spread across the residential areas. In conclusion, the county should endeavor to employ GIS techniques to assess the rapid population change within the county and have targeted interventions to address the disparities in waste collection systems against population increase.

Strategies for cultivating powerful knowledge using geospatial technologies at the secondary education level

ABSTRACT The purpose of this study is to develop geospatial technology-based teaching and learning strategies for cultivating powerful knowledge in geography. Utilising the theoretical framework of the five types of powerful geographical knowledge proposed by Maude (2016; 2018), strategies were designed to enhance each type of knowledge. The learning modules developed for secondary school students in South Korea include exploring land cover change using ESRI StoryMaps, presenting emotional geographies as heat maps, and visualising population density via dasymetric mapping. This article presents practical teaching and learning tactics for promoting powerful knowledge based on geospatial technologies. In the context of a growing interest in the educational implications of powerful knowledge, this study proposes ways in which geography education can make a substantive contribution and play a significant role in enhancing the relevance of the subject within the curriculum.

Accessibility of TB diagnostic services at primary healthcare clinics in the eThekwini district, South Africa: a geospatial analysis

BackgroundImproving geographic access can aid in managing tuberculosis (TB) by enabling early diagnosis and treatment initiation. Although geospatial techniques have been used to map the transmission patterns of drug-resistant TB...

Incorporating spatial autocorrelation in dasymetric mapping: A hierarchical Poisson spatial disaggregation regression model

The growing demand for spatially detailed population products in various fields continues to rise, as users shift their focus from aggregated areal totals to high-resolution grid estimates. Aggregating demographic data to areas, such as census tracts or block groups, can mask localized heterogeneities within those areas. This paper presents a new pycnophylactic (density-preserving) geospatial model for disaggregating population to high-resolution grids. We describe a Bayesian Hierarchical Poisson Spatial Disaggregation Regression Model (HPSDRM), which incorporates land cover covariates and two levels of spatial autocorrelation. We evaluated the model's predictive ability first with simulation studies, and then by disaggregating census population data for Davidson County, TN, from the census tract-level to a fine grid and comparing predicted to actual block-level population counts. The interpolated population map successfully identified spatial heterogeneities, such as hot- and cold-spots within census tracts. The HPDSRM model out-performed three other types of disaggregation modeling, which suggests the value of incorporating spatial autocorrelation. Based upon this study, HPSDRM has potential for disaggregating other demographic data, such as socioeconomic indicators.

Investigating the effects of spatial scales on social vulnerability index: A hybrid uncertainty and sensitivity analysis approach combined with remote sensing land cover data.

Investigating the effects of spatial scales on the uncertainty and sensitivity analysis of the social vulnerability index (SoVI) model output is critical, especially for spatial scales finer than the census block group or census block. This study applied the intelligent dasymetric mapping approach to spatially disaggregate the census tract scale SoVI model into a 300-m grids resolution SoVI map in Davidson County, Nashville. Then, uncertainty analysis and variance-based global sensitivity analysis were conducted on two scales of SoVI models: (a) census tract scale; (b) 300-m grids scale. Uncertainty analysis results indicate that the SoVI model has better confidence in identifying places with a higher socially vulnerable status, no matter the spatial scales in which the SoVI is constructed. However, the spatial scale of SoVI does affect the sensitivity analysis results. The sensitivity analysis suggests that for census tract scale SoVI, the indicator transformation and weighting scheme are the two major uncertainty contributors in the SoVI index modeling stages. While for finer spatial scales like the 300-m grid's resolution, the weighting scheme becomes the uttermost dominant uncertainty contributor, absorbing uncertainty contributions from indicator transformation.

Urban–Rural Boundary Delineation Based on Population Spatialization: A Case Study of Guizhou Province, China

Rational delineation of urban–rural boundaries is a foundational prerequisite for holistic urban and rural development planning and rational resource allocation. However, using a single data source for urban–rural boundaries yields non-comprehensive results. To address this problem, the present study proposes a method for extracting urban–rural boundaries using multiple sources such as population data, nighttime light data, land use, and points of interest (POI) data. Considering Guizhou Province for a case study, this study presents a two-step method for identifying urban–rural boundaries. First, the random forest model was combined with the dasymetric mapping method to obtain the province’s population spatialization data with a 30-m resolution. Second, based on the spatialized population, the urban–rural boundary for Guizhou Province in 2020 was extracted using the breaking point method. This method comprehensively integrated the benefits of various data and judiciously extracted the boundaries of the main urban areas and small and medium-sized towns of each city in the study province at the same spatial scale. The stratified random sampling method revealed an average overall accuracy of 88.05%. The proposed method has high universality and application value and can be useful for accurate and practical identification of urban–rural boundaries.

An ANN-based method for population Dasymetric mapping to avoid the scale heterogeneity: A case study in Hong Kong, 2016–2021

A comprehensive understanding of population distribution is critical for assessing socio-economic issues. However, the widely used dasymetric mapping method relies on models built at a coarse administrative scale and estimates population at a fine-gridded scale. This difference in scale between the training and estimating domains results in significant heterogeneity in data distribution. To address this issue, we proposed a scale heterogeneity-avoided method based on artificial neural networks that can take population density as an independent variable and gridded properties, including remote sensing images, digital terrain models, road networks, building footprints, and land use, as dependent variables. Our experiments in Hong Kong in 2016 and 2021 showed significant advantages of the proposed method. Compared to commonly used methods, our approach demonstrated a 19.4% improvement in the root mean square error. Furthermore, the advantages of our method became more apparent at larger census units, and the accuracy of the pre-trained model for directly estimating population in other temporal phases was satisfactory. Among the geospatial data variables, land use was the most significant in accurately estimating population. Replacing land use data with random numbers led to a decrease in accuracy by over 89.0%, while other properties only resulted in decreases of 2.7% to 13.9%. We further investigated spatiotemporal changes in population distribution from 2016 to 2021, finding that population growth mainly occurred in new built-up areas, while larger population decreases occurred in old towns. Throughout the study period, the population tended to concentrate more, as the average population density increased while the median population density decreased.

Using Dasymetric Mapping Applications towards the Rapid Appraisal of Salt Marsh Blue Carbon Levels

Using Dasymetric Mapping Applications towards the Rapid Appraisal of Salt Marsh Blue Carbon Levels

Opportunities and Challenges of Remote Sensing, Geospatial Data, and Machine Learning in Obtaining Accessibility and Location Information for Sustainable Development in Indonesia

With the advancement of technologies so does the data collection method which creates a large, rapid, and diverse stream of data. Statistic Indonesia (BPS) has also encouraged to utilize this by starting to collect geospatial information on respondents and public facilities. To keep up with this a change needs to be made in processing methods to accommodate massive, high-dimensional, and multiform data collected in different forms such as machine learning. This progression also opens up a new opportunity for tackling various statistical data problems such as accessibility and location data. Remote sensing is one of the big data sources that undergoes a lot of changes shown in the high spatial and temporal resolution satellite imagery availability, together with the BPS geotagging data shows great promise in classifying land use and geospatial analysis. Even so, there are still some challenges in remote sensing as well as other geospatial data utilization. The goals of this review paper are to study the opportunities and challenges in utilizing remote sensing, geospatial data, and machine learning for accessibility and location information. In this paper, we explore the possibilities and limitations in its implementation into SDGs indicators that involve accessibility and location such as indicators 9.1.1, 11.1.1, 11.2.1, 11.3.1, and 11.7.1 including other variables needed for the calculation like access to public facilities. Moreover, our experiment using geotagging data shows potential in improving proportion estimation when compared to using a simple ratio. Our DEGURBA following the UN definition using machine learning LULC for dasymetric mapping also provides more insight compared to the existing data. We can conclude that there are great opportunities in applying remote sensing and other geospatial data to monitor the accessibility and location to further sustainable development in Indonesia.

Probabilistic mapping of life loss due to dam-break flooding

AbstractAssessment of flood damage caused by dam failures is typically performed deterministically on the basis of a single preselected scenario, neglecting uncertainties in dam-break parameters, exposure information, and vulnerability model. This paper proposes a probabilistic flood damage model for the estimation of life loss due to dam-break flooding with the aim of overcoming this limitation and performing a more comprehensive and informative evaluation of flood risk. The significant novelty lies in the fact that the model combines uncertainties associated with all three components of risk: hazard, exposure, and vulnerability. Uncertainty in flood hazard is introduced by considering a set of dam-break scenarios, each characterized by different breach widths and reservoir levels. Each scenario is linked to a probability, which is assumed conditional on the dam-break event. Uncertainty in exposure is accounted for using dasymetric maps of the population at risk for two socio-economic states (representing business and non-business hours of a typical week), along with associated likelihood. Vulnerability to flooding is described through a well-established empirical hazard-loss function relating the fatality rate of the population at risk to the flood hazard, the flood severity understanding, and the warning time; a confidence band provides quantitative information about the associated uncertainty. The probabilistic damage model was applied to the case study of the hypothetical collapse of Mignano concrete gravity dam (northern Italy). The main outcome is represented by probabilistic flood damage maps, which show the spatial distribution of selected percentiles of a loss-of-life risk index coupled with the corresponding uncertainty bounds.

Disaggregation of the Copernicus Land Use/Land Cover (LULC) and Population Density Data to Fit Mesoscale Flood Risk Assessment Requirements in Partially Urbanized Catchments in Croatia

Flood risk assessment at the mesoscale requires data that are spatially and thematically detailed enough to provide reliable estimates at the catchment level. However, data availability and suitability are often contradictory: available data are rarely suitable at the required level of detail. To overcome this problem, numerous disaggregation methods have been proposed in recent decades, often based on somewhat generalised imperviousness characteristics derived from the available urban land use/land cover (LULC) nomenclature. To reduce generalisation, we propose a new disaggregation approach using a spatially distributed imperviousness density (IMD) layer at a very detailed spatial resolution of 10 m as ancillary data to improve the thematic detail of the urban classes of the available LULC datasets (Coastal Zones, Natura 2000) and the dasymetric mapping of the census data. The nomenclature of the urban classes and the impervious density thresholds were taken from the detailed Urban Atlas dataset. The disaggregation of the census data is then built on the resulting geometry of thematically improved residential classes. Assuming that IMD values indicate a built-up density, the proposed weighting scheme is IMD-dependent: it accounts for variability in the built-up density and, hence, variability in population. The approach was tested in three catchments in Croatia, each with a different degree of urbanisation. The resulting statistics (mean square error and percentage error) indicate that residential areas and population density depend on IMD. Using IMD as additional data therefore greatly improves the assessment of elements that are exposed to flooding and, consequently, the damage and flood risk assessment.

Characterizing Spatial Accessibility of COVID-19 Testing and Vaccination Sites using Dasymetric Mapping and GIS

Abstract. The COVID-19 pandemic accentuated existing health disparities in the U.S., inflicting staggeringly disproportionate rates of exposure, infection and death among racial and ethnic minority populations. Early evidence in 2020 had suggested that testing locations were not equitably accessible to racial/ethnic minority populations, and thus, increased the likelihood of unnoticed virus spread in minority communities. Additionally, racial/ethnic minority populations have been less likely to be vaccinated for COVID-19 as of February 2021, when vaccination had just started rolling out. In this study, we use Intelligent Dasymetric Mapping (IDM) and GIS Service Area analysis to examine spatial access to testing and vaccination sites in the Denver metropolitan area in January 2021, when testing and vaccination access was limited and competitive. Our results indicate that the racial/ethnic composition in total service areas with short walking- or driving-travel times was similar to the expected racial/ethnic composition of the Denver metro area, with minority populations slightly overrepresented in the total service areas. To ensure equitable vaccination and testing in minority communities in the future, in addition to ensuring spatial access, policymakers and health officials should increase community outreach, establish trust, and lower potential costs. Our study demonstrates the value of IDM in studying public health access.

Spatial patterns in pollution discharges from livestock and poultry farm and the linkage between manure nutrients load and the carrying capacity of croplands in China

The rapid development of livestock and poultry farming in China has resulted in an increasing threat of water pollution. In particular, mitigating livestock-related pollutant discharges is a key issue for environmental sustainability, especially for inland surface water bodies. In order to ensure the effective control of pollution and the efficient utilization management of livestock manure, spatially explicit surveys of pollutant generation and discharge from the livestock sector must be performed. In the present study, we estimated the grid cell-level distributions in the generation and discharge of four typical pollutants (chemical oxygen demand, ammonium nitrogen, total nitrogen and total phosphorus) from the livestock sector across the country with a spatial resolution of 30 arc-seconds. The distributions were estimated using the most recent pollution source census data and multi-sourced ancillary materials by a dasymetric mapping approach. We further investigated the feasibility of the resource utilization of livestock manure by comparing manure-source nutrients with the carrying capacity of adjacent croplands. Our results show that low-intensive farming generated and discharged the majority of livestock farming pollution, with other cattle and pigs breeding identified as the two major sources of pollution from the livestock sector. Southwest, Central and East China suffered the highly densified pollutants generation and discharges. Furthermore, cropland exceeding its carrying capacity was concentrated in these regions. Our findings provide additional insights into livestock and poultry farming in the context of relocation, strengthening regulation, transforming breeding operations, and rationalizing the resource use of manure, all of which are important measures for the sustainable development of both agriculture and the environment.

Benefits of using address-based dasymetric mapping in micro-level census disaggregation

Abstract. Dasymetric mapping is a well-known technique when attempting to refine census data spatially and/or temporally. Existing approaches in micro-level census disaggregation make use of building areas or volumes in the mapping process. In an empirical error comparison it is shown that using additional address data rather than only building footprints or 3D models can substantially reduce dislocation of residential population. We propose the use of address points as a geometric representation unit for a more refined census disaggregation method in the future.

Spatial Population Distribution Data Disaggregation Based on SDGSAT-1 Nighttime Light and Land Use Data Using Guilin, China, as an Example

A high-resolution population distribution map is crucial for numerous applications such as urban planning, disaster management, public health, and resource allocation, and it plays a pivotal role in evaluating and making decisions to achieve the UN Sustainable Development Goals (SDGs). Although there are many population products derived from remote sensing nighttime light (NTL) and other auxiliary data, they are limited by the coarse spatial resolution of NTL data. As a result, the outcomes’ spatial resolution is restricted, and it cannot meet the requirements of some applications. To address this limitation, this study employs the nighttime light data provided by the SDGSAT-1 satellite, which has a spatial resolution of 10 m, and land use data as auxiliary data to disaggregate the population distribution data from WorldPop data (100 m resolution) to a high resolution of 10 m. The case study conducted in Guilin, China, using the multi-class weighted dasymetric mapping method shows that the total error during the disaggregation is 0.63%, and the accuracy of 146 towns in the study area is represented by an R2 of 0.99. In comparison to the WorldPop data, the result’s information entropy and spatial frequency increases by 345% and 1142%, respectively, which demonstrates the effectiveness of this approach in studying population distributions with high spatial resolution.

Assessing spatial variation in water supply of a city using dasymetric mapping

Abstract Wastewater has the potential to act as a supplement when freshwater resources are limited. To utilize this, city administrators need to identify the type and quantity of wastewater generated and its various functional uses. In countries like India, where detailed data about wastewater is not available, any potential reuse proposal is difficult to design and execute. To cover this gap, this research proposes a technique using dasymetric mapping and Voronoi diagrams to estimate the quantity and spatial distribution of wastewater generated in a city, based on population data and water supply information, by mapping and identifying the variation in the supply. The developed research framework is tested in Bhopal city in India. The results suggest that there is higher water consumption in areas of high population density, whereas wastewater generation displays a greater variation based on land use. Among 217 Voronoi polygons created throughout the city, eight have wastewater generation at more than 10 litres/sq. m of area. The total wastewater generated while considering only municipal water supply was 148.32 MLD. On intersecting the Voronoi polygons with ward boundaries, it is found that six wards generate wastewater more than 50 litres/sq. m, whereas seven wards generate less than 1 litre/sq. m of area.

On Farmland and Floodplains—Modeling Urban Growth Impacts Based on Global Population Scenarios in Pune, India

Emerging megacities in the global south face unprecedented transformation dynamics, manifested in rapid demographic, economic, and physical growth. Anticipating the associated sustainability and resilience challenges requires an understanding of future trajectories. Global change models provide consistent high-level urbanization scenarios. City-scale urban growth models accurately simulate complex physical growth. Modeling approaches linking the global and the local scale, however, are underdeveloped. This work introduces a novel approach to inform a local urban growth model by global Shared Socioeconomic Pathways to produce consistent maps of future urban expansion and population density via cellular automaton and dasymetric mapping. We demonstrate the approach for the case of Pune, India. Three scenarios are explored until 2050: business as usual (BAU), high, and low urbanization. After calibration and validation, the BAU scenario yields a 55% growth in Pune’s population and 90% in built-up extent, entailing significant impacts: Pune’s core city densifies further with up to 60,000 persons/km2, adding pressure to its strained infrastructure. In addition, 66–70% more residents are exposed to flood risk. Half of the urban expansion replaces agriculture, converting 167 km2 of land. The high-urbanization scenario intensifies these impacts. These results illustrate how spatially explicit scenario projections help identify impacts of urbanization and inform long-term planning.

Toxic Tides and Environmental Injustice: Social Vulnerability to Sea Level Rise and Flooding of Hazardous Sites in Coastal California.

Sea level rise (SLR) and heavy precipitation events are increasing the frequency and extent of coastal flooding, which can trigger releases of toxic chemicals from hazardous sites, many of which are in low-income communities of color. We used regression models to estimate the association between facility flood risk and social vulnerability indicators in low-lying block groups in California. We applied dasymetric mapping techniques to refine facility boundaries and population estimates and probabilistic SLR projections to estimate facilities' future flood risk. We estimate that 423 facilities are at risk of flooding in 2100 under a high emissions scenario (RCP 8.5). One unit standard deviation increases in nonvoters, poverty rate, renters, residents of color, and linguistically isolated households were associated with a 1.5-2.2 times higher odds of the presence of an at-risk site within 1 km (ORs [95% CIs]: 2.2 [1.8, 2.8], 1.9 [1.5, 2.3], 1.7 [1.4, 1.9], 1.5 [1.2, 1.9], and 1.5 [1.2, 1.9], respectively). Among block groups near at least one at-risk site, the number of sites increased with poverty, proportion of renters and residents of color, and lower voter turnout. These results underscore the need for further research and disaster planning that addresses the differential hazards and health risks of SLR.

High-resolution livestock seasonal distribution data on the Qinghai-Tibet Plateau in 2020

Incorporating seasonality into livestock spatial distribution is of great significance for studying the complex system interaction between climate, vegetation, water, and herder activities, associated with livestock. The Qinghai-Tibet Plateau (QTP) has the world’s most elevated pastoral area and is a hot spot for global environmental change. This study provides the spatial distribution of cattle, sheep, and livestock grazing on the warm-season and cold-season pastures at a 15 arc-second spatial resolution on the QTP. Warm/cold-season pastures were delineated by identifying the key elements that affect the seasonal distribution of grazing and combining the random forest classification model, and the average area under the receiver operating characteristic curve of the model is 0.98. Spatial disaggregation weights were derived using the prediction from a random forest model that linked county-level census livestock numbers to topography, climate, vegetation, and socioeconomic predictors. The coefficients of determination of external cross-scale validations between dasymetric mapping results and township census data range from 0.52 to 0.70. The data could provide important information for further modeling of human-environment interaction under climate change for this region.