Spatial Analysis Tools Research Articles

THE USE OF GIS TECHNOLOGIES TO DETERMINE TRANSPORT ACCESSIBILITY IN TOURISM

The article examines the possibilities of using GIS technologies in the tourism sphere. The study focused on the conceptual modeling of tourism and the geoinformation support in planning tourist routes. The subject area of tourism as a conceptual model in terms of database modeling and GIS using UML language was characterized and presented. The method of using the GIS to solve the problem of modeling transport accessibility zones to tourist attractions in shaping transport routes has been proposed. The QGIS was used in tandem with a database management system such as PostgreSQL. GRASS GIS environment was used to perform spatial analysis. The object of this research is the transport network in one of the districts of the Ternopil region. We have tested in practice the proposed technological scheme on the example of the Ternopil district with the use of PostgreSQL database management system, QGIS, and GRAS GIS. We have created a tourist isochrone map of this region with some attractive places. The proposed methodology for determining transport accessibility using spatial analysis tools with the creation of isochrone maps and a flexible system of adjustments in the GIS makes it possible to optimize already existing tourism routes and create new ones.

Use of Geoprocessing in Identification of Areas Suitable for the Implementation of Center Pivot Irrigation in the District of Mossurrize, in Republic of Mozambique

With population growth, there is an increase in the demand for food, making irrigation an important technology for this purpose. Therefore, planning, and technical knowledge of environmental variables are essential for the implementation of irrigation, facilitated by geoprocessing techniques, which can help in decision-making, mainly due to the ability and ease of working large areas. The objective of this study is to analyze areas with potential for the implementation of a center pivot irrigation system in the district of Mossurrize. The analysis was carried out using geoprocessing tools through QGIS 2.3.20.0 and ArcGIS software. The methodology considers the combined analysis of different information plans to generate a synthesis map. The information plan variables were reclassified with weights from 1 to 9, demonstrating the influence of each element on the considered phenomenon. Then the variable maps were submitted to weighted analysis. The tools for spatial analysis through geoprocessing proved to be efficient in the manipulation and integration of the data used to identify and quantify the areas suitable for the implantation of a center pivot irrigation system and may be a viable alternative for decision making.

THU500 Geospatial Analysis To Quantify Spatial Heterogeneity Of Tumor Microenvironment

Abstract Disclosure: J. Yoo: None. C. Mitra: None. Y. Yue: None. A. Soliman: None. Z. Madak Erdogan: None. Metabolic heterogeneity, a concept describing distinct differences in nutrient metabolism within the cells of a healthy or diseased tissue, is a common challenge in development and cancer research. Current methods, such as histology and Magnetic Resonance Imaging (MRI), cannot visualize nutrient uptake into single cells, making it difficult to understand the heterogeneity that affects cellular growth and metabolism. Visium Spatial Transcriptomics, a method that shows regional gene expression, is emerging as a powerful spatial analysis tool, and we aim to integrate this transcriptomic method with other biologically relevant datasets to highlight the importance of cell-microenvironment interactions. We propose to leverage geostatistical tools to model the multivariate covariance structure of genes expression profiles to allow: 1) understanding of their spatial characteristics and covariance changes under certain conditions (e.g. drug treatment or damage), 2) using these covariance structures to simulate synthetic samples at a scale sufficient to train the state of art neural network architectures, and 3) incorporating spatial awareness directly in the machine learning/deep learning models by enriching the spatial transcriptomics data using distance proximity functions. We will quantify the tumor microenvironment spatial heterogeneity using different approaches. First, we will analyze the degree of spatial auto-correlation to estimate the ranges of spatial heterogeneity within a tumor. We also plan to quantify the degree of anisotropy to identify if the tumor heterogeneity is happening along specific axis. Different local clustering indicators will be also calculated, such as the Getis-Ord (Gi*) statistic and Local Moran's I to identify the prevalence of hot and cold spots in the tumor. These hot and cold spots will be used to segment the tumor area into sub-regions and further quantify the local heterogeneity with a single tumor. We hypothesize that transcriptional heterogeneity reflects phenotypic differences in different regions of healthy or diseased tissue, rendering cells in certain regions to display a differential response to external stimuli (e.g. diet or drugs). Integration of multiple information (e.g. retrospective patient data) with different spatial heterogeneity indicators identified from the geospatial analyses could aid in identification of patients who are resistant to cancer therapies and further advance personalized therapy. Presentation: Thursday, June 15, 2023

Exploring spatial relationship between landscape configuration and ecosystem services: A case study of Xiamen–Zhangzhou–Quanzhou in China

Human activities have significantly impacted our landscapes and the functioning of our ecosystems. Landscape planning can be optimized by understanding the spatial relationships between landscape patterns and ecosystem services. However, existing research on these relationships is scarce. We sought to fill this gap with a case study of the Xiamen–Zhangzhou–Quanzhou area in China. Changes in landscape configuration were estimated in terms of the number of patches, edge density, Shannon's diversity index, and aggregation index. We applied the spatial analysis tools in GIS and InVEST to examine the following five ecosystem services: grain production, water retention, carbon sequestration, soil conservation, and habitat quality. We performed multiscale geographically-weighted regression to investigate the spatial relationships between landscape configuration and the provision of ecosystem services between 2000 and 2020. The results showed that the landscape was fragmented and diversified with a decline in various ecosystem services. The spatial relationships were characterized by non-stationarity and heterogeneity. The effect of landscape configuration on ecosystem services generally weakened, and landscape aggregation had the strongest influence on service provision. Our findings serve as a reference for the formulation of policy on landscape planning and management.

Increased Risk of American Tegumentary Leishmaniasis in an Urban and Rural Area of Caratinga, Brazil between 2016 and 2021.

We used spatial analysis tools to examine the epidemiological situation and spatial distribution of American tegumentary leishmaniasis in the municipality of Caratinga between 2016 and 2021. In addition, potential sandfly vectors were captured. All information used in this study was retrieved from public health archives and confirmed in the state health services databases. All cases were analyzed using Geographic Information Systems software. In addition, sandfly collections and molecular detection of Leishmania were carried out in areas with the highest number of cases. During the analyzed period, American tegumentary leishmaniasis (ATL) cases increased and remained high in the last years. The hotspots included urban areas of Caratinga city and the districts of Patrocínio of Caratinga and Sapucaia. The species Nyssomyia whitmani, Nyssomyia intermedia, and Migonemyia migonei were the most abundant species and the ITS1-polymerase chain reaction technique detected Leishmania DNA in these species. On the basis of our analyses, the urbanization of ATL in Caratinga has taken place in recent years. Because of the increase in the number of human cases and the presence of vectors, it is recommended that health authorities focus on control measures in hotspots.

Research on rainfall prediction based on RBF neural network model and stormwater inundation risk in scenic areas: A case study of the Yesanpo Scenic Area, Baoding, China

Research on stormwater inundation risk and rainwater management in scenic areas has a lot to do with rainfall during the flood season. When the measured rainfall data is limited, an artificial network model with nonlinear mapping capability can be applied to predict rainfall data during the flood season, which increases the sample size of rainfall data and improves the accuracy of research results. Based on a radial basis function (RBF)neural network model, this paper takes the Yesanpo Scenic Area in Baoding City, Hebei Province as an example to estimate the monthly maximum rainfall data during the flood season (July–September) of 2022, 2023, and 2024 in the study area. On this basis, the Pearson III frequency curve is used to calculate the design rainfall corresponding to the rainfall frequency of 20%, 5%, and 2%. With the help of SCS-CN model and ArcGIS spatial analysis tools, the stormwater inundation areas are simulated in the study area, which are divided into three risk levels: high, medium, and low, providing a reference for the stormwater management in the Yesanpo Scenic Area.

Analytical situational center of the region in the context of energy-ecological, socio-economic and infrastructural indicators

Today, geo-information systems play a special role in regional and industry management, which serve as an effective tool for modeling regional systems. This work presents the results of the development of a situation center, the task of which is to store and update data on the state of regional systems. The situation center serves as a convenient tool for structuring and presenting data. The information base for the formation of an array of initial data is the Federal State Statistics Service, from where statistical data on multi-level regional systems were uploaded in the context of energy-environmental, socio-economic and infrastructure indicators. The results of the study make it possible to present the analytical situational center as a system for managing a database of initial and analytical data on energy-environmental, socio-economic and infrastructure indicators, including a built-in computational and analytical module. The developed tool helps to reduce the time to search for the necessary indicator, builds graphs and maps, calculates the pace of a particular indicator. In addition, the tool can be constantly improved by adding new groups of indicators, infographics and automatic calculations. The study findings represent a contribution to understanding the practice of using spatial analysis tools. Work is performed within GUU grant (research № 1002-23).

Spatial patterns on tourism establishments in five CIP´s in Mexico, 2010–2022

AbstractStudies on the spatial patterns of economic activity contribute to economic performance management, planning, and promotion. Investors, policymakers, and social organizations have access to valuable information on the concentration and location of activity. Spatial patterns of tourism have been studied to better organize tourist flows, optimize existing infrastructure, plan new facilities, and promote new destinations. In the late 1960s, the Central Mexican government started projects aimed at constructing large tourism facilities primarily for foreign visitors. These were called centros integralmente planeados or “fully planned centers” (CIP’s) and were headed by Fondo Nacional de Fomento al Turismo (National Tourism Promotion Fund) FONATUR. The objective of this study is to explore the spatial distribution patterns of tourism establishments in five municipalities where CIP?s are located. The study uses exploratory spatial data analysis (ESDA) tools, such as average nearest neighbor index, standard deviation ellipse, spatial kernel density, global and local indicator of spatial autocorrelation, applied to geographic information system (GIS) data representing tourism establishments. The results show changes in establishment distribution and orientation, as well as spatial concentration in all cases and years. All CIP?s experienced a significant reduction in establishments, particularly small and medium-sized businesses. This paper is one of the first to analyze simultaneously the spatial distribution and concentration of tourism establishments in five municipalities where CIP’s are located.

Spatial distribution characteristics and causes of public sports venues in China

Public sports venues serve as a crucial material medium for human athletic activities. Research on their spatial distribution holds profound implications for optimizing spatial layout and promoting human sports endeavors. This paper takes interest points of sports venues in China as research samples, employing GIS spatial analysis tools, mathematical statistics, and Geo-detector as research methods. It systematically investigates the spatial distribution characteristics and causes of sports venues in China, aiming to provide insights and references for the optimization of spatial arrangement, sustainable development, and relevant policy formulation of sports venues in the country. The results indicate that: (1) In terms of overall distribution characteristics, sports venues in China present a multi-centric agglomeration in geographical space. (2) From a regional distribution perspective, the spatial layout of these venues reveals a pattern of “more in the south, less in the north,” “dense in the southeast, sparse in the northwest,” and a coastal strip distribution. (3) Regarding spatial correlation, the hot and cold spot partitions of sports venues largely align with the “Hu-Line”, with their spatial distribution tending towards a positively correlated pattern of high–high clustering and low–low clustering. (4) There is evident heterogeneity in factors affecting sports venues’ spatial distribution. The distribution results from the interactive coupling of multiple factors, where the interaction between any two factors offers stronger explanatory power for the spatial layout of the venues.

Geospatial Analysis in Web Browsers—Comparison Study on WebGIS Process-Based Applications

With the rapid development of internet technologies in recent years, the shift from the desktop to the web platform can be seen within geospatial analysis. While analytical tools, such as buffer or clip, are routinely used in desktop environments, WebGIS deals with geographic information, including geospatial analysis, within the online environment. The main aim of this paper is to perform a comparison and evaluation of vector-oriented online geoprocessing tools in a WebGIS environment, supported by the development of a custom solution for geospatial analysis. The application called GeOnline is developed and tested as a case study to demonstrate the availability of spatial analysis tools within the web browser. It implements the specialized geospatial library Turf.js, which allows using non-trivial geospatial analysis, such as intersect, clip or calculate centroids. It handles client-side processes. Both a functionality comparison and performance testing are carried out, while the paper primarily focuses on data-driven (data-based) analysis and not only on visual-driven (visual-based) analysis. The comparative study evaluates five geospatial tools (ArcGIS Online, GISCloud, CARTO, FOURSQUARE, GeOnline) and summarizes the solutions from different aspects, including the number of supported operations. Finally, performance tests on GeOnline separately and among alternative solutions are performed. While ArcGIS Online is considered the most comprehensive solution on the market, GeOnline performs well compared to alternative solutions.

Are Measures of Patrollability Affected by Tessellating Shape

A grid-based thematic map is a spatial analysis tool that can be used to identify crime patterns, aiding law enforcement agencies in identifying problem areas within their jurisdictions and allocating resources to address them. Grids used to create these maps are comprised of shapes that tessellate (i.e., squares, hexagons, triangles). Square grids are used most often in crime hot spot mapping, but little is known whether the choice of tessellating shape affects performance metrics used to assess prospective hot spot forecasts. Using a purposive sample of known crime locations in St. Petersburg, Florida, the current study investigated whether tessellating shape used in grid-based thematic maps affects one particular performance metric: patrollability. Findings were mixed, showing that the effect tessellating shape has on grid-based thematic maps varies by type of crime and on which patrollability metric is used to assess forecasting performance. Results are discussed in terms of their impact on practioners' use of crime hot spot mapping in law enforcement and on future research.

Assessment of Urban Wastelands using GIS and IoT as Tools for Spatial Data Analysis

Large urban development around cities often results in vacant, abandoned or unproductive land parcels, sometimes called ‘wastelands’. There are various types of urban vacant land identified as post-industrial, derelict, land without any vegetation and left unattended, natural, and transportation-related vacant lands. The traditional way of collecting the data to study and manage such massive wastelands is hugely time consuming. Geographical Information Systems (GIS) and Remote sensing applications can extract periodic spatial changes and make it easier to collect and generate base maps, assess the number of wastelands in a given city, and remotely assess the extent of the area of wastelands/ vacant/ degraded lands. GIS also helps as a valuable tool to identify the neighboring land uses, lakes, green cover, and road networks, which could be considered as base work for any study to progress further with accurate information. Newer technologies like big data and cloud-assisted technology are employed in IoT applications for these wastelands to formulate an intelligent environment. Factors such as workforce, time, labor, transportation, money, and all other logistics can thus be strategized better with the combination of GIS and IoT applications; these are integral to wasteland monitoring and management practices for urban regions. This review paper aims to demonstrate how GIS, remote sensing, and IoT facilitate us to carry out monitoring, analyze, extrapolate, process, store, and integrate the spatial data for urban wastelands; and becomes a basis for planning and decision-making for on-site and management plans.

Spatio-Temporal Analysis of Threatened Bird Species in Perak Malaysia Using Citizen Science Data

Birds are recognized as important indicators of the current state of the environment, mainly as they are sensitive to habitat change. There is little research that spatially analyses the threatened bird hotspots and their relationship with land use and land cover changes, particularly in Malaysia. This study aims to spatially analyze the pattern of threatened birds in Perak, Malaysia using seven years of temporal data from the Citizen Science database. Spatial analysis tools, such as the Average Nearest neighbour analysis and high/low clustering (Getis-Ord General G), were used to analyze the distribution and spatial concentration of the data, followed by the Pearson correlation to determine the relationship between bird occurrence with land-use types. The results showed that the hotspot location of threatened birds for seven years was in Taiping City, while the coldspot was in the mangrove area of Kuala Gula. The study discovered a pattern of water birds commonly spotted in mangrove areas (i.e., Milky Stork and Lesser Adjutant) within the Taiping Lake Garden. The correlation analysis results indicated that shorter distances to green areas and longer distances to urban areas can influence the number of threatened birds spotted. Such findings can assist relevant authorities in the effort to protect and preserve threatened and endangered bird species by promoting a green landscape design and wildlife conservation within a city.

Monitoring the spatial distribution patternaccording to urban land use and health risk assessment on potential toxic metal contamination via street dust in Ankara, Türkiye.

The urbanization processes with growing vehicle numbers cause heavy metal pollution in street dust, and high populations in metropolitan cities are exposed to pollutants. This paper aims to monitor the spatial distribution of heavy metals and evaluate the concentrations via health risk assessment of HMs (Cu, Ni, Cd, Co, Pb, and Zn) that expose the inhabitants to health hazards. According to the results of the current study, sixty street dust samples were applied to the acid digestion technique and quantification by inductively coupled plasma-mass spectrometry (ICP-MS). The spatial distribution of the selected heavy metals in the street dust was investigated using the spatial analysis tool in ArcGIS 10.0 according to population density and land use. In the present study, we used hazard index and cancer risk methods to estimate the public health risk of the pollutants exposed to street dust in Ankara. The concentrations range of the elements in street dust over the study area ranged from 3.34-4.50, 31.69-42.87, 16.09-21.54, 42.85-57.55, 0.00-3.51, and 23.03-30.79, respectively. The overall decreasing order of mean concentration of metals was observed as follows: Pb > Cu > Ni > Co > Cd > Zn. Vehicle traffic and industrial activities seem to be the most critical anthropogenic sources responsible for dust pollution in the study area. The risk assessment of Pb and Ni exposure was the highest, and the hazard index values were 2.42E + 00 and 2.28E + 00 mg/kg/day for children. However, the effect on adults was 2.62E-01 and 2.37E-02 mg/kg/day, followed by inhalation and dermal contact with street dust was almost negligible. The decreasing concentration is modeled spatially along the western development corridor of the city. The risk to public health is high in areas with high densities close to the city center and the main artery.

Community conservation and remote sensing of the desert-adapted lions in northwest Namibia

Community-based conservation of the desert-adapted lions takes place within the semi-arid and arid environments of northwest Namibia. This area is primarily designated as communal conservancies, a form of community-based natural resource management (CBNRM). The article describes the activities of the Lion Rangers program, a CBNRM organization, emphasizing how the use of remote sensing techniques, including the Spatial Analysis and Report Tools (SMART) software and mobile-app, GPS/satellite collars, and trail cameras, contribute to lion monitoring and limiting human–lion conflict (HLC). Remote sensing data are being integrated with historical and sociological research, with applicable lessons for lion conservation and conservation of other problem-causing species.

GIS Based Monitoring of Critical Area Upstream Watershed of PB. Soedirman Reservoir, Central Java, Indonesia

Upstream watershed has an important role as a conservation area to protect environmental functions for life. The damage of upstream watersheds causes various environmental problems such as soil erosion, landslides, river sedimentation, flooding, and others. Monitoring of upstream watersheds is important to control potential environmental damage. This study used a geographic information system (GIS) for monitoring the critical area upstream of the study site. The aim of this study is to identify critical areas in the watershed by using GIS. Six indicators presented in raster maps such as annual rainfall, soil type, land use type, land slope, population density, and water quality were used to determine the critical area. These indicators were then quantitatively analyzed by using spatial analysis tools of Arc GIS 10.6. Five classes of the critical category were used in this study namely very low, low, moderate, high, and very high. The result showed that 61.74% of the study site was covered by a low class of critical areas. Meanwhile, 37.89% and 0.36% of the study site were covered by moderate and high class critical areas, respectively. Areas with moderate and high class critical areas were found in the eastern part of the study site. The findings of this study provide a reference for determining priority areas for conservation planning.

The evolution of spatiotemporal patterns and influencing factors of high-level tourist attractions in the Yellow River Basin

Introduction: High-level tourist attractions (HLTAs) are essential components of high-quality tourism development in the Yellow River Basin. In the context of holistic tourism and mass tourism, it is necessary to re-examine the spatial pattern of HLTAs.Methods: Selecting the Qing–Gan–Ning region of the Yellow River Basin as a case study site, based on the data on 590 high-level tourist attractions in 2009, 2015, and 2021, and with the help of ArcGIS10.8 spatial analysis tools, the nearest neighbor index, kernel density analysis, and standard deviation ellipse methods, such as ellipse and ESDA spatial exploratory analysis, were used to analyze the spatiotemporal pattern of the spatial distribution of high-level tourist attractions in the study area from the aspects of type, density, and spatial autocorrelation. Overlay analysis, buffer analysis, and other methods were used to select the influencing factors, and finally, the influencing factors were verified with the help of GeoDetector.Conclusions: The conclusions are as follows: the NNI values for the Qing–Gan–Ning area of the Yellow River Basin are 0.699, 0.7, and 0.618, and the spatial structure type was clustered. The distribution density showed an evolutionary trend of point-like agglomeration and linear expansion, with the provincial capital as the core and the Yellow River as the axis. The distribution density of high-level tourist attractions is 27, 44, and 74 per 10,000 km2. In terms of the dynamic distribution direction of the center of mass, there was little interannual variation, showing a northeast–southwest direction, which is consistent with the flow direction of the Yellow River in the region. Furthermore, the analysis of Moran’s I index showed clear spatial autocorrelation at the county scale. HLTAs exhibited clustering and wider distribution in H–H and L–L zones, while the L–H and H–L zones displayed a more dispersed and narrower distribution. The order of factors affecting the spatial distribution of HLTAs was economic factors (0.5257) > social factors (0.5235) > natural factors (0.491), and interactive detection showed that there were two-factor enhancements and nonlinear enhancements in the factors.Dicussions: This study contributes to the conservation development and sustainable development of ecotourism resources in the Yellow River Basin.

Influence of COVID-19 on night-time lights in Czechia

ABSTRACT Night-Time Light (NTL) satellite imagery is widely recognized and valued as a powerful tool for spatial analysis and research. Despite its global popularity, there is absence of similar studies dealing with NTL intensity in Czechia. Using VIIRS/DNB data, our study focused on analyzing the changes in night-time light intensity before and during the coronavirus restrictions. These restrictions had a substantial impact on social and economic life, particularly during the peak of pandemic, affecting not only Czechia but also other regions worldwide. A mosaic of the study area was prepared, followed by the aggregation of the original raster data to municipalities with extended power (MEPs) and calculation of the average light index (ALI). The resulting typology made it possible to identify regional differences in NTL intensity. The main hypothesis was confirmed, it was observed that the ALIexhibited a decrease of 18% in 2020, whereas in 2021, the decline averaged at 9%.

Spatial distribution and influencing factors of high-quality tourist attractions in Shandong Province, China.

Optimizing the spatial layout of high-quality tourist attractions is of great significance in the sustainable development of the tourism industry. This work employs the ArcGIS spatial analysis tool to study the form, equality, and density of the spatial distribution of the 892 3A+ tourist attractions (high-quality tourist attractions hereafter) in Shandong Province, China. It also examines the factors influencing the spatial distribution of tourist attractions from the perspectives of geographic features and landscapes, culture and heritage, socioeconomic development, and transportation. We therefore find the following: 1) High-quality tourist attractions in Shandong Province have obvious clustering in spatial distribution with the high-density areas mainly concentrated in Qingdao, Jining, Jinan, Tai'an and other cities. Influenced by resource endowment and economic development, the two major geographical areas in Central Shandong and Jiaodong Peninsula have the most concentrated distribution of high-quality tourist attractions. 2) The distribution of high-quality tourist attractions shows a southwest‒northeast clustering direction; Qingdao is a high-high clustering area, and Heze is a low-high clustering area with low uniformity of spatial distribution and obvious spatial divergence. 3) Tourist attractions show an obvious "N" type high-density distribution belt and nuclear density distribution across the three existing agglomeration centers in the Jining-Tai'an intersection, Binzhou-Dongying intersection, and Qingdao Jiaozhou Bay coast. 4) Topography, climate conditions, history and culture are intrinsic factors affecting the spatial distribution of tourist attractions, while socioeconomic and transportation conditions are external requirements for the development thereof; collectively, they constrain the spatial distribution of high-quality tourist attractions.

DETERMINING TRANS JOGJA BUS STOP LOCATIONS USING A COMBINATION OF SPATIAL ANALYSIS AND FUZZY LOGIC

<p><em>The increase in population from migration coupled with natural growth resulted in a high population density in Yogyakarta City, with a negative impact of traffic congestion due to the number of vehicles that were not proportional to road capacity. One of the efforts to lessen congestion was through efficient public transportation. Trans Jogja can be one of the solutions to this problem. However, despite having up to 90 stops in Yogyakarta City, the existing stop locations are not evenly distributed. Thus, in order to propose appropriate bus stop locations, this study aimed to utilize the weighting and utilization of GIS, based on a number of factors usithe ng fuzzy logic method. The result showed that the weight of eh parameter also influenced locations with a high suitability. Values with a high match were pixel values ranging from 6.824 to 9.49. The location of high suitability was close to the road around the location with a high level of crowds, such as office areas, shopping centers, hotels, educational facilities, and tourism. This study proved that fuzzy logic could be used as a tool in spatial analysis to obtain criteria for a location by considering the probability of correctness of each selected parameter.</em></p>