Spatial Index Research Articles

Spatial inequity index for essential facility access at building-level in highly dense urban areas

Equity and inclusion continue to pose significant challenges for many cities. Disparities in access to resources and facilities among the residents of these areas are often influenced by factors such as geographical location and income level, among others. Therefore, creating a quantitative basis to detect spatial and socio-economic inequities in residents' access to vital infrastructures is crucial for advancing smart cities. This paper introduces a novel measure of Spatial Inequity that is based on 1) computing building-level scores for each building in terms of access to facilities, and 2) comparing the distribution of the scores of these facilities using the Gini coefficient. The proposed building-level scores will enable the identification of GIS hotspots at a high resolution. Then, the Gini-based measure will facilitate a comparison of the distribution of the scores, and determine if they are evenly distributed and equitably accessible. The Gini coefficient was highest (0.89) for waste management and elderly care, and lowest (0.56) for hospitals, reflecting varying levels of inequality. This study applies this Spatial Ineqity methodological framework to Hong Kong, a representative of Highly Dense Cities. The framework benchmarks urban infrastructure, identifies hotspots, and selects interventions for equitable urban development.

Utilizing virtual reality before, versus during, the COVID-19 pandemic

As the COVID-19 pandemic abruptly pushed interior design (ID) instruction online, instructors were challenged to adapt, and students adapted a new method of virtual reality (VR). The VR method before COVID-19 was a Homido V2 VR headset with iPhone viewing 360-degree panorama jpeg, and during COVID-19 a liquid crystal display (LCD) computer monitor viewing 360-degree panorama jpeg. The purpose of this study was, if a statistically significant difference (SSD) in spatial presence was found between the two types of VR, then an argument could be supported to evaluate spatial presence, before VR is implemented into ID curriculum. This study was at one Midwestern United States university with a sample (N = 52) of ID undergraduate students. The results revealed an SSD in the spatial presence in the aforementioned VR types. This SSD was found in two of the three dependent variables: Spatial Presence: Possible Action (SPPA; U = 772, p < 0.001), example survey question feeling you could jump into the action, and Spatial Presence: Self Location (SPSL; U = 789, p < 0.001), example feeling you are in the middle of the action. The third dependent variable, Spatial Situation Model (SSM; U = 1320, p = 0.834) did not reveal an SSD, example imagining the arrangement of the spaces. To support results, the Virtual Reality Spatial Presence Index (VRSPI) applied scored neutral (neither strong, nor weak) for spatial presence in both. This study filled research gaps on VR spatial presence measurement, with implications supporting a measurable advantage in ID students using VR headsets and ID curriculum developers evaluating VR before implementation.

Simulating Urban Expansion from the Perspective of Spatial Anisotropy and Expansion Neighborhood

Exploring spatial anisotropy features and capturing spatial interactions during urban change simulation is of great significance to enhance the effectiveness of dynamic urban modeling and improve simulation accuracy. Addressing the inadequacies of current cellular automaton-based urban expansion models in exploring spatial anisotropy features, overlooking spatial interaction forces, and the ineffective expansion of cells due to traditional neighborhood computation methods, this study builds upon the machine learning-based urban expansion model. It introduces a spatial anisotropy index into the comprehensive probability module and incorporates a gravity-guided expansion neighborhood operator into the iterative module. Consequently, the RF-CNN-SAI-CA model is developed. Focusing on the 21 districts of the main urban area in Chongqing, the study conducts comparative analysis and ablation experiments using different models to simulate the land use changes between 2010 and 2020. Different model comparison results show that the recommended model in this study has a Kappa value of 0.8561 and an FOM value of 0.4596. Compared with the RF-CA model and the FA-MLP-CA model, the Kappa values are higher by 0.0407 and 0.1577, respectively, while the FOM values are improved by 0.0529 and 0.0654, respectively. Ablation experiment results indicate that removing gravity, SAI, and expansion neighborhood operators leads to a decrease in both Kappa and FOM values. These findings demonstrate that the RF-CNN-SAI-CA model, based on the expanded neighborhood iteration algorithm, effectively integrates spatial anisotropy features, captures spatial interaction forces, and resolves neighborhood cell failure issues, thereby significantly improving simulation effectiveness.

Chronological age-based vs. Bio-banded friendly football tournament: Effects on physical performance and spatial exploration behaviour in 7v7 match play

ABSTRACT Biological age-based competitions have been suggested to mitigate maturation bias among youth football players. This study analysed the physical demands and spatial exploration behaviour when playing football matches against biological or chronological age-based opposition. One-hundred and sixteen U-13 and U-14 male regional-level football players were recruited from five Portuguese football academies. Each player completed eight games, four per condition (chronological and biological-based), on the same day. Games were played in 7 vs. 7 formats, with 20-min duration, and conformed to standard officiating and rule procedures. The following variables were considered from GPS-data in each game: distance covered, high-intensity accelerations and decelerations, body impacts, and average and peak speeds. Furthermore, average, and peak heart rates (HRpeak) and spatial exploration index were collected. Players covered significantly less distance (ES = 1.11), performed fewer decelerations (ES = 0.82), achieved lower average speeds (ES = 1.15), had fewer body impacts (ES = 0.94), and recorded lower HRpeak (ES = 0.56) in the biological age-based competition compared to the chronological-age-based. No significant differences were found between the two formats in spatial exploration. Both game formats offer distinct physical demands at the external and internal levels. Finally, practitioners can manipulate the type of opposition to get distinct physical demands to mitigate the effect of maturation bias during development.

Study on Spatio-Temporal Indexing Model of Geohazard Monitoring Data Based on Data Stream Clustering Algorithm

Yunnan Province, residing in the eastern segment of the Qinghai–Tibet Plateau and the western part of the Yunnan–Guizhou Plateau, faces significant challenges due to its intricate geological structures and frequent geohazards. These pose monumental risks to community safety and infrastructure. Unfortunately, conventional spatial indexing methods struggle with the enormous influx of geohazard data, exhibiting inadequacies in efficient spatio-temporal querying and failing to meet the swift response imperatives for real-time geohazard monitoring and early warning mechanisms. In response to these challenges, this study proffers a cutting-edge spatio-temporal indexing model, the BCHR-index, undergirded by data stream clustering algorithms. The operational schema of the BCHR-index model is bifurcated into two stages: real-time and offline. The real-time phase proficiently uses micro-clusters shaped by the CluStream algorithm in unison with a B+ tree to construct indices in memory, thereby satisfying the exigent response necessities for geohazard data streams. Conversely, the offline stage employs the CluStream algorithm and the Hilbert curve to manage heterogeneously distributed spatial objects. Paired with a B+ tree, this framework promotes efficient spatio-temporal querying of geohazard data. The empirical results indicate that the indexing model implemented in this study affords millisecond-level responses when faced with query requests from real-time geohazard data streams. Moreover, in aspects of spatial query efficiency and data-insertion performance, it demonstrates superior results compared to the R-tree and Hilbert-R tree models.

Spatiotemporal analysis of urban development patterns and COVID-19 in Mashhad, Iran

This study evaluates the impact of city spatial development patterns on the spread of airborne pandemics, focusing on Mashhad, Iran. Using ArcGIS spatial statistics, 91 spatial indices were examined between March 5, 2020, and October 2, 2021, during five distinct waves of the virus, revealing that private vehicle ownership and limited use of public transportation did not significantly reduce COVID-19 infection rates in Mashhad. Instead, factors such as proximity to transportation hubs, healthcare facility polarization, and retail shop distribution had a significant impact. Surprisingly, demographic vulnerability and conventional population-related factors had relatively minor effects. The study also highlights the influence of building concentration with high floor area ratios (FAR) and the impact of local authorities' optimism in reducing social distancing measures, especially in travel and traffic control, on the increased spread of the virus during recent waves.

Ground reaction forces better than center of pressure differentiate postural control between young female volleyball players and untrained peers

A comprehensive explanation of the relationship between postural control and athletic performance requires compare body balance in athletes with their never training counterparts. To fill this gap in relation to volleyball, the aim of this study was to compare the balance of intermediate adolescent female players (VOL, n = 61) with inactive peers (CON, n = 57). The participants were investigated in normal quiet stance during 20 s trials on a Kistler force plate. The traditional spatial (amplitude and mean speed) and temporal (frequency and entropy) indices were computed for ground reaction forces (GRF) and center-of-pressure (COP) time-series. The spatial parameters of the both time-series did not discriminate the two groups. However, the temporal GRF parameters revealed much lower values in VOL than in CON (p < .0001). This leads to three important conclusions regarding posturography applications. First, GRF and COP provide different information regarding postural control. Second, measures based on GRF are more sensitive to changes in balance related to volleyball training and perhaps to similar training and sports activity regimens. And third, the indicators calculated based on these two time series can complement each other and thus enrich the insight into the relationship between balance and sports performance level.

Geographic Information System-Based Comprehensive Shared Micromobility Station Siting Optimization for Small Urban Areas

Shared micromobility travel modes such as dockless e-scooters and bikeshare programs have become increasingly popular in the U.S. in the last decade because of their potential to improve multi-modal accessibility within communities. Smaller urbanized areas with lower population densities and fewer resources for system planning, operation, and maintenance present unique challenges with siting optimal station/service area locations. This research develops a comprehensive geographic information system (GIS)-based methodology for optimizing micromobility stations/service area locations using available and rasterized geospatial data to capture bikeshare demand indicators. Inputs are prioritized by overall importance according to the results of a survey of transportation professionals, with weights calculated by an analytic hierarchy process. These different factors are combined to create a new spatial index value to identify hotspots for candidate station/service area locations, which can be further analyzed to choose optimal locations based on the budgeted quantity of station/service area locations and ideal spacing between stations/service areas. The case study of the methodology is presented on a bikeshare station siting study in Iowa City, Iowa, U.S., using data from the Metropolitan Planning Organization of Johnson County. This research seeks to improve shared micromobility station/service area planning to better orient service to a variety of transportation goals including regular/commuting use, recreational use, equity, first/last mile connection to transit, and operational partnership opportunities. Multimodal travel times and job accessibility in the study area are evaluated both before and after the introduction of bikeshare, and both greatly improve with the introduction of optimal stations. Public agencies could expect to benefit from this comprehensive methodology because it uses easily obtainable data sources and provides the flexibility to weight the importance of factors differently to fit their communities’ specific transportation goals.

Spatial intratumor heterogeneity of programmed death-ligand 1 expression predicts poor prognosis in resected non-small cell lung cancer.

We quantified the pathological spatial intratumor heterogeneity of programmed death-ligand 1 (PD-L1) expression and investigated its relevance to patient outcomes in surgically resected non-small cell lung carcinoma (NSCLC). This study enrolled 239 consecutive surgically resected NSCLC specimens of pathological stage IIA-IIIB. To characterize the spatial intratumor heterogeneity of PD-L1 expression in NSCLC tissues, we developed a mathematical model based on texture image analysis and determined the spatial heterogeneity index of PD-L1 for each tumor. The correlation between the spatial heterogeneity index of PD-L1 values and clinicopathological characteristics, including prognosis, was analyzed. Furthermore, an independent cohort of 70 cases was analyzed for model validation. Clinicopathological analysis showed correlations between high spatial heterogeneity index of PD-L1 values and histological subtype (squamous cell carcinoma; P < .001) and vascular invasion (P = .004). Survival analysis revealed that patients with high spatial heterogeneity index of PD-L1 values presented a significantly worse recurrence-free rate than those with low spatial heterogeneity index of PD-L1 values (5-year recurrence-free survival [RFS] = 26.3% vs 47.1%, P < .005). The impact of spatial heterogeneity index of PD-L1 on cancer survival rates was verified through validation in an independent cohort. Additionally, high spatial heterogeneity index of PD-L1 values were associated with tumor recurrence in squamous cell carcinoma (5-year RFS = 29.2% vs 52.8%, P < .05) and adenocarcinoma (5-year RFS = 19.6% vs 43.0%, P < .01). Moreover, we demonstrated that a high spatial heterogeneity index of PD-L1 value was an independent risk factor for tumor recurrence. We presented an image analysis model to quantify the spatial intratumor heterogeneity of protein expression in tumor tissues. This model demonstrated that the spatial intratumor heterogeneity of PD-L1 expression in surgically resected NSCLC predicts poor patient outcomes.

EXPERIMENTING TIN DATA STRUCTURE FOR REPRESENTING PLANE SURFACE AND SUBSURFACE SPATIAL OBJECTS - PRELIMINARY WORK

Abstract. Understanding and analyzing complex spatial objects involves the integration of surface, terrain, and subsurface data into 3D spatial models. However, describing and efficiently analyzing the connections between these data model is particularly challenging. This study aims to generate TIN data structure that enable the 3D representation of surface terrain subsurface integration. Furthermore, to ensure accurate representation within a unified 3D model, spatial relationships between the TIN data model could be obtained. Consequently, three – dimensional Triangular Irregular Networks (3D TIN) mesh of spatial objects representations and indexing structures were created. The integration of spatial objects, encompassing surface, terrain, and subsurface elements, is realized through a unified model. This synergy is achieved by combining the distinct 2D and 3D topology structures of each component. The resulting comprehensive model captures the intricate relationships and interactions within the spatial environment. This study provides additional insight into the representation and analysis of surface, terrain, and subsurface management in 3D spatial models. Hence, better decision making, resource management, underground utility installations, and evaluation of spatial objects will be made possible in our future work.

How Much Do Urban Terminal Delivery Paths Depend on Urban Roads – A Research Based on Bipartite Graph Network

The structural deficiencies of the terminal delivery path often make it the main culprit of urban traffic congestion and environmental pollution. Traditional studies of express networks regarded them as an independent entity, ignoring the endogenous role of urban road network morphology and structure. To solve this problem, this paper explored the spatial dependency of terminal delivery routes in Xi'an City based on the idea of a bipartite graph network. A spatial dependency matrix of delivery paths–urban roads was constructed by abstracting delivery paths as node-set A and urban roads as node-set B. In addition, three spatial dependencies indexes, including degree centrality, betweenness centrality and closeness centrality were introduced to analyse the coupling features of these two objects. The results show that these dependency measures can reflect the coupling features of urban terminal delivery paths and urban roads. Firstly, degree centrality demonstrates terminal delivery path coverage and coupling hierarchy and scale-free nature. Secondly, betweenness centrality presents the road utilisation balance of terminal delivery paths. Thirdly, closeness centrality explains how easy it is for delivery paths to connect with others.

Application of MSPA-MCR models to construct ecological security pattern in the basin: A case study of Dawen River basin

Building an ecological security pattern (ESP) can safeguard and improve the structure, functions and services of regional ecosystems. Furthermore, it is also one of the significant spatial methods to mitigate the conflict between ecological conservation and economic growth. Most previous researches on the construction of ESP have directly utilized extensive regions of forestland, grassland, water area or nature reserves as ecological sources (ESs) without weighing the habitat quality (HQ) and ecological protection red line (EPRL). The ES of the Dawen River basin (DRB) was identified through morphological spatial pattern analysis (MSPA) and landscape connectivity index. By employing the minimum cumulative resistance model (MCR), the potential ecological corridor (EC) was identified, and its importance was evaluated by gravity model. Simultaneously, the spatial properties of HQ and ecological resistance (ER) were analyzed. Finally, this paper proposed an optimization strategy for ESP in accordance with the EPRL. The results indicate that: (1) The DRB contained 15 important ESs, mainly concentrated in water and mountain forest areas. (2) 105 ECs were extracted by MCR and gravity model, including 35 level 1 corridors and 70 level 2 corridors, which were primarily concentrated in the eastern region of the basin. (3) Based on the spatial characteristics of HQ and ER, as well as the EPRL in the research, the ESP of “one axis, two belts and five zones” in the DRB was constructed and control strategies were proposed. This research offers some reference for ecological environment protection and restoration and regional sustainable development in the Yellow River basin.

A non-axisymmetric temperature field reconstruction method based on the compressive sensing background oriented Schlieren method

Measurement of the combustion temperature field is an extremely important issue in industrial production. Temperature is one of the key parameters in combustion studies. With the temperature field distribution of the combustion field obtained, heat transfer, heat convection, and heat radiation can be calculated directly and efficiently. Traditional background oriented Schlieren (BOS) is an effective method for non-axisymmetric temperature field measurements, but it requires simultaneous Schlieren imaging at multiple angles for tomographic reconstruction, which will greatly limit its application. In this paper, the compressive sensing algorithm is introduced into the temperature field reconstruction, which establishes the system of equations between the deflection angle and the refractive index gradient. Then, the reconstruction of the non-axisymmetric temperature field is realized by solving the underdetermined system of equations by the method of solving the sparse solution through the compressive sensing. First, light offsets across the non-axisymmetric temperature field are measured by the under-angled BOS system and image processing method. Second, the spatial refractive index field is reconstructed by the compressive sensing BOS method proposed in this paper. Finally, the spatial temperature field is obtained. The experimental results show that by comparing the iRadon reconstruction algorithm and the compressive sensing reconstruction algorithm, the temperature field reconstructed by the compressive sensing under the condition of the under-angled sampling of projection data had a higher accuracy than that reconstructed by the tomographic reconstruction algorithm under the same condition. The average error of the temperature field was reduced from 34.6 to 29.7 K under the same measurement conditions; however, the accuracy is better maintained by using the compressive sensing algorithm under the condition of undersampling projection.

Spatial Accessibility to Primary Care in Metropolitan France: Results Using the SCALE Spatial Accessibility Index for All Regions.

Accessibility to care is a major public health issue. Various tools to assess it are available, but they do not solve the problem of scale. Moreover, accessibility is a multidimensional concept that is not taken into account with current tools. The SCALE index aims to overcome these two limitations by proposing a synthetic measure on a more precise scale than the administrative unit or the sub-municipal scale. Under the assumption of access to care facilities for all and access to the nearest facilities, the potential accessibility distance was calculated for each couple (residential area, accessible facilities). This was defined as the average distance by road that the population has to travel to access care. To take the availability of resources into account, these distances were weighted by the theoretical pressure on the facilities. The SCALE index was then calculated using a linear combination of the distances of potential accessibility to care facilities It highlights differences in accessibility at the national and regional scale. Using this index, it was possible to provide maps for all French regions and the major cities in a story-map. The major conurbation around Paris and the main urban centers has high accessibility. Low accessibility forms a "Y" shape. In conclusion, the SCALE index measures accessibility at the scale of a small geographic unit taking the proximity and the availability of health professionals into account. It is also possible to take into account the diversity of accessibility in a given territory.

Application of clustering strategy for automatic segmentation of tissue regions in mass spectrometry imaging.

Mass spectrometry imaging (MSI) has been widely used in biomedical research fields. Each pixel in MSI consists of a mass spectrum that reflects the molecule feature of the tissue spot. Because MSI contains high-dimensional datasets, it is highly desired to develop computational methods for data mining and constructing tissue segmentation maps. To visualize different tissue regions based on mass spectrum features and improve the efficiency in processing enormous data, we proposed a computational strategy that consists of four procedures including preprocessing, data reduction, clustering, and quantitative validation. In this study, we examined the combination of t-distributed stochastic neighbor embedding (t-SNE) and hierarchical clustering (HC) for MSI data analysis. Using publicly available MSI datasets, one dataset of mouse urinary bladder, and one dataset of human colorectal cancer, we demonstrated that the generated tissue segmentation maps from this combination were superior to other data reduction and clustering algorithms. Using the staining image as a reference, we assessed the performance of clustering algorithms with external and internal clustering validation measures, including purity, adjusted Rand index (ARI), Davies-Bouldin index (DBI), and spatial aggregation index (SAI). The result indicated that SAI delivered excellent performance for automatic segmentation of tissue regions in MSI. We used a clustering algorithm to construct tissue automatic segmentation in MSI datasets. The performance was evaluated by comparing it with the stained image and calculating clustering validation indexes. The results indicated that SAI is important for automatic tissue segmentation in MSI, different from traditional clustering validation measures. Compared to the reports that used internal clustering validation measures such as DBI, our method offers more effective evaluation of clustering results for MSI segmentation. We envision that the proposed automatic image segmentation strategy can facilitate deep learning in molecular feature extraction and biomarker discovery for the biomedical applications of MSI.

Study on the Coordinated Development of Tourism Industry–Regional Economy–Ecological Environment in the Yili River Valley

This study aims to clarify the relationship among the three systems of tourism industry, regional economy, and ecological environment in the Yili River Valley, which is essential for the sustainable development of the region. We explore the spatio-temporal evolution characteristics and influencing factors of the tourism–economy–ecosystem in the Yili River Valley using the center of gravity model, spatial mismatch index model, spatial variance model, and obstacle degree model, based on panel data from 2010 to 2019. The study shows that (1) the comprehensive development indices of the tourism industry, regional economy, and ecological environment in the Yili River Valley exhibit varying degrees of growth trends. Smaller increases are observed in ecological environment indices, while larger increases are found in regional economy and tourism industry indices. Both the regional economy and tourism industry indices share a strong correlation and similar development trends. (2) At the macro-regional scale, during the study period, the centers of gravity of the tourism industry, regional economy, and ecological environment in the Yili River Valley have consistently been separated from the spatial geometric center of the region. The degree of misalignment of the centers of gravity of the three systems as a whole has gradually reduced, indicating that the level of coordinated development of the region has continuously improved. (3) At the micro-regional scale, during the study period, most of the spatial misalignment indices of the Yili River Valley regions showed positive and negative fluctuations, and the types of misalignment varied to different degrees. However, the gap in spatial misalignment indices between different regions gradually narrowed, and the divergent characteristics of “east-west concentration, north-south dispersion” were gradually broken, with the overall indices showing a balanced development trend. (4) The spatial dislocation index of each region in the Yili River Valley is affected by multiple systems and factors, and the overall spatial dislocation factors are mainly the comprehensive utilization rate of general solid waste, exhaust gas emission, dust removal amount, etc., which mostly originate from the ecological environment system.

High Spatial Resolution Leaf Area Index Estimation for Woodland in Saihanba Forestry Center, China

Owing to advancements in satellite remote sensing technology, the acquisition of global land surface parameters, notably, the leaf area index (LAI), has become increasingly accessible. The Sentinel-2 (S2) satellite plays an important role in the monitoring of ecological environments and resource management. The prevalent use of the 20 m spatial resolution band in S2-based inversion models imposes significant limitations on the applicability of S2 data in applications requiring finer spatial resolution. Furthermore, although a substantial body of research on LAI retrieval using S2 data concentrates on agricultural landscapes, studies dedicated to forest ecosystems, although increasing, remain relatively less prevalent. This study aims to establish a viable methodology for retrieving 10 m resolution LAI data in forested regions. The empirical model of the soil adjusted vegetation index (SAVI), the backpack neural network based on simulated annealing (SA-BP) algorithm, and the variational heteroscedastic Gaussian process regression (VHGPR) model are established in this experiment based on the LAI data measured and the corresponding 10 m spatial resolution S2 satellite surface reflectance data in the Saihanba Forestry Center (SFC). The LAI retrieval performance of the three models is then validated using field data, and the error sources of the best performing VHGPR models (R2 of 0.8696 and RMSE of 0.5078) are further analyzed. Moreover, the VHGPR model stands out for its capacity to quantify the uncertainty in LAI estimation, presenting a notable advantage in assessing the significance of input data, eliminating redundant bands, and being well suited for uncertainty estimation. This feature is particularly valuable in generating accurate LAI products, especially in regions characterized by diverse forest compositions.

Spatial variability and risk assessment of potentially toxic elements in the effluent of Kumasi Abattoir [sbnd] Ghana

Hypothesis-driven experiments have been conducted to explore the spatial variability and potential ecological risk indices (RIs) of potentially toxic elements (PTEs) in untreated in situ effluent continuously discharged downstream by the Kumasi Abattoir Ghana (KAG). The study focused on seven PTEs nickel (Ni), lead (Pb), cadmium (Cd), manganese (Mn), zinc (Zn), copper (Cu), and iron (Fe), based on these hypotheses: (i) Variations in PTE levels may be significant in the effluent; (ii) Comparative RI assessments based on the three background values natural background values (NBVs) for freshwater, maximum admissible concentrations (MACs), and reference values (RVs) provide objective evaluations; and (iii) PTEs’ cluster distances are linked to RIs. Effluent samples were randomly collected in quintuplicates from the drainage receptacle and analyzed using standard methods. The spatial variability of PTEs was investigated using divisive hierarchical cluster analysis (DHCA). The RIs and their uncertainties were computed using Hakanson's equations and standard deviation (SD). PTE dispersion was analyzed using the coefficient of variation (CV). Analysis of variance (ANOVA) was employed to test for the significance of hypotheses 1 and 2, complemented by Scheffé's post-hoc test where significance was detected. Hypothesis 3 was investigated by analyzing PTE cluster distances and RIs based on Pearson's correlation metric. The DHCA produced two clusters Cd–Cu–Mn–Ni and Zn-(Fe)-Pb. The CVs (< 50 %) indicated medium variability, relatively uniform spatial distributions, and minimal fluctuations. High to very high RIs ± SDs obtained were 300 ± 57.5 (MACs), 2900 ± 500 (RVs), and 7666 ± 2980 (NBVs). The ANOVA yielded significant results for both hypotheses, while moderate to high correlations were obtained for the third hypothesis. While RIs provided compelling evidence of inimical effluent, the study highlights the influence of background values on RIs. It further suggests that the PTE cluster distances may be used as surrogates for the RIs.

How Game Ecology Drives Player Choices in Strategy Video Games: An Environmental Determinism Perspective

Background Recent years have witnessed increasing attention on the study of game ecology, the virtual environments, and underlying mechanics within games. Yet, these studies often focus on the active role that players have in changing game ecology, while overlooking the reciprocal influence between game ecology and players. Methods This study investigates the potential impact of game ecology on player choices from an environmental determinism perspective—the development of humanity is shaped by the physical environment. This new perspective is substantiated by a case study of two strategy games, Sid Meier's Civilization VI and Humankind, using an immersive-participatory method to analyze their game mechanics. Results This study identifies four major forms of game mechanics that use game ecology to influence player choices: spatial constraints, movement modifiers, uncommon events, and dynamic indexes. Discussion This study discusses how the identified game mechanics reflect environmental determinism and contrast with the conventional ideology of cultural determinism. Additionally, environmental determinism in video games, derived from a broader social background, is constantly evolving into new forms. Conclusion This study introduces a new perspective of environmental determinism for analyzing the interaction between game ecology and players. This approach has the potential to offer valuable insights into guiding future game design and research endeavors.

Study on multi-scenarios regulating strategy of land use conflict in urban agglomerations under the perspective of “three-zone space”: a case study of Harbin-Changchun urban agglomerations, China

Introduction: Against the background of rapid global urbanization, the urban space expansion has led to increasingly acute land-use conflicts. Accurately understanding the spatial and temporal evolution characteristics of land-use conflict patterns and measuring the level of spatial conflicts are crucial for intensifying sustainable use and management of land resources. Existing research focuses on analyzing the current status of land-use conflicts, while there is limited discussion on tracking spatial-temporal dynamic patterns and simulating future conflict trends.Method: In this paper, the level of spatial conflict was measured by constructing a spatial comprehensive conflict index (SCCI) model, and the spatio-temporal evolution characteristics of land use conflict in Harbin-Changchun urban agglomeration from 2000 to 2020 are discussed. In addition, the PLUS model is used to simulate and predict the pattern of land use conflict in 2030, finally put forward the control strategy.Results: Result shows that: (1) Over the past 20 years, urban space has expanded rapidly with two provincial capitals as growth poles. Land-use conflicts generally exhibit a distribution pattern of “high in the west and low in the east, high in the core and low in the periphery.” (2) During the past 20 years, conflicts have intensified initially and then eased. Severe uncontrolled conflicts are concentrated in the transition zone between urban and rural areas of urban agglomerations. (3) Agricultural space is the core resource and key area contested by different interest subjects in the process of land development and utilization.Discussion: The agricultural space priority scenario proves to be the most effective in controlling spatial conflicts. However, a single space priority scenario cannot adequately address multiple spatial rights and interests. Therefore, different spatial management modes should be implemented in different areas. The purpose of this paper is to provide scientific strategies and suggestions for controlling land-use conflicts in urban agglomerations and achieving sustainable development of regional land use.