Although numerous epidemiological studies have demonstrated a relationship between environmental factors and chronic diseases, there is a lack of comprehensive population health vulnerability assessment studies from the perspective of environmental exposure, population sensitivity and adaptation on a regional scale. To address this gap, this study focused on six high-mortality chronic diseases in China and constructed an exposure–sensitivity–adaptability framework-based index system using multivariate data. The constructed system effectively estimated health vulnerability for the chronic diseases. The R-square between vulnerability and mortality rates for respiratory diseases and malignant tumors exceeded 0.7 and was around 0.6 for the other four chronic diseases. In 2020, Chongqing exhibited the highest vulnerability to respiratory diseases. For heart diseases, vulnerability values exceeding 0.5 were observed mainly in northern and northeastern provinces. Vulnerability values above 0.5 were observed in Jiangsu, Shanghai, Tianjin, Shandong and Liaoning for cerebrovascular diseases and malignant tumors. Shanghai had the highest vulnerability to endogenous metabolic diseases, and Tibet exhibited the highest vulnerability to digestive system diseases. The main related factor analysis results show that high temperature and humidity, severe temperature fluctuations, serious air pollution, high proportion of middle-aged and elderly population, as well as high consumption of aquatic products, red meat and eggs increased health vulnerability, while increasing per capita educational resources helped reduce vulnerability.
{"title":"Impact of Environmental Exposure on Chronic Diseases in China and Assessment of Population Health Vulnerability","authors":"Zhibin Huang, C. Cao, Min Xu, Xinwei Yang","doi":"10.3390/ijgi12040155","DOIUrl":"https://doi.org/10.3390/ijgi12040155","url":null,"abstract":"Although numerous epidemiological studies have demonstrated a relationship between environmental factors and chronic diseases, there is a lack of comprehensive population health vulnerability assessment studies from the perspective of environmental exposure, population sensitivity and adaptation on a regional scale. To address this gap, this study focused on six high-mortality chronic diseases in China and constructed an exposure–sensitivity–adaptability framework-based index system using multivariate data. The constructed system effectively estimated health vulnerability for the chronic diseases. The R-square between vulnerability and mortality rates for respiratory diseases and malignant tumors exceeded 0.7 and was around 0.6 for the other four chronic diseases. In 2020, Chongqing exhibited the highest vulnerability to respiratory diseases. For heart diseases, vulnerability values exceeding 0.5 were observed mainly in northern and northeastern provinces. Vulnerability values above 0.5 were observed in Jiangsu, Shanghai, Tianjin, Shandong and Liaoning for cerebrovascular diseases and malignant tumors. Shanghai had the highest vulnerability to endogenous metabolic diseases, and Tibet exhibited the highest vulnerability to digestive system diseases. The main related factor analysis results show that high temperature and humidity, severe temperature fluctuations, serious air pollution, high proportion of middle-aged and elderly population, as well as high consumption of aquatic products, red meat and eggs increased health vulnerability, while increasing per capita educational resources helped reduce vulnerability.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":"50 1","pages":"155"},"PeriodicalIF":0.0,"publicationDate":"2023-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86759812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Markus Schaffert, Konstantin Geist, Jonathan Albrecht, Dorothea Enners, Hartmut Müller
In this article, we describe the design of a method for measuring walkability and its application in two medium-sized cities in Germany. The method modifies the established Walk Score with regard to the needs of older people. While the original Walk Score takes a 2D approach by calculating the reachability of service facilities on a flat road network, we include 3D information by taking into account slopes and stairs. We also pay attention to the longer walking times of the elderly and adjust the selection and weighting of supply facilities according to their relevance for elderly people. The implementation results in a concentric walkability pattern, with a high Walk Score in the inner-city area that is decreasing towards the periphery, but with many anomalies resulting from local inhomogeneity in population and facility distribution and topography. The study shows that it is possible to refine the Walk Score to meet the needs of older people, as well as to implement the methodology in Germany using a combination of voluntary geographic information and high-quality official datasets. We see our research as a step forward on the way to more realistic walkability metrics for senior-sensitive urban planning.
{"title":"Walk Score from 2D to 3D - Walkability for the Elderly in Two Medium-Sized Cities in Germany","authors":"Markus Schaffert, Konstantin Geist, Jonathan Albrecht, Dorothea Enners, Hartmut Müller","doi":"10.3390/ijgi12040157","DOIUrl":"https://doi.org/10.3390/ijgi12040157","url":null,"abstract":"In this article, we describe the design of a method for measuring walkability and its application in two medium-sized cities in Germany. The method modifies the established Walk Score with regard to the needs of older people. While the original Walk Score takes a 2D approach by calculating the reachability of service facilities on a flat road network, we include 3D information by taking into account slopes and stairs. We also pay attention to the longer walking times of the elderly and adjust the selection and weighting of supply facilities according to their relevance for elderly people. The implementation results in a concentric walkability pattern, with a high Walk Score in the inner-city area that is decreasing towards the periphery, but with many anomalies resulting from local inhomogeneity in population and facility distribution and topography. The study shows that it is possible to refine the Walk Score to meet the needs of older people, as well as to implement the methodology in Germany using a combination of voluntary geographic information and high-quality official datasets. We see our research as a step forward on the way to more realistic walkability metrics for senior-sensitive urban planning.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":"1986 1","pages":"157"},"PeriodicalIF":0.0,"publicationDate":"2023-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87807090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaoyu Guo, Sui Zeng, Aihemaiti Namaiti, Jian Zeng
Determining whether the supply–demand matching (SDM) of urban public health resources is reasonable involves important issues such as health security and the rational use of resources. Using the central urban area of Tianjin as the research area, this paper used the Gaussian-based 2-step floating catchment area method (Ga2SFCA), combined with multi-source data, and comprehensively considered public medical, natural, and physical resources to evaluate the SDM of single-category and integrated public health resources in the research area. The results showed the following: (1) there was a good fit between supply and demand for resources related to public health and natural health in Tianjin’s central urban area. For resources related to public physical health, there was a poor fit between supply and demand; the population in the areas with insufficient supply and demand and scarce resources accounted for 82.78% of the total and was mainly distributed in the marginal areas of the four districts around the city and the six districts of the inner city. (2) For integrated public health resources, the degree of SDM was generally good. It had a circular structure that gradually shrank from the core to the edge. In order to promote the supply–demand balance of urban public health resources, this paper proposed three strategies involving three aspects: the supply, accessibility, and demand of urban public health resources. These strategies involve the service supply level, urban traffic network and slow traffic, development intensity, and population scale.
{"title":"Evaluation of Supply-Demand Matching of Public Health Resources Based on Ga2SFCA: A Case Study of the Central Urban Area of Tianjin","authors":"Xiaoyu Guo, Sui Zeng, Aihemaiti Namaiti, Jian Zeng","doi":"10.3390/ijgi12040156","DOIUrl":"https://doi.org/10.3390/ijgi12040156","url":null,"abstract":"Determining whether the supply–demand matching (SDM) of urban public health resources is reasonable involves important issues such as health security and the rational use of resources. Using the central urban area of Tianjin as the research area, this paper used the Gaussian-based 2-step floating catchment area method (Ga2SFCA), combined with multi-source data, and comprehensively considered public medical, natural, and physical resources to evaluate the SDM of single-category and integrated public health resources in the research area. The results showed the following: (1) there was a good fit between supply and demand for resources related to public health and natural health in Tianjin’s central urban area. For resources related to public physical health, there was a poor fit between supply and demand; the population in the areas with insufficient supply and demand and scarce resources accounted for 82.78% of the total and was mainly distributed in the marginal areas of the four districts around the city and the six districts of the inner city. (2) For integrated public health resources, the degree of SDM was generally good. It had a circular structure that gradually shrank from the core to the edge. In order to promote the supply–demand balance of urban public health resources, this paper proposed three strategies involving three aspects: the supply, accessibility, and demand of urban public health resources. These strategies involve the service supply level, urban traffic network and slow traffic, development intensity, and population scale.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":"41 1","pages":"156"},"PeriodicalIF":0.0,"publicationDate":"2023-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75356418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Weijie Li, Changxia Liang, Fan Yang, Bo Ai, Qingtong Shi, Guannan Lv
There are some limitations in traditional ocean scalar field visualization methods, such as inaccurate expression and low efficiency in the three-dimensional digital Earth environment. This paper presents a spherical volume-rendering method based on adaptive ray casting to express ocean scalar field. Specifically, the minimum bounding volume based on spherical mosaic is constructed as the proxy geometry, and the depth texture of the seabed terrain is applied to determine the position of sampling points in the spatial interpolation process, which realizes the fusion of ocean scalar field and seabed terrain. Then, we propose an adaptive sampling step algorithm according to the heterogeneous depth distribution and data change rate of the ocean scalar field dataset to improve the efficiency of the ray-casting algorithm. In addition, this paper proposes a nonlinear color-mapping enhancement scheme based on the skewness characteristics of the datasets to optimize the expression effect of volume rendering, and the transparency transfer function is designed to realize volume rendering and local feature structure extraction of ocean scalar field data in the study area.
{"title":"A Spherical Volume-Rendering Method of Ocean Scalar Data Based on Adaptive Ray Casting","authors":"Weijie Li, Changxia Liang, Fan Yang, Bo Ai, Qingtong Shi, Guannan Lv","doi":"10.3390/ijgi12040153","DOIUrl":"https://doi.org/10.3390/ijgi12040153","url":null,"abstract":"There are some limitations in traditional ocean scalar field visualization methods, such as inaccurate expression and low efficiency in the three-dimensional digital Earth environment. This paper presents a spherical volume-rendering method based on adaptive ray casting to express ocean scalar field. Specifically, the minimum bounding volume based on spherical mosaic is constructed as the proxy geometry, and the depth texture of the seabed terrain is applied to determine the position of sampling points in the spatial interpolation process, which realizes the fusion of ocean scalar field and seabed terrain. Then, we propose an adaptive sampling step algorithm according to the heterogeneous depth distribution and data change rate of the ocean scalar field dataset to improve the efficiency of the ray-casting algorithm. In addition, this paper proposes a nonlinear color-mapping enhancement scheme based on the skewness characteristics of the datasets to optimize the expression effect of volume rendering, and the transparency transfer function is designed to realize volume rendering and local feature structure extraction of ocean scalar field data in the study area.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":"399 1","pages":"153"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76323134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Siwaner Wang, Qian Sun, Pengfei Chen, Hui Qiu, Yang Chen
Since late 2019, the explosive outbreak of Coronavirus Disease 19 (COVID-19) has emerged as a global threat, necessitating a worldwide overhaul of public health systems. One critical strategy to prevent virus transmission and safeguard public health, involves deploying Nucleic Acid Testing (NAT) sites. Nevertheless, determining the optimal locations for public NAT sites presents a significant challenge, due to the varying number of sites required in different regions, and the substantial influences of population, the population heterogeneity, and daily dynamics, on the effectiveness of fixed location schemes. To address this issue, this study proposes a data-driven framework based on classical location-allocation models and bi-objective optimization models. The framework optimizes the number and location of NAT sites, while balancing various cost constraints and adapting to population dynamics during different periods of the day. The bi-objective optimization process utilizes the Knee point identification (KPI) algorithm, which is computationally efficient and does not require prior knowledge. A case study conducted in Shenzhen, China, demonstrates that the proposed framework provides a broader service coverage area and better accommodates residents’ demands during different periods, compared to the actual layout of NAT sites in the city. The study’s findings can facilitate the rapid planning of primary healthcare facilities, and promote the development of sustainable healthy cities.
{"title":"Location Scheme of Routine Nucleic Acid Testing Sites Based on Location-Allocation Models: A Case Study of Shenzhen City","authors":"Siwaner Wang, Qian Sun, Pengfei Chen, Hui Qiu, Yang Chen","doi":"10.3390/ijgi12040152","DOIUrl":"https://doi.org/10.3390/ijgi12040152","url":null,"abstract":"Since late 2019, the explosive outbreak of Coronavirus Disease 19 (COVID-19) has emerged as a global threat, necessitating a worldwide overhaul of public health systems. One critical strategy to prevent virus transmission and safeguard public health, involves deploying Nucleic Acid Testing (NAT) sites. Nevertheless, determining the optimal locations for public NAT sites presents a significant challenge, due to the varying number of sites required in different regions, and the substantial influences of population, the population heterogeneity, and daily dynamics, on the effectiveness of fixed location schemes. To address this issue, this study proposes a data-driven framework based on classical location-allocation models and bi-objective optimization models. The framework optimizes the number and location of NAT sites, while balancing various cost constraints and adapting to population dynamics during different periods of the day. The bi-objective optimization process utilizes the Knee point identification (KPI) algorithm, which is computationally efficient and does not require prior knowledge. A case study conducted in Shenzhen, China, demonstrates that the proposed framework provides a broader service coverage area and better accommodates residents’ demands during different periods, compared to the actual layout of NAT sites in the city. The study’s findings can facilitate the rapid planning of primary healthcare facilities, and promote the development of sustainable healthy cities.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":"18 1","pages":"152"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72862523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Belt and Road has developed rapidly in recent years. Constructing a comprehensive traffic network is conducive to promoting the development of the the Belt and Road. To optimize the layout of the Belt and Road comprehensive traffic network, this paper identifies important cities. First, a weighted super adjacency matrix is defined, which includes sea, air, railway transportation and trans-shipment transportation between these transportation modes. With this matrix, the Belt and Road comprehensive traffic network (B&RCTN) is constructed. To identify important node cities, this paper proposes a method to calculate multi-layer centrality which considers inter-layer relationships. With the results of the above four centrality indexes, the Entropy Weight TOPSIS is used to synthesize the evaluation of the four indexes. Finally, the multi-layer comprehensive centrality rank of node cities is obtained. Result shows that there are 72 important cities in B&RCTN. These important cities are mainly distributed in the east and west of Eurasia. Eastern cities are located in East Asia and Southeast Asia, including 36 cities such as Singapore, Shanghai, Guangzhou, Shenzhen and Hong Kong. Western cities are concentrated in West Asia, Western Europe and North Africa along the Mediterranean coast, including 31 cities such as Istanbul, Dubai, Vienna, Trieste and Koper. There are few important cities in central Eurasia, except Almaty in Central Asia and Colombo in South Asia. In addition, important cities also include Moscow in Eastern Europe, Lagos and Lome in West Africa. Finally, based on the distribution of important cities, this paper puts forward some suggestions on the development of the Belt and Road comprehensive transportation.
{"title":"Identify Important Cities in the Belt and Road Comprehensive Traffic Network","authors":"Fengjie Xie, Xiao Wang, Cuiping Ren","doi":"10.3390/ijgi12040154","DOIUrl":"https://doi.org/10.3390/ijgi12040154","url":null,"abstract":"The Belt and Road has developed rapidly in recent years. Constructing a comprehensive traffic network is conducive to promoting the development of the the Belt and Road. To optimize the layout of the Belt and Road comprehensive traffic network, this paper identifies important cities. First, a weighted super adjacency matrix is defined, which includes sea, air, railway transportation and trans-shipment transportation between these transportation modes. With this matrix, the Belt and Road comprehensive traffic network (B&RCTN) is constructed. To identify important node cities, this paper proposes a method to calculate multi-layer centrality which considers inter-layer relationships. With the results of the above four centrality indexes, the Entropy Weight TOPSIS is used to synthesize the evaluation of the four indexes. Finally, the multi-layer comprehensive centrality rank of node cities is obtained. Result shows that there are 72 important cities in B&RCTN. These important cities are mainly distributed in the east and west of Eurasia. Eastern cities are located in East Asia and Southeast Asia, including 36 cities such as Singapore, Shanghai, Guangzhou, Shenzhen and Hong Kong. Western cities are concentrated in West Asia, Western Europe and North Africa along the Mediterranean coast, including 31 cities such as Istanbul, Dubai, Vienna, Trieste and Koper. There are few important cities in central Eurasia, except Almaty in Central Asia and Colombo in South Asia. In addition, important cities also include Moscow in Eastern Europe, Lagos and Lome in West Africa. Finally, based on the distribution of important cities, this paper puts forward some suggestions on the development of the Belt and Road comprehensive transportation.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":"4 1","pages":"154"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79112689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The urban heat island (UHI) effect is an important topic for many cities across the globe. Previous studies, however, have mostly focused on UHI changes along either the spatial or temporal dimension. A simultaneous evaluation of the spatial and temporal variations is essential for understanding the long-term impacts of land cover on the UHI. This study presents the first evaluation and application of a newly developed spatiotemporal weighted regression framework (STWR), the performance of which was tested against conventional models including the ordinary least squares (OLS) and the geographically weighted regression (GWR) models. We conducted a series of simulation tests followed by an empirical study over central Phoenix, AZ. The results show that the STWR model achieves better parameter estimation and response prediction results with significantly smaller errors than the OLS and GWR models. This finding holds true when the regression coefficients are constant, spatially heterogeneous, and spatiotemporally heterogeneous. The empirical study reveals that the STWR model provides better model fit than the OLS and GWR models. The LST has a negative relationship with GNDVI and LNDVI and a positive relationship with GNDBI for the three years studied. Over the last 20 years, the cooling effect from green vegetation has weakened and the warming effect from built-up features has intensified. We suggest the wide adoption of the STWR model for spatiotemporal studies, as it uses past observations to reduce uncertainty and improve estimation and prediction results.
{"title":"Land Cover Impacts on Surface Temperatures: Evaluation and Application of a Novel Spatiotemporal Weighted Regression Approach","authors":"C. Fan, Xiang Que, Zhe Wang, Xiaogang Ma","doi":"10.3390/ijgi12040151","DOIUrl":"https://doi.org/10.3390/ijgi12040151","url":null,"abstract":"The urban heat island (UHI) effect is an important topic for many cities across the globe. Previous studies, however, have mostly focused on UHI changes along either the spatial or temporal dimension. A simultaneous evaluation of the spatial and temporal variations is essential for understanding the long-term impacts of land cover on the UHI. This study presents the first evaluation and application of a newly developed spatiotemporal weighted regression framework (STWR), the performance of which was tested against conventional models including the ordinary least squares (OLS) and the geographically weighted regression (GWR) models. We conducted a series of simulation tests followed by an empirical study over central Phoenix, AZ. The results show that the STWR model achieves better parameter estimation and response prediction results with significantly smaller errors than the OLS and GWR models. This finding holds true when the regression coefficients are constant, spatially heterogeneous, and spatiotemporally heterogeneous. The empirical study reveals that the STWR model provides better model fit than the OLS and GWR models. The LST has a negative relationship with GNDVI and LNDVI and a positive relationship with GNDBI for the three years studied. Over the last 20 years, the cooling effect from green vegetation has weakened and the warming effect from built-up features has intensified. We suggest the wide adoption of the STWR model for spatiotemporal studies, as it uses past observations to reduce uncertainty and improve estimation and prediction results.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":"1 1","pages":"151"},"PeriodicalIF":0.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89436453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the field of geoinformation science, multiview, image-based 3D city modeling has developed rapidly, and image depth estimation is an important step in it. To address the problems of the poor adaptability of training models of existing neural network methods and the long reconstruction time of traditional geometric methods, we propose a general depth estimation method for fast 3D city modeling that combines prior knowledge and information propagation. First, the original image is downsampled and input into the neural network to predict the initial depth value. Then, depth plane fitting and joint optimization are combined with the superpixel information and the superpixel optimized depth value is upsampled to the original resolution. Finally, the depth information propagation is checked pixel-by-pixel to obtain the final depth estimate. Experiments were conducted using multiple image datasets taken from actual indoor and outdoor scenes. Our method was compared and analyzed with a variety of existing widely used methods. The experimental results show that our method maintains high reconstruction accuracy and a fast reconstruction speed, and it achieves better performance. This paper offers a framework to integrate neural networks and traditional geometric methods, which provide a new approach for obtaining geographic information and fast 3D city modeling.
{"title":"Joint Deep Learning and Information Propagation for Fast 3D City Modeling","authors":"Yang Dong, Jiaxuan Song, D. Fan, S. Ji, R. Lei","doi":"10.3390/ijgi12040150","DOIUrl":"https://doi.org/10.3390/ijgi12040150","url":null,"abstract":"In the field of geoinformation science, multiview, image-based 3D city modeling has developed rapidly, and image depth estimation is an important step in it. To address the problems of the poor adaptability of training models of existing neural network methods and the long reconstruction time of traditional geometric methods, we propose a general depth estimation method for fast 3D city modeling that combines prior knowledge and information propagation. First, the original image is downsampled and input into the neural network to predict the initial depth value. Then, depth plane fitting and joint optimization are combined with the superpixel information and the superpixel optimized depth value is upsampled to the original resolution. Finally, the depth information propagation is checked pixel-by-pixel to obtain the final depth estimate. Experiments were conducted using multiple image datasets taken from actual indoor and outdoor scenes. Our method was compared and analyzed with a variety of existing widely used methods. The experimental results show that our method maintains high reconstruction accuracy and a fast reconstruction speed, and it achieves better performance. This paper offers a framework to integrate neural networks and traditional geometric methods, which provide a new approach for obtaining geographic information and fast 3D city modeling.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":"2 1","pages":"150"},"PeriodicalIF":0.0,"publicationDate":"2023-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89311109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper aims to present and discuss the method of geocoding historical place names from historic maps that cannot be georeferenced in the GIS environment. This concerns especially maps drawn in the early modern period, i.e., before the common use of precise topographic surveys. Such maps are valuable sources of place names and geocoding them is an asset to historical and geographical analyses. Geocoding is a process of matching spatial data (such as place names) with reference datasets (databases, gazetteers) and therefore giving them geographic coordinates. Such referencing can be done using multiple tools (online, desktop), reference datasets (modern, historical) and methods (manual, semi-automatic, automatic), but no suitable approach to handling inaccurate historic maps has yet been proposed. In this paper, selected geocoding strategies were described, as well as the author’s method of matching place names from inaccurate cartographic sources. The study was based on Charles Perthées maps of Polish palatinates (1:225,000, 1783–1804)—maps that are not mathematically precise enough to be georeferenced. The proposed semi-automatic and curated approach results in 85% accuracy. It reflects the manual workflow of historical geographers who identify place names with their modern counterparts by analysing their location and proper name.
{"title":"Mapping Imprecision: How to Geocode Data from Inaccurate Historic Maps","authors":"Tomasz Panecki","doi":"10.3390/ijgi12040149","DOIUrl":"https://doi.org/10.3390/ijgi12040149","url":null,"abstract":"This paper aims to present and discuss the method of geocoding historical place names from historic maps that cannot be georeferenced in the GIS environment. This concerns especially maps drawn in the early modern period, i.e., before the common use of precise topographic surveys. Such maps are valuable sources of place names and geocoding them is an asset to historical and geographical analyses. Geocoding is a process of matching spatial data (such as place names) with reference datasets (databases, gazetteers) and therefore giving them geographic coordinates. Such referencing can be done using multiple tools (online, desktop), reference datasets (modern, historical) and methods (manual, semi-automatic, automatic), but no suitable approach to handling inaccurate historic maps has yet been proposed. In this paper, selected geocoding strategies were described, as well as the author’s method of matching place names from inaccurate cartographic sources. The study was based on Charles Perthées maps of Polish palatinates (1:225,000, 1783–1804)—maps that are not mathematically precise enough to be georeferenced. The proposed semi-automatic and curated approach results in 85% accuracy. It reflects the manual workflow of historical geographers who identify place names with their modern counterparts by analysing their location and proper name.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":"30 1","pages":"149"},"PeriodicalIF":0.0,"publicationDate":"2023-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84949112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To understand the complex phenomena in social space and monitor the dynamic changes in people’s tracks, we need more cross-scale data. However, when we retrieve data, we often ignore the impact of multi-scale, resulting in incomplete results. To solve this problem, we proposed a management method of multi-granularity dimensions for spatiotemporal data. This method systematically described dimension granularity and the fuzzy caused by dimension granularity, and used multi-scale integer coding technology to organize and manage multi-granularity dimensions, and realized the integrity of the data query results according to the correlation between the different scale codes. We simulated the time and band data for the experiment. The experimental results showed that: (1) this method effectively solves the problem of incomplete query results of the intersection query method. (2) Compared with traditional string encoding, the query efficiency of multiscale integer encoding is twice as high. (3) The proportion of different dimension granularity has an impact on the query effect of multi-scale integer coding. When the proportion of fine-grained data is high, the advantage of multi-scale integer coding is greater.
{"title":"A Management Method of Multi-Granularity Dimensions for Spatiotemporal Data","authors":"Wen Cao, Wenhao Liu, Xiaochong Tong, Jianfei Wang, Feilin Peng, Yuzhen Tian, Jingwen Zhu","doi":"10.3390/ijgi12040148","DOIUrl":"https://doi.org/10.3390/ijgi12040148","url":null,"abstract":"To understand the complex phenomena in social space and monitor the dynamic changes in people’s tracks, we need more cross-scale data. However, when we retrieve data, we often ignore the impact of multi-scale, resulting in incomplete results. To solve this problem, we proposed a management method of multi-granularity dimensions for spatiotemporal data. This method systematically described dimension granularity and the fuzzy caused by dimension granularity, and used multi-scale integer coding technology to organize and manage multi-granularity dimensions, and realized the integrity of the data query results according to the correlation between the different scale codes. We simulated the time and band data for the experiment. The experimental results showed that: (1) this method effectively solves the problem of incomplete query results of the intersection query method. (2) Compared with traditional string encoding, the query efficiency of multiscale integer encoding is twice as high. (3) The proportion of different dimension granularity has an impact on the query effect of multi-scale integer coding. When the proportion of fine-grained data is high, the advantage of multi-scale integer coding is greater.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":"112 1","pages":"148"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89283800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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