Geographic Information System Network Research Articles

Coupling effect of key factors on ecosystem services in border areas: a study of the Pu'er region, Southwestern China.

The coupling effects created by transboundary and local factors on ecosystem services are often difficult to determine. This poses great challenges for ecosystem protection and management in border areas. To decrease uncertainty, it is crucial to quantify and spatialize the impact multiple factors have on ecosystem services within different scenarios. In this study, we identified key transboundary and local factors from a set of 15 sorted factors related to four main ecosystem services. We employed a Bayesian Network-Geographic Information System (BN-GIS) model to simulate 90 scenarios with multiple factors combinations, quantifying and spatializing the coupling effects on the main ecosystem services. These simulations were conducted in the Pu'er region, which is situated alongside three countries, and serves as a representative border area in southwest China. The results showed that: (1) The coupling effects of multiple factors yield significant variations when combined in different scenarios. Managers can optimize ecosystem services by strategically regulating factors within specific areas through the acquisition of various probabilistic distributions and combinations of key factors in positive coupling effect scenarios. The outcome is a positive coupling effect. (2) Among the four main ecosystem services in the Pu'er region, food availability and biodiversity were affected by key transboundary and local factors. This suggests that the coupling of transboundary and local factors is more likely to have a significant impact on these two ecosystem services. Of the 45 combination scenarios on food availability, the majority exhibit a negative coupling effect. In contrast, among the 45 combination scenarios on biodiversity, most scenarios have a positive coupling effect. This indicates that food availability is at a higher risk of being influenced by the coupling effects of multiple factors, while biodiversity faces a lower risk. (3) Transboundary pests & diseases, application of pesticides, fertilizer & filming , population density, and land use were the key factors affecting food availability. Bio-invasion, the normalized differential vegetation index, precipitation, and the landscape contagion index were the key factors affecting biodiversity. In this case, focusing on preventing transboundary factors such as transboundary pests & disease and bio-invasion should be the goal. (4) Attention should also be paid to the conditions under which these transboundary factors combine with local factors. In the areas where these negative coupling effects occur, enhanced monitoring of both transboundary and local factors is essential to prevent adverse effects.

Assessment of Disparity in Accessing Railway Stations in Thailand: an Application Geographic Information System Network Analysis

Ensuring that people from all regions in a country can easily access railway stations is a crucial objective in the development of transportation and mobility systems and is essential for the convenient travel within the country. Furthermore, accurately assessing accessibility in disadvantaged areas is pivotal in designing and implementing development policies for equitable and sustainable transportation systems. Although various methods and tools are available for measuring accessibility, interpreting local-level data remains challenging. This study was conducted in Thailand, a country with severe accessibility disparities but undergoing considerable development. Therefore, the objective of this study was to evaluate accessibility disparities to analyze accessibility capabilities and inequality of railway stations. The objective of the study was to aid the development of inequality and railway services to cover all areas. The geographic information system methodology with network analysis tools was used to visualize and analyze spatial accessibility data and identify regional disparities. The results of this study indicate inequality in accessing railway services, which revealed that densely populated areas have superior access to railway stations compared with districts with a lower population density. Additionally, over 41% of the study area’s population resides more than half an hour away from access to a railway station. This study highlights regional inequality in accessing railway station services. Furthermore, strategies to alleviate this inequality are presented, and the findings will aid government agencies and railway planners in designing critical strategies or formulating policies for improving accessibility to rail transportation systems and reducing inequality in accessing railway services.

Wireless Intelligent Sensor Network in Dynamic Environmental Monitoring of Archaeological Excavation Site

This paper intends to apply the Intelligent Wireless Sensor Network (IWSN) to the archaeological excavation site and make the dynamic site of archaeological excavation can be monitored intelligently in real-time. This paper firstly discusses the structure and function of Wireless Sensor Network (WSN). Secondly, according to the problem of limited dynamic monitoring of WSN, an IWSN is established. The dynamic environmental monitoring of archaeological excavation sites is achieved by using Geographic Information System (GIS) and remote sensing technology combined with IWSN. Finally, this paper analyses the environmental monitoring results of the archaeological excavation site, the dynamic environmental monitoring location of the archaeological excavation site of the IWSN, and the simulation results of the Intelligent Wireless Sensor Network-Geographic Information System (IWSN-GIS) in the dynamic environmental monitoring of the archaeological excavation site. The results show that the Inverse Distance to a Power (IDP) algorithm in temperature and humidity monitoring data on the overall downward trend, the algorithm performance is relatively stable, and the algorithm performance is better than the other two algorithms. The monitoring performance of IWSN-GIS system is better than that of IWSN system. It shows that with the increase in the communication distance of sensor nodes, the relative error of different monitoring systems for the dynamic monitoring position of the archaeological excavation site is decreasing. IWSN-GIS can monitor the environment before, during, and after the archaeological site. This paper realizes the intelligent, scientific, and technological archaeology of archaeological excavation through IWSN, GIS, and remote sensing image technology, and provides technical support for digital archaeology in archaeological research.

Mapping and Prioritizing Potential Illegal Dump Sites Using Geographic Information System Network Analysis and Multiple Remote Sensing Indices

Due to rapid urbanization and population growth, identification and management of illegal dump sites has been a global challenge. In this study, satellite imagery and geographic information system were used to map potential illegal dump sites (PIDS). An original analytical approach was developed to identify PIDS using a set of remote sensing indices and vector files. The Network Analysis tool was used to prioritize PIDS considering driving distance between PIDS and neighboring populated points. A total of five variables (Landfills, LST, HCHO, Highways, and EVI) were considered. A study area in Saskatchewan, Canada, was selected, and the identified PIDS account for about 37.3% of the total area. Road network intensity and accessibility appear important to the occurrence of PIDS. Overall road densities in identified PIDS ranged from 0.098 to 0.251 km/km2. All five variables have observable effects on the occurrence of PIDS; however, LST and highways are recommended for future studies due to their higher membership grade and spatial sensitivity. The combination of multiple remote sensing indices and network analysis on PIDS prioritization is advantageous. The proposed PIDS mapping and prioritization method can be easily employed elsewhere.

Optimization model for cost estimation of decentralized wastewater treatment units

In this study, an optimization model, which is a combination of Genetic algorithm (GA) and geographical information system (GIS) was used to find minimum construction, operation, and flow reuse cost ( Fmin) . This Fmin model will be used for the estimation of the constructional and operational costs of decentralized wastewater treatment units [DTUs] and can be used for the estimation of reusing treated municipal wastewater flow for irrigation purposes in Sulaimani city, Iraq as well. In the optimization processes, an objective function technique was used based on data from 31 small-sized DTU and the costs of reusing their effluents in 827 deferent sizes green areas [GRs] across the city. A transportation matrix model of size [31x827] was built to find the Fmin for conveying the required flow from the DTUs to the GRs destinations. Also, optimum required pipe lengths and routes for the treated flow conveying were found using GIS Network Analysis – Origin-Destination (OD) Matrix Tools. The results showed that the proposed model could successfully obtain an optimal solution. The algorithm is flexible enough to consider a variety of scenarios and compare the best options.

Urban Planning Design and Evaluation Based on GIS Information and Bayesian Network

In recent years, China’s urban construction has set off a climax, with the rapid increase of the number of cities and towns, the increasingly perfect functions of cities and towns, the continuous improvement of the level of urbanization, and the urban landscape changing with each passing day. Under the influence of many factors, urban planning and assessment are facing unprecedented pressure, leading to the inconsistency between the compilation results of planning. Under the guidance of system analysis and system dynamics, this paper designs and develops an urban planning information management system based on GIS (Geographic Information System) and Bayesian network, and then the system is applied to the field of urban planning. In this system, all kinds of data generated by planning are stored in order to realize the standardization of data, and then the urban planning work is guided by the effective management of the result data so as to know the implementation status of urban planning in time and provide the basis for the future planning and implementation.

The Comprehensive Automobile Research System (CARS) – a Python-based automobile emissions inventory model

Abstract. The Comprehensive Automobile Research System (CARS) is an open-source Python-based automobile emissions inventory model designed to efficiently estimate high-quality emissions from motor vehicle emission sources. It can estimate air pollutant, greenhouse gas, and air toxin criteria at any spatial resolution based on the spatiotemporal resolutions of input datasets. The CARS is designed to utilize local vehicle activity data, such as vehicle travel distance, road-link-level network geographic information system (GIS) information, and vehicle-specific average speed by road type, to generate an automobile emissions inventory for policymakers, stakeholders, and the air quality modeling community. The CARS model adopted the European Environment Agency's on-road automobile emissions calculation methodologies to estimate the hot exhaust, cold start, and evaporative emissions from on-road automobile sources. It can optionally utilize average speed distribution (ASD) of all road types to reflect more realistic vehicle speed variations. In addition, through utilizing high-resolution road GIS data, the CARS can estimate the road-link-level emissions to improve the inventory's spatial resolution. When we compared the official 2015 national mobile emissions from Korea's Clean Air Policy Support System (CAPSS) against the ones estimated by the CARS, there is a significant increase in volatile organic compounds (VOCs) (33 %) and carbon monoxide (CO) (52 %) measured, with a slight increase in fine particulate matter (PM2.5) (15 %) emissions. Nitrogen oxide (NOx) and sulfur oxide (SOx) measurements are reduced by 24 % and 17 %, respectively, in the CARS estimates. The main differences are driven by different vehicle activities and the incorporation of road-specific ASD, which plays a critical role in hot exhaust emission estimates but was not implemented in Korea's CAPSS mobile emissions inventory. While 52 % of vehicles use gasoline fuel and 35 % use diesel, gasoline vehicles only contribute 7.7 % of total NOx emissions, whereas diesel vehicles contribute 85.3 %. However, for VOC emissions, gasoline vehicles contribute 52.1 %, whereas diesel vehicles are limited to 23 %. Diesel buses comprise only 0.3 % of vehicles and have the largest contribution to NOx emissions (8.51 % of NOx total) per vehicle due to having longest daily vehicle kilometer travel (VKT). For VOC emissions, compressed natural gas (CNG) buses are the largest contributor at 19.5 % of total VOC emissions. For primary PM2.5, more than 98.5 % is from diesel vehicles. The CARS model's in-depth analysis feature can assist government policymakers and stakeholders in developing the best emission abatement strategies.

A BP Neural Network-Based GIS-Data-Driven Automated Valuation Framework for Benchmark Land Price

The automated valuation of benchmark land price plays an essential role in regulating land demand in Chinese real-estate market as the big data are currently accumulated rapidly. However, this problem becomes highly challenging due to the multidimension, large volume, and nonlinearity of the land price-influencing factors. In this paper, an effective data-driven automated valuation framework is proposed for valuing real estate assets by combining a GIS (geographic information system) and neural network technologies. This framework can automatically obtain the values of spatial factors affecting land price from GIS and generate training set data for training the neural network to identify the complex relationship between all kinds of factors and benchmark land prices. The effectiveness and universality of the framework is verified via the data of benchmark land prices in Wuhan. The framework can be applied for automated benchmark land price valuation in other cities.

Geographic access to lung cancer screening among eligible adults living in rural and urban environments in the United States.

Although recommended lung cancer screening with low-dose computed tomography scanning (LDCT) reduces mortality among high-risk adults, annual screening rates remain low. This study complements a previous nationwide assessment of access to lung cancer screening within 40 miles by evaluating differences in accessibility across rural and urban settings for the population aged 50 to 80years and a subset eligible population based on the 2021 US Preventive Services Task Force LDCT lung screening recommendations. Distances from population centers to screening facilities (American College of Radiology Lung Cancer Screening Registry) were calculated, and the number of individuals who had access within graduating distances, including 10, 20, 40, 50, and 100 miles, were estimated. Census tract results were aggregated to counties, and both geographies were classified with rural-urban schemas. Approximately 5% of the eligible population did not have access to lung cancer screening facilities within 40 miles; however, different patterns of accessibility were observed at different distances, between regions, and across rural-urban environments. Across all distances and geographies, there was a larger percentage of the population in rural geographies with no access. Although the rural population represented approximately 8% of the eligible population, the larger percentage of the rural population with no access was noteworthy and translated into a larger number of individuals with no access at longer distance thresholds (≥40 miles). Disparities in access should be examined as both percentages of the population and numbers of individuals with no access in order to tailor interventions to communities and increase access. Geospatial analysis at the census tract level is recommended to help to identify optimal focus areas and reach the most people. As annual lung cancer screening rates remain low, this study examines access to lung cancer screening nationwide and across rural and urban settings. A geographic information system network analysis of census tract-level populations is used to estimate access at different distances, including 10, 20, 40, 50, and 100 miles, and the results are aggregated to counties. Approximately 5% of the eligible population does not have access to screening facilities within 40 miles; however, different patterns of accessibility are observed at different distances, between regions, and across rural-urban environments. Across all distances and geographies, there is a larger percentage of the population in rural geographies with no access.

Forest Ecological Diversity Change Prediction Discrete Dynamic Model

Forest ecosystem is the most important terrestrial ecosystem type of species diversity in the world. The protection and sustainable use of forest ecological diversity is of great significance to forest protection and sustainable management. In order to maintain biodiversity, the traditional concept of forest resources management must be changed. In recent years, the mining and application of big data have become the frontier content of international ecological diversity and macroecology research. By establishing the discrete dynamic model of forest ecological diversity change, this study finds a method that can keep the number of trees stable in a short time and ensure the sustainable development of forest resources. This article studies and constructs the forest information system and constructs an open-source WebGIS (network geographic information system) scheme for the field of spatial information and supporting OGC (occupationally generated content). At the same time, the grey theory GM (1,1) model is used to predict the development trend of forest resources in China. The results show that under the promotion of policies and research projects, China’s forestry development has realized the double growth of forest area and volume, and the forest coverage rate has reached a new level.

GIS-based volunteer cotton habitat prediction and plant-level detection with UAV remote sensing

Volunteer cotton plants germinate and grow at unwanted locations like transport routes and can serve as hosts for a harmful cotton pests called cotton boll weevils. The main objective of this study was to develop a geographic information system (GIS) framework to efficiently locate volunteer cotton plants in the cotton production regions in southern Texas, thus reducing time and economic cost for their removal. A GIS network analysis tool was applied to estimate the most likely routes for cotton transportation, and a GIS model was created to identify and visualize potential areas of volunteer cotton growth. The GIS model indicated that, of the 31 counties in southern Texas that may have habitat for volunteer cotton, Hidalgo, Cameron, Nueces, and San Patricio are the counties at the greatest risk. Moreover, a method based on unmanned aerial vehicle (UAV) remote sensing was proposed to detect the precise locations of volunteer cotton plants in potential areas for their subsequent removal. In this study, a UAV was used to scan limited samples of potential volunteer cotton growth areas identified with the GIS model. The results indicated that UAV remote sensing coupled with the proposed image analysis methods could accurately identify the precise locations of volunteer cotton and could potentially assist in the elimination of volunteer cotton along transport routes.

Driving Coverage of Charging Stations for Battery Electric Trucks Located at Truck Stop Facilities

Battery-powered electric trucks could soon be deployed on a large scale along long-haul routes on the U.S. trunk highway system. These vehicles have numerous advantages, including zero emissions, fuel savings, and lower maintenance costs, that make them attractive for motor carrier companies. However, the deployment of this technology depends on the development of a convenient network of fast-charging stations that can provide sufficient driving coverage along the Interstate highway system. The majority of existing fast-charging stations in rural areas currently are not adequate for the movement of large trucks. A potential solution is to install fast-charging stations at the vast network of rural truck stops. Truck stops are specifically designed for the movement of trucks and are already located on the routes with the highest truck demand. The main objective of this study was to develop a methodology for identifying the driving coverage provided if fast-charging stations were located at truck stop facilities along the U.S. Interstate highway system. The contiguous U.S.A. was taken as the study area. The study approach was based on a geographic information system network analysis with a specific focus on the service area. It was found that truck stop facilities could potentially provide 99.5% driving coverage for electric trucks on the Interstate highway system. This makes them opportune locations for future fast-charging stations. These findings may assist transportation planners and operators in defining strategies required for planning the deployment of long-haul electric trucks on the U.S. highway system.

Estimating tree-related power outages for regional utility network using airborne LiDAR data and spatial statistics

Trees play an integral role in the “green” framework of an urban ecosystem. However, just as they are beneficial to the environment, they can pose a significant risk to utility infrastructure networks, particularly in severe weather events. The objectives of this research were to explore the effect of scale and spatial variation on the relationships between trees and utility assets for vegetation-related power outages through the incorporation of remote sensing and geographic information system (GIS) analysis. Tree location and structural metrics derived from airborne Light Detection and Ranging (LiDAR) data were combined with regional utility network GIS data to test the prediction analysis capabilities of global and local statistics at multiple scales. Pearson’s correlation was carried out to examine the relationships between tree structure and utility asset variables to vegetation-related power outages, including the effect of the resolution, or grid-cell size, on those relationships. To test the performance of global and local regression modeling on outage prediction, ordinary least square (OLS) and geographically weighted regression (GWR) models were evaluated using four explanatory variables (utility wire length, utility pole count, tree canopy area, maximum tree height) at four different grid cell scales (50 m, 500 m, 1 km, 2 km). In general, Pearson’s correlation demonstrated the strongest positive relationship between explanatory variables and power outages when only aggregating 50-m grid cells exhibiting co-location of trees and utility assets to 2-km grid cells. Local regression models performed better than global models at all scales, with GWR producing the highest adjusted R2 and lowest Akaike information criterion (AIC) values of 0.955 and 3213, respectively. Additionally, the performance of OLS and GWR models increased with scale as both models produced the highest adjusted R2 at 2-km grid-cell scale. GWR model outputs demonstrated unique spatial patterning across the study area. This research demonstrated the effect of scale and spatial variation on regression analysis for the estimation of tree-related power outages.

Topological network and GIS approach to modeling earthquake risk of infrastructure systems: A case study in Japan

In this study, we propose a network-geographic information system (GIS) modeling framework to construct a risk map for revealing infrastructure vulnerability to seismic hazards. The framework considers the cascading network of interdependencies among the effects and intermediate effects of earthquakes. Network modeling is applied to analyze infrastructure vulnerabilities and identify the infrastructure types that are vulnerable to earthquakes, while geographical modeling is applied to identify seismic hazards in terms of ground motion intensity and earthquake source faults. Reported earthquake cases in Japan are selected for analysis, and an illustrative example of the modeling of a risk map is conducted on a section of a conventional train line in Japan. In the network modeling, statistics and network diagrams are developed to analyze the relational earthquake incident data. The results demonstrate that earthquakes can cause direct and indirect effects on infrastructure, and interdependencies can be found among the effects and intermediate effects. In the geographical modeling, probabilistic distribution is adopted to predict the seismic characteristics; the results reveal the intensity occurrence probability and cascading consequences of different earthquake intensities, and predict specific degrees of destruction. Infrastructure vulnerability and seismic hazard maps are developed according to the analyzed results. A risk map is then constructed by integrating the infrastructure vulnerability map with the seismic hazard map. This study characterizes the risk of earthquakes to infrastructures, capturing the complex interdependencies to reveal the cascading effects from earthquakes, and quantifying and demonstrating infrastructure areas vulnerable to seismic hazards.

Solid Waste Management and Transport Route Optimization Using Geographic Information System in Lagos Metropolis, Nigeria

With an unprecedented population and rapid urbanization, the solid waste collection and transportation has become a major challenge in Lagos metropolis. The main objective of the study was to optimize routes for solid waste collection and disposal using Geographic Information System (GIS) in order to improve solid waste management system in Lagos metropolis. Data on the GIS Network Analyst was used to determine movements between the transfer loading stations and the landfills to determine the collection time, travel distances, optimized routes and alternative routes for solid waste disposal while maximising total solid waste collection and disposal for environmental sustainability. Based on the findings, the study therefore recommends possible interventions such as regular collection of solid waste, proper management of the transfer loading stations and landfills, and that the collection of solid waste should be given more attention because it is important in the development of cities.
 Keywords: Solid wastes, Transportation, Route Optimization, GIS, Lagos metropolis

Road versus Rail: Assessing the Implications of Transport Infrastructure for Spatial Growth Pattern in China’s Megaregions

The exacerbation of the concentration of population and economic activities in a few core metropolitan areas has become an urgent issue for China’s regional development, and necessitates a more polycentric spatial development pattern. Transportation infrastructure improvement can serve as a vital instrument to achieve the objective of polycentric spatial development. Focusing on the 10 megaregions of China, this research examines a fundamental issue for China’s urban and regional development in the context of massive investment in highway and railway: the impacts of road and rail network on the spatial pattern of growth and development. Using the county-level national censuses of 1982, 1990, 2000, and 2010 and the historical transport network geographic information system (GIS) data, this paper analyzes the differentiated effects of road and rail for population growth. The research results revealed the stronger positive growth impacts of rail compared with road, and further examination indicates that railway has stronger positive impacts for regional growth in the less-developed areas.

Integrating GIS Accessibility and Location-Allocation Models with Multicriteria Decision Analysis for Evaluating Quality of Life in Buraidah City, KSA

The present study was aimed at evaluating the quality of life in the districts of Buraidah city in the Kingdom of Saudi Arabia as part of the Kingdom’s Vision 2030, based on provided services in terms of distance and time parameters. This was achieved by integrating Geographic Information System (GIS)-based network analysis and multicriteria decision analysis (MCDA) techniques. The service area analysis technique of the GIS Network Analysis tools was applied to the available services in the districts of Buraidah city. Twelve services were investigated with regard to their coverage within 5, 10, and 15 minutes of travel time: universities, high schools, middle schools, elementary schools, hospitals, health centers, ambulance facilities, government services, religious services, security services, sports services, and recreational services. GIS-based multicriteria decision analysis (GIS-MCDA) was implemented in the study to obtain a spatial suitability map of the standard of living quality in the various districts in Buraidah city. The analytic hierarchy process (AHP) technique was used to determine the criteria weights. The location-allocation model was used in the study as well for suggestions of new service locations to improve the spatial distribution of services and enhance the quality of life in the districts. The results of the final suitability map to determine districts with the best standard of living according to the provided services show that the living standards in the city are very good, with very high and high levels in around 51 districts, representing 72.9% (449,235 people), and about 19 districts with moderate, low, and very low levels, representing 27.1% (56,765 people). Narrowing the quality of life gap and improving spatial planning requires correcting the current deficiency in services, which is estimated in this study to be 16 services: one high school, one elementary school, one middle school, two universities, one sports service, two security services, two courts, two hospitals, one health center, one park, and two ambulance facilities.

REAL-TIME ACQUISITION AND ANALYSIS METHOD AND SYSTEM OF SECURITY HIDDEN DANGER INFORMATION IN INTELLIGENT SCENIC SPOTS UNDER THE ENVIRONMENT OF INTERNET OF THINGS

Abstract. Aiming at the problem that the existing methods of collecting and processing information of potential location safety hazards in scenic spots can not identify and analyze real-time potential hazards quickly and accurately, a real-time method and system of collecting and analyzing information of potential location safety hazards for tourists in intelligent scenic spots under the environment of Internet of Things is proposed.The set of method and system includes: collecting geographical coordinate information of potential safety hazards threatening visitor through the handheld terminal, and connecting the collected data and the spatial analysis request to the GIS spatial analysis server through the Web server in real time, so as to realize the buffer analysis considering the gradient; based on the knowledge base of secure locations in scenic spots and internet of things equipment, such as rainfall-sensor and water depth sensors, the radius of hidden danger buffer and parameter information of warning level are calculated in real time, and the knowledge formed in the analysis process is saved. By comparing and analyzing the historical information of Location Safety Knowledge Base, the temporal and spatial variation rules of newly added hidden danger features are discovered to determine the optimal value of hidden danger parameters. Then, through the geographic information system network service software, the new hidden danger buffer element layer is sent to the handheld terminal in real time, so as to avoid the impact of unsafe tourist caused by the untimely updating of hidden danger information in scenic spots, and improve the efficiency of safe production in scenic spots.

GIS for planning a sustainable and inclusive community: multi-criteria suitability analysis for siting low-income housing in a sustainable community and suitable neighborhood in Buffalo Metropolitan Area, New York

This study utilized Geographic Information System (GIS) as a tool for sustainable community planning which incorporateslow-income communities into current sustainable neighborhoods. GIS suitability analysis aimed at finding suitable and sustainable neighborhoods for locating subsidized housing projects for low-income families through the Low-Income Housing Tax Credit (LIHTC) program in the Buffalo Metropolitan Area (BMA), New York. LIHTC projects allocate 30% of their rental units for low-income families. They must be located in sustainable neighborhoods to ensure economically and socially sustainable livesfor low-income inhabitants. Data was collected from various New York State government agencies and used as input for several raster-based criteria maps using GIS network analysis, raster cost-distance analysis, Kernel density analysis, raster-based local statistics, and surface analysis.The criteria of sustainable neighborhoodsusing GIS multi-criteria analysis (MCA) were grouped into five factors, namely public amenities, economic viability, population growth, property conditions, and environmental factors. A final suitability map was created by multiplying the derived raster maps with their respective weight resulting from the Analytical Hierarchical Process (AHP). The study found several locations in suburban areas of the BMA that meet the criteria and canbe usedby the Buffalo Metropolitan Housing Authority (BMHA) to site the LIHTC housing.

Evolution and Evaluation of the Guangzhou Metro Network Topology Based on an Integration of Complex Network Analysis and GIS

This paper takes the metro network of Guangzhou as a case study, and provides a quantitative analysis of the historical development of the network from 1999 to 2018. Particularly, the evolution of the topological structure of the Guangzhou Metro Network (GMN) is evaluated and characterized through the integration of geographic information system (GIS) and complex network analysis. The results show that: (1) The metro network of Guangzhou possesses the basic characteristics of small-world network, (2) with the development of GMN, the network complexity is increased and the spatial dispersion of the nodes tends to ease, but the average travel time and transfer rate continues to rise up, leading to the decreasing of the network transmission efficiency and the scattering of the nodes, (3) a good fault tolerance of the overall metro network of Guangzhou is revealed, but the spatial variance is observed, (4) the peak of degree centrality (DC) of the nodes is gradually moving northward along “Kecun Station–Guangzhou railway station–Jiahe Wanggang station”, while the peak of betweenness centrality (BC) is changing from “Kecun station” to “Jiahe Wanggang station”, and Jiahe Wanggang station has evolved into the most critical node in the current metro network of Guangzhou. In conclusion, this study should provide the scientific basis and significant decision-making support to the planning and operation management of GMN.