Spatial Data Sources Research Articles

Comparison of different spatial temperature data sources and resolutions for use in understanding intra-urban heat variation

In this study, we investigate the compatibility of specific vulnerability indicators and heat exposure data and the suitability of spatial temperature-related data at a range of resolutions, to represent spatial temperature variations within cities using data from Atlanta, Georgia. For this purpose, we include various types of known and theoretically based vulnerability indicators such as specific street-level landscape features and urban form metrics, population-based and zone-based variables as predictors, and different measures of temperature, including air temperature (as vector-based data), land surface temperature (at resolution ranges from 30 m to 305 m), and mean radiant temperature (at resolution ranges from 1 m to 39 m) as dependent variables. Using regression analysis, we examine how different sets of predictors and spatial resolutions can explain spatial heat variation. Our findings suggest that the lower resolution of land surface temperature data, up to 152 m, and mean radiant temperature data, up to 15 m, may still satisfactorily represent spatial urban temperature variation caused by landscape elements. The results of this study have important implications for heat-related policies and planning by providing insights into the appropriate sets of data and relevant resolution of temperature measurements for representing spatial urban heat variations.

Exploring the Relationships between Land Surface Temperature and Its Influencing Factors Using Multisource Spatial Big Data: A Case Study in Beijing, China

A better understanding of the relationship between land surface temperature (LST) and its influencing factors is important to the livable, healthy, and sustainable development of cities. In this study, we focused on the potential effect of human daily activities on LST from a short-term perspective. Beijing was selected as a case city, and Weibo check-in data were employed to measure the intensity of human daily activities. MODIS data were analyzed and used for urban LST measurement. We adopted spatial autocorrelation analysis, Pearson correlation analysis, and spatial autoregressive model to explore the influence mechanism of LST, and the study was performed at both the pixel scale and subdistrict scale. The results show that there is a significant and positive spatial autocorrelation between LSTs, and urban landscape components are strong explainers of LST. A significant and positive effect of human daily activities on LST is captured at night, and this effect can last and accumulate over a few hours. The variables of land use functions and building forms show varying impacts on LST from daytime to nighttime. Moreover, the comparison between results at different scales indicates that the relationships between LST and some explainers are sensitive to the study scale. The current study enriches the literature on LST and offers meaningful and practical suggestions for the monitoring, early warning, and management of urban thermal environment with remote sensing technology and spatial big data sources.

Windows and Doors Extraction from Point Cloud Data Combining Semantic Features and Material Characteristics

Point cloud data have become the primary spatial data source for the 3D reconstruction of building engineering, where 3D reconstructed building information models can improve construction efficiency. In such applications, detecting windows and doors is essential. Previous research mainly used red-green-blue (RGB) information or semantic features for detection, where the combination of these two features was not considered. Therefore, this research proposed a practical approach to detecting windows and doors using point cloud data with the combination of semantic features and material characteristics. The point cloud data are first segmented using Gradient Filtering and Random Sample Consensus (RANSAC) to obtain the 3D indoor data without intrusions and protrusions. As input, the 3D indoor data are projected to horizontal planes as 2D point cloud data. The 2D point cloud data are then transformed to 2D images, representing the indoor area for feature extraction. On the 2D images, the 2D boundary of each potential opening is extracted using an improved Bounding Box algorithm, and the extraction result is transformed back to 3D data. Based on the 3D data, the reflectivity of building material is applied to differentiate windows and doors from potential openings, and the number of data points is used to check the opening condition of windows and doors. The abovementioned approach was tested using the point cloud data representing one campus building, including two big rooms and one corridor. The experimental results showed that accurate detection of windows and doors was successfully reached. The completeness of the detection is 100%, and the correctness of the detection is 90.32%. The total time for the feature extraction is 22.8 s for processing 2 million point cloud data, including time from reading data of 10.319 s and time from showing the results of 4.938 s.

Identification of Sea Surface Temperature and Sea Surface Salinity Fronts along the California Coast: Application Using Saildrone and Satellite Derived Products

Coastal upwelling regions are one of the most dynamic areas of the world’s oceans. The California and Baja California Coasts are impacted by both coastal upwelling and the California Current, leading to frontal activity that is captured by gradients in both Sea Surface Temperature (SST) and Sea Surface Salinity (SSS). Satellite data are a great source of spatial data to study fronts. However, biases near coastal areas and coarse resolutions can impair its usefulness in upwelling areas. In this work gradients in SST from NASA Multi-Scale Ultra-High Resolution (MUR) and in two SSS products derived from the Soil Moisture Active Passive (SMAP) NASA mission are compared directly with gradients derived from the Saildrone uncrewed vehicles to validate the gradients as well as to assess their ability to detect known frontal features. The three remotely sensed data sets (MURSST/JPL, SMAP SSS/RSS, SMAP SSS) were co-located with the Saildrone data prior to the calculation of the gradients. Wavelet analysis is used to determine how well the satellite derived SST and SSS products are reproducing the Saildrone derived gradients. Overall results indicate the remote sensing products are reproducing features of known areas of coastal upwelling. Differences between the SST and SSS gradients are mainly associated with the limitations of the microwave derived SSS coverage near land and its reduced spatial resolution. The results are promising for using remote sensing data sets to monitor frontal structure along the California Coast and the application to long term changes in coastal upwelling and dynamics.

The Island of Korčula on Early Modern Maps

Based on selected cartographic depictions of Korčula from the early modern age, the paper examines the geographical knowledge about that island at the time the maps were made. Special attention is directed to the hitherto unknown map of the island of Korčula from the second half of the 16th or the first half of the 17th century, which is kept in the Archivio do Stato di Venezia, given that it shows a very detailed escription of the island of Korčula, especially its western part. The objective of this paper, using the example of the island of Korčula, is to point out the importance of cartographic renderings as an important source of spatial data and to determine whether there was a gradual improvement in the quality of the displayed geographical content in accordance with the assumed development of geographical knowledge and cartographic techniques from the beginning of the 16th century to the end of the 18th century. On the basis of the selected cartographic depictions of the island of Korčula, the significance of the island of Korčula in maritime and geographic frameworks characterized by different political interests, primarily that of Venice, was confirmed. The diachronic series of early modern geographical maps and nautical charts points to the gradual development of geographical knowledge about the island of Korčula, as well as to the improvement of the quality of graphical visualization of the relevant spatial data about that Croatian island.

A Review on Geospatial Information Systems in Cloud Computing

Cloud computing technology is one of the most important technology in information systems (IS) today. It is an important alternative that ensures powerful data processing, storage and exchange. And as a result of the emergence of multiple sources of spatial data and their tremendous growth and the expansion of areas of use of Geospatial Information Systems (GIS), it became necessary to integrate these tow technologies to achieve the optimum benefit of these technologies to make Geospatial Information Systems capable of accommodating the large, rapid, and diverse amounts of spatial data available nowadays. Cloud computing is a new paradigm in Geographic Information Systems. Although there are many publications related to cloud computing in Geographic Information System, there is no systematic review of current research taxonomies .The purpose of this paper is to conduct a survey on GIS Cloud Computing. It addresses the different GIS Cloud frameworks or architectures. The search for articles carried out in general academic databases including the Scopus database, Web of Science Core Collection, and Google Scholar Citations. Retried articles are analysed according to inclusion and exclusion criteria, finally articles selected for review.

Augmented Reality and GPS-Based Resource Efficient Navigation System for Outdoor Environments: Integrating Device Camera, Sensors, and Storage

Contemporary navigation systems rely upon localisation accuracy and humongous spatial data for navigational assistance. Such spatial-data sources may have access restrictions or quality issues and require massive storage space. Affordable high-performance mobile consumer hardware and smart software have resulted in the popularity of AR and VR technologies. These technologies can help to develop sustainable devices for navigation. This paper introduces a robust, memory-efficient, augmented-reality-based navigation system for outdoor environments using crowdsourced spatial data, a device camera, and mapping algorithms. The proposed system unifies the basic map information, points of interest, and individual GPS trajectories of moving entities to generate and render the mapping information. This system can perform map localisation, pathfinding, and visualisation using a low-power mobile device. A case study was undertaken to evaluate the proposed system. It was observed that the proposed system resulted in a 29 percent decrease in CPU load and a 35 percent drop in memory requirements. As spatial information was stored as comma-separated values, it required almost negligible storage space compared to traditional spatial databases. The proposed navigation system attained a maximum accuracy of 99 percent with a root mean square error value of 0.113 and a minimum accuracy of 96 percent with a corresponding root mean square value of 0.17.

New Era for Geo-Parsing to Obtain Actual Locations: A Novel Toponym Correction Method Based on Remote Sensing Images

Geo-parsing, one of the key components of geographical information retrieval, is a process to recognize and geo-locate toponyms mentioned in texts. Such a process can obtain locations contained in toponyms successfully with consistent updating of neural network models and multiple contextual features. The significant offset distance between the geo-parsed locations and the actual occurrence locations still remains. This is because the geo-parsed locations sourced from toponyms in texts always point to the centers of cities, counties, or towns, and cannot directly represent the actual occurrence locations such as factories, farms, and activity areas. Consequently, The significant offset distances between the geo-parsed locations and the actual occurrence locations limit text mining applications in micro-scale geographic discoveries. This research aims at decreasing offset distances of geo-parsed locations by proposing a novel Toponym Correction Method based on satellite Remote Sensing Images (TC-RSI). The TC-RSI method uses satellite remote sensing images to provide extra detailed spatial information that can be associated with the sentence toponym by corresponding attributes. The TC-RSI method was validated in a case study of the forest ecological pattern dataset of An’hui province from visual, statistical, and robustness assessments. The correction results show that the TC-RSI method dramatically decreases the offset distances from about 50 km to about 1 km and promotes geographical discoveries on smaller scales. A series of analyses indicated that the TC-RSI is a valid, effective, and promising method to improve the accuracy of geo-parsed locations, which allows text mining to find more accurate geographical discoveries with lower offset distances. Moreover, toponym correction promotes the use of more diverse spatial data sources, such as Lidar, domain gazetteers, Wikimedia, and streetscapes, which are expected to usher in a new era of geo-parsing with toponym corrections.

Harmonized landform regions in the glaciated Central Lowlands, USA

ABSTRACT Many distinct glacial episodes in the past ~1 million years in the Central Lowlands of North America left behind a patchwork of glaciated landscapes of different ages and formed through different glacial, paraglacial, and proglacial processes. Herein, we synthesize and reconcile diverse data sources across nine states in the Central Lowlands to create a generalized landform regions map, incorporating information from surficial geology, age-constrained glacial boundaries, soil properties and parent material, and topography. The resulting map presents regions of till plains, moraines, outwash plains, and glacial lakes with ages ranging from 10.2 cal ka BP to greater than 500 cal ka BP. This new map improves on regional perspectives of the glaciated Central Lowlands by modifying and reconciling boundaries to agree with a variety of spatial data sources with finer detail. This refined map enables a more accurate spatial analysis of the landform regions by reducing noise from imprecise boundaries.

PM2.5 Exposure and Health Risk Assessment Using Remote Sensing Data and GIS.

Assessing personal exposure risk from PM2.5 air pollution poses challenges due to the limited availability of high spatial resolution data for PM2.5 and population density. This study introduced a seasonal spatial-temporal method of modeling PM2.5 distribution characteristics at a 1-km grid level based on remote sensing data and Geographic Information Systems (GIS). The high-accuracy population density data and the relative exposure risk model were used to assess the relationship between exposure to PM2.5 air pollution and public health. The results indicated that the spatial-temporal PM2.5 concentration could be simulated by MODIS images and GIS method and could provide high spatial resolution data sources for exposure risk assessment. PM2.5 air pollution risks were most serious in spring and winter, and high risks of environmental health hazards were mostly concentrated in densely populated areas in Shanghai-Hangzhou Bay, China. Policies to control the total population and pollution discharge need follow the principle of adaptation to local conditions in high-risk areas. Air quality maintenance and ecological maintenance should be carried out in low-risk areas to reduce exposure risk and improve environmental health.

Folate availability in the rat embryo: Implications in maternal diabetes and its modulation by mTOR

A better understanding of how the spatial configuration of cities, understood as urban structure and forms, can achieve sustainable development is needed. This paper presents spatial data and an automated workflow for studying the urban structures (i.e., road and transportation networks) and forms (i.e., building size, position, function and density) of two medium-sized European cities - Bergen, Norway and Zürich, Switzerland. The data focuses on examining correlations between the densification patterns and transport energy usage of these cities de Koning et al., (2020). Spatial and tabular datasets for (i) urban structures, (ii) urban forms, (iii) building density, (iv) road centre lines and (v) transport energy usage are obtained as georeferenced files from OpenStreetMap (OSM) and upon request from collaborating local and national authorities. Transport energy data is derived from traffic data collected from the Norwegian Public Road Authorities or simulated via a traffic model. Open-source data is used wherever possible. Data gaps within proprietary data are supplemented with proxies or open-source data.Hand-drawn axial maps drawn by the authors using the Space Syntax methods and analysed via depthmapX software are a crucial dataset presented here. All analysed data are then returned to a Geographical Information System (GIS) platform and processed via an automated workflow of 19 steps built via the ModelBuilderTM tool in ESRI® ArcGIS. The automated workflow allows for repetitive cross-city comparison and the compilation of diverse spatial data sources for analysis.In combination with the novel workflow, the dataset can be used for future comparative studies in spatial planning, transport planning and management of energy systems to facilitate informed decision-making towards more sustainable developments.

Spatio-Temporal Analysis of Trends on Habitat Disturbance Between 1986 And 2021 In Protected Areas of, Zimbabwe

The main aim of this research is to analyse spatio- temporal trends on habitat disturbance between 1986 and 2021 in protected areas of, Zimbabwe with a focus on anthropogenic activities such as mining which are leading to biodiversity loss, habitat and ecosystem services disturbances. This paper provides an overview of recent studies using Remote Sensing (RS and Geographic Information Systems (GIS) techniques to assess the extent of mining disturbance on plant habitats in protected areas of Zimbabwe. Through a systematic review, literature hotspot analysis was done as well as a trend analysis at regional specific level together with statistical tests in order to come up with an overview of the past studies which were done on habitat disturbance in protected areas of Zimbabwe over a thirty-five-year period. The paper highlights the complex nature of the impacts of mining as well as discuss spatial research methods, data sources and limitations. The results indicated an exponential growth of scientific literature on human-environment interactions in the mining environments at regional scale and a non- monotonic trend at country level. This has prompted a need to synthesize literature to guide future research. Conclusively there is limited research done on habitat disturbance in protected mining environments, hence the need for advanced geospatial scientific studies in the future on spatio-temporal analysis of trends on habitat disturbance

Road network evolution in the urban and rural United States since 1900

Road networks represent a key component of human settlements, such as cities, towns, and villages, that mediate pollution and congestion, as well as economic development. However, little is known about the long-term development trajectories of road networks in rural and urban settings. We leverage novel spatial data sources to reconstruct and analyze road networks in more than 850 US cities and over 2500 US counties since 1900. Our analysis reveals significant variations in the structure of roads both within cities and across the conterminous US. Despite differences in the evolution of these networks, there are commonalities and strong geographic patterns. These results persist across the rural-urban continuum and are therefore not just a product of accelerated urban growth. These findings refine and extend existing knowledge and illuminate the need for policies for urban and rural planning including the critical assessment of new development trends.

Methodology for Precision Land Use Mapping towards Sustainable Urbanized Land Development.

Land-use/land cover maps constitute one of the key sources of information on urban space. To address the problems associated with the lack of timely and detailed land-use maps, the authors have developed a universal methodological approach for monitoring land use structure that is particularly useful in a rapidly evolving urban environment. Therefore, the main aim of this study was to develop a universal methodology for high-precision land-use analysis in urbanized areas in the context of large-scale mapping. The method uses geoinformation tools, photogrammetric data (orthophoto maps) as well as data acquired during a field inventory (involving a field survey and field mapping). The proposed approach is based on the modified existing approaches towards a detailed identification of land-use patterns while reducing the difficulties arising from the limitations of existing land use data sources. The methodology consists of several steps. First, the data sources for land-use analysis were selected. Subsequently, the classification of land-use categories in urban space was made. Finally, the method to high-precision land-use analysis for large-scale mapping was defined under the assumption that it is to be universal for use in countries with different levels of spatial and economic development. The proposed research method is based on an interpolation algorithm. It is highly valid, flexible, modifiable, accurate, and it can be applied to process publicly available and free sources of spatial data. Validation of the method on a test object (city of Ostróda, Poland) showed its high effectiveness, which is limited only by the type of data. The results obtained with the use of the proposed method not only supported the determination of the present land-use structure in the town but were also used to identify areas with the highest and lowest intensity and concentration of specific land-cover types.

Методика дешифрирования архивных и современных космических снимков для изучения динамики склоновых процессов в долине р. Гейзерная (Камчатка)

Based on the database of spatial thematic data sources for the Geysernaya River valley (Kamchatka), the work was carried out to identify the areas of the greatest activation of modern exogenous processes and the manifestation of endogenous processes over the maximum possible time interval (more than 50 years). Space images of high spatial resolution (0.5–3 m), received from Key-Hole-4 (1964), GeoEye (2009), Pleiades-1A/1B (2013, 2016), WorldView-2 (2017) satellites, results of field survey and UAV imagery in 2021, literature and archive materials were used as sources. The developed interpretation technique included both sequential interpretation of preliminary geometrically coordinated multi-temporal images and their digital processing: brightness quantization, calculation of vegetation index NDVI, formation of color multi-temporal composite, which allowed to separate vegetation-covered and bare slope areas by formal features and increased the overall reliability of interpretation. Calculations based on the interpretation results determined that the proportion of the total valley area affected by modern slope processes was 10.5 % in 1964, 14.8 % in 2009, and 13 % in 2017. In the process of images interpretation, it was established that slope processes dominated on the left bank of the Geyser River, where more than 75 % of the area of all outcrops (or 10.2 % of the total studied area of the valley) was concentrated, which is apparently caused by confinement of this valley side to the caldera limiting circular fault, which preserves its activity, and also by the presence of the area of modern uplift there. On the right bank of the river, large areas are occupied by ancient large gravitational shifts, possibly associated with collapsing edges of extrusive massifs. Landslides-slides and erosion in the groundwater outlets are predominant among the modern processes here. The right bank accounts for less than 25 % of the area of all outcrops (or 3.3 % of the total study area).

Ретроспективный анализ и особенности создания цифровых инженерно-хозяйственных карт муниципальных образований (на примере МО Иркутской области)

The article considers retrospective, modern and prospective ideas about the types of data sources for engineering mapping with economic applications, examples of the selection of source materials and cartographic documents for creating economic engineering maps of various types, including the prepa-ration of a unified electronic cartographic base, data of remote sensing of the Earth from space and aerial pho-tography. The retrospective analysis of cartographic works differs from other research methods in that it compares the planned results with the achieved results, and also takes into account the existing expe-rience in creating maps, which makes it possible to optimize and regulate the mapping technology in the future. The advantages of modern sources of spatial data, their relationship with each other and their content of thematic information for solving specific engineering problems of forestry, agriculture, land cadastre are reflected. Also indicators of optimal management of the municipal territories in the Irkutsk region and the study of natural and ecological complexes of all Baikal areas are noted. Based on the retrospective analysis the article reflects the method of choosing the scale of the created map and the selection of materials, corresponding to it, and considers the main combinations used for the synthesis of multispectral images for the purpose of engineering mapping.

Mapping the spatial distribution of wetlands in Argentina (South America) from a fusion of national databases

Context There a large information gap on the spatial distribution and diversity of wetland types in South America. Aims We focus on mapping the spatial distribution of broad wetland types in Argentina, based on the integration of open spatial data sources developed by national government agencies. Methods We designed a two-tier process, as follows: we filtered broad wetland types described in the attributes of the spatial datasets and created a separate vector layer for each wetland class; we then ensembled the layers by populating a 25-m cell raster template. Key results Our WetCarto_AR layer indicates that wetlands cover 13.5% of mainland Argentina, being distributed throughout the country with a greater concentration towards the north-east, but patchy in the rest of the country. Palustrine is the dominant wetlands class followed by Riparian and Lacustrine. Global datasets underestimated wetland coverage, although the same large wetlands are recognised in all. Conclusions Our results make visible the known spatial extent of wetlands in Argentina and provide information to feed or validate global models. Implications Results stress the importance of existing local databases, which, even when generated for other purposes, can be a starting point for country or region wetland mapping.

Geofizičko postavljanje i seizmičnost u zoni Rift Malavi, Jugoistočna Afrika, analizirano kartografskim skriptiranjem

This paper describes a scripting cartographic techniques that automatically generate maps from open source spatial data using syntax of General Mapping Tools (GMT) and R. A case study present mapping East Africa with a focus on Malawi. In this study, two different approaches of scripting cartography using R programming language and GMT were studied for geophysical analysis aimed to visualize a series of eight new maps in Malawi: topography based on the GEBCO data, seismicity, geomorphometric modeling based on SRTM-90 m (slope, aspect, hillshade and elevation) and geophysical fields: geoid based on EGM-2008 and free-air Faye's gravity based on satellite derived gravity data from CryoSat-2 and Jason-1. In contrast to previous maps of Malawi, a scripting approach was introduced as a console-based cartographic mapping developed for plotting a series of thematic maps based on the high-resolution data. The maps demonstrate correlations between the topography and tectonic faults (Malawi Rift Zone) and earthquakes in the Malawi Lake and extent of landforms. The results demonstrate strong correspondence between the topography and geophysical fields (geoid and gravity): negative values of geoid (-15 to -20) are notable over the Malawi (Nyasa) Lake which corresponds with local topographic depressions. Free-air gravity fields reach the lowest values (-50 to -100) over the Malawi Lake. Local heights in gravity are compared with topographic mountain ranges in the NW and SW of the country on the borders with Zambia and Mozambique. The location of earthquakes vary with the majority located in the north. The geomorphological landforms demonstrate variability in slope steepness and aspect orientation shown on histogram. The techniques of scripts can be used to automatically map spatial data using raster datasets for geophysical visualization, and this paper demonstrated this through a variety of map from the presented thematic series of geophysical maps of Malawi. Full scripts used for mapping are available on the author's public GitHub repository with provided link to her open access codes.

Environmental and Household-Based Spatial Risks for Tungiasis in an Endemic Area of Coastal Kenya.

Tungiasis is a cutaneous parasitosis caused by an embedded female sand flea. The distribution of cases can be spatially heterogeneous even in areas with similar risk profiles. This study assesses household and remotely sensed environmental factors that contribute to the geographic distribution of tungiasis cases in a rural area along the Southern Kenyan Coast. Data on household tungiasis case status, demographic and socioeconomic information, and geographic locations were recorded during regular survey activities of the Health and Demographic Surveillance System, mainly during 2011. Data were joined with other spatial data sources using latitude/longitude coordinates. Generalized additive models were used to predict and visualize spatial risks for tungiasis. The household-level prevalence of tungiasis was 3.4% (272/7925). There was a 1.1% (461/41,135) prevalence of infection among all participants. A significant spatial variability was observed in the unadjusted model (p-value < 0.001). The number of children per household, earthen floor, organic roof, elevation, aluminum content in the soil, and distance to the nearest animal reserve attenuated the odds ratios and partially explained the spatial variation of tungiasis. Spatial heterogeneity in tungiasis risk remained even after a factor adjustment. This suggests that there are possible unmeasured factors associated with the complex ecology of sand fleas that may contribute to the disease’s uneven distribution.

Novel Framework for 3D Road Extraction Based on Airborne LiDAR and High-Resolution Remote Sensing Imagery

3D GIS has attracted increasing attention from academics, industries, and governments with the increase in the requirements for the interoperability and integration of different sources of spatial data. Three-dimensional road extraction based on multisource remote sensing data is still a challenging task due to road occlusion and topological complexity. This paper presents a novel framework for 3D road extraction by integrating LiDAR point clouds and high-resolution remote sensing imagery. First, a multiscale collaborative representation-based road probability estimation method was proposed to segment road surfaces from high-resolution remote sensing imagery. Then, an automatic stratification process was conducted to specify the layer values of each road segment. Additionally, a multifactor filtering strategy was proposed in consideration of the complexity of ground features and the existence of noise in LiDAR points. Lastly, a least-square-based elevation interpolation method is used for restoring the elevation information of road sections blocked by overpasses. The experimental results based on two datasets in Hong Kong Island show that the proposed method obtains competitively satisfactory results.