Proximity Factors Research Articles

Wind Power Plant Location Selection with Fuzzy Logic and Multi-Criteria Decision-Making Methods

This study aims to examine the effectiveness of the fuzzy multi-criteria decision-making methods, especially Fuzzy Weight by Envelope and Slope (F-WENSLO) and Fuzzy Ranking Alternatives with Weights of Criterion (F-RAWEC) for evaluating the potential locations for wind power plants. The study's objective is to provide a solid framework for deciding which locations are most suitable for wind energy projects, taking into account various criteria and expert opinion. The study includes the evaluation of five potential places including Gürün, Kangal, Divriği, Ulaş and Zara in Sivas province of Turkey. The evaluation criteria include wind speed and direction, altitude, land use, environmental impacts, infrastructure proximity, social acceptance, economic costs, security and risk factors, climatic conditions and legal and permit requirements. Scores from experts from various fields and weights of criteria were determined. The analysis revealed that Ulaş and Kangal got the highest point for the wind power plant installation. As Ulaş gets the highest point due to favorable wind conditions, favorable altitude and advantageous land use; Kangal stood out as a strong candidate because of its acceptable wind speed, positive social acceptance and low economic costs. The study highlights the importance of integrating multiple criteria and expert assessments in the decision-making process. The findings suggest that fuzzy multi-criteria decision-making methods can be effectively supportive of wind power plant site selection. The study provides valuable information for project managers and policy makers, emphasizing the importance of criteria such as security in the choice of location, legal requirements and social acceptance. Future research may expand these findings to examine the integration of additional criteria, alternative locations, and other multi-criteria decision-making techniques.

GIS and remote sensing-based wildlife habitat suitability analysis for mountain nyala (Tragelaphus buxtoni) species mapping at Bale Mountains National Park, Ethiopia

Ethiopia's Bale Mountains National Park protects the continent's largest remaining alpine environments. This park was first suggested in 1973 to safeguard its great biodiversity, including the endangered Mountain Nyala (Tragelaphus buxtoni) and Red Foxes. Despite these conservation measures, the lack of infrastructure and the enormous area projected have resulted in significant wildlife habitat fragmentation. The purpose of this study is to analyse the habitat appropriateness of the Mountain Nyala in Bale Mountains National Park using GIS and remote sensing technologies in order to inform conservation efforts and assist park managers in making decisions. To identify potential habitats for the Mountain Nyala, the study employed GIS spatial analyst techniques such as the Digital Elevation Model (DEM) and Landsat 9 (OLI/TIRS) data, as well as key environmental factors such as vegetation types, soil types, topographic factors (elevation and slope), climate factors (temperature), and proximity factors (distance to settlements, roads, and rivers). The Weights of these factors was calculated using IDRISI32 Multi-Criteria Evaluation (MCE) with the pair-wise comparison method. These weighted factor maps were then integrated through weighted overlay analysis to model wildlife habitat suitability. The analysis revealed five classes of habitat suitability; of the total land area studied, 1326.7 km2 (60%) was deemed suitable for the Mountain Nyala while 881.3 km2 (40%) was unsuitable. Specifically, 327.4 km2 (15%) was classified as highly suitable, 240.7 km2 (11%) as moderately suitable, 758.6 km2 (34%) as marginally suitable, 352.4 km2 (16%) as currently not suitable, and 528.9 km2 (24%) as permanently not suitable. The majority of suitable habitats are concentrated in the northern part of the park, along the western border, and in the Harrena forest area. This study provides vital insights into habitat suitability that are crucial for the conservation of the Mountain Nyala and the overall management of the park.

Assessment of urban sprawl using proximity factors in Lucknow City, India

Assessment of urban sprawl using proximity factors in Lucknow City, India

XAI in geographic analysis of innovation: Evaluating proximity factors in the innovation networks of Chinese technology companies through web-based data

This research explores the nonlinear interactions among multidimensional proximities, including geographical, cognitive, organizational, institutional, social, and technological aspects, and their impact on innovation within networks of over three million technology firms in China. Utilizing an innovative combination of web-based hyperlink and textual data analysis, supplemented by patent information, we delve into how these proximity dimensions influence corporate innovation capabilities. Our methodology integrates text-based deep learning techniques and employs the XGBoost model along with the SHapley Additive exPlanations (SHAP) algorithm and partial dependence plots to uncover the nuanced effects of proximity on innovation. The findings reveal that while geographical distance often correlates with larger cognitive and organizational proximities, underdeveloped regions exhibit stronger technological, institutional, and social proximities compared to their developed counterparts. The study further identifies social structure and technological differences as pivotal factors impacting collaborative innovation, with both positive and negative effects fluctuating alongside changes in proximity dimensions. Notably, we uncover that geographical proximity has a pronounced boundary effect on innovation, highlighting the critical role of spatial considerations in the digital age of innovation networks. This research contributes to the understanding of urban innovation dynamics and offers valuable insights for policymakers and urban planners aiming to foster innovation ecosystems.

Land use/land cover (LULC) changes modeling and susceptibility mapping using the binary logistic regression at the territorial level in eastern DR Congo

The Land Use and Land Cover (LULC) changes are commonly used to determine the landscape conditions and have significant impacts on the earth's surface processes. During the last three decades, there has been an acceleration of LULC changes in Eastern DR Congo. However, there is no comprehensive overview of the drivers of these changes at the territorial level in this region, even though the knowledge of these drivers is important for land use planning and spatial modeling of environmental changes. Using the Kalehe Territory as a case study, this work sought to fill this gap by analyzing the drivers of LULC changes during the 1987-2020 period. A mixed approach combining remote sensing, Geographic Information System, and logistic regression modeling was used. The results indicated that the prominent LULC changes in the study area are deforestation, built-up area expansion, cropland expansion, and shrubland expansion. These changes are significantly influenced by biophysical factors (slope, altitude, and soil type), conservation zoning, population dynamics, and accessibility factors at different levels. The occurrence of conservation zones decreases the susceptibility to deforestation, built-up land, and cropland expansions. In contrast, the proximity factors (distance to road, artisanal mining, and locality) increase the susceptibility to LULC changes. These factors can be integrated into spatial models to forecast LULC changes susceptibility in this region. Furthermore, the establishment of future land use management policy at the territorial level in eastern DR Congo should be space-specific as the susceptibility of LULC changes shows a spatial trend.

Population Pressure and Changing Land Use Land Cover in Morigaon District, Assam, India - A Geospatial Analysis

Objective: This study intends to prioritize the underlying mechanisms underpinning the spatial-temporal variations in Land Use and Land Cover (LULC) change in the Morigaon district, located in the middle part of Brahmaputra valley of Assam, India covering an area of 1498 km2.At the same time, this study focuses on the impact of population pressure on changing LULC in the study area. Methods: This research adopted geospatial approach for analysing land use land cover change in the study area. Satellite images from the years 1992 to 2022 have been utilized to identify the land transformation. The analysis of the land transformation has taken into account the five main LULC classifications of agriculture, forest, water bodies, built-up lands, and sand deposits. It has been discovered that the LULC classes identified in this study, which comprise both natural and anthropogenic elements, have a very strong link with the population growth in the Morigaon district. To show the relationship between population expansion and LULC indicators Pearson correlation method is used. Findings: Water bodies, forests and agriculture have significantly decreased by 0.13%, 4.66% and 1.64% respectively due to substantial anthropogenic activity during the past 30 years. In contrast, sand deposits and built-up lands increased by 3.13% and 3.30%. To show the relationship between population expansion and LULC indicators Pearson correlation method is used. Very significant relationships have been found between population and built-up lands with rho value 0.95. The relationship between population with agriculture, forest, water bodies and sand deposits have been seen with rho value -0.51, -0.89, -0.64 and 0.97 respectively. The loss of the geographic extent of natural classes like water bodies, forests, signifies that the geometrical progression in the human population has greatly amplified anthropogenic activities. Novelty: The novelty lies in adopting geospatial techniques that would certainly enhance land resource conservation. Environmental concerns have been increasing due to significant land surface change, which is predominantly the result of human-induced activities. In the studied area, socioeconomic, demographic, and proximity factors have a significant impact on the LULC change processes. The findings can be utilized to forecast potential LULC scenarios and offer direction for land management strategy. Keywords: LULC, Satellite Images, Anthropogenic, Pearson Correlation, Proximity Factor

Urban Sprawl in the context of proximity factors using Shannon’s Entropy Index and fractal dimensions: a case of Lucknow

ABSTRACT This study employs Geomatics techniques, including Remote Sensing (RS) for satellite dataset acquisition and Geographic Information System (GIS) for analysing Land-use Land-cover (LULC) patterns, to assess the nature of urban sprawl. Central to this analysis are the proximity to the Central Business District (CBD) and major roads. The research utilises low- and high-density measures to distinguish the varying characteristics of urban sprawl, allowing for the prioritisation of sustainable objectives across different city zones. The study applies Shannon’s Entropy Index (SEI) and Fractal Dimensions (FD) analysis to the years 1991 and 2021, focusing on CBD and major roads proximity.

Structural changes in the location and sectoral specialization of livestock breeding in the pre-war and during the war period

The article reveals structural shifts in the territorial organization and sectoral structure of animal husbandry in pre-war and during the war. Their trends were determined, an assessment was made, and certain regularities over the past 30 years were summarized. It was established that previously, animal husbandry was represented in almost all agricultural enterprises in the form of the organization and operation of farms of collective farms and state farms and had a relatively explicit zonal specialization by the objectively existing agricultural zones based on agro-climatic and production and economic indicators - Forest or Polissia, Forest-Steppe and Steppe; zones of the altitudinal belt in the mountains; suburban economy near large cities and urban agglomerations. However, in the 90s of the last centuries, the country started the curtailment of animal husbandry, the trends of which have not been overcome and persisted during the last 30 years and were significantly deepened by the war. It was determined that their causes were formed under the influence of inconsistent reforms and imperfect institutions for regulating the development of the industry; lack of investments and slow modernization and introduction of innovations; lack of entrepreneurial initiative among the vast majority of product manufacturers; deformation of the structure of agricultural land in the form of a significant reduction in the area of natural hayfields and pastures, a reduction in fodder production; the decline of selection and tribal food; a decrease in productivity and competitiveness compared to the indicators of the leading countries of the world and meat exporting countries. The only exception is the breeding of chickens and the production of products from them, which was revived on an innovative basis in the form of industrialized enterprises of extensive agricultural holdings with overconcentration of production and its main factors (land and capital) and acquired the status of a leading industry not only in the domestic, but also in the international product market poultry farming Cattle for meat, sheep breeding, horse breeding, and animal husbandry experienced the most significant reduction. On the contrary, beekeeping increased but suffered huge losses due to the war. As for the placement of industries, it had an explicit zonal conditionality and territorial organization with an orientation to the factors of raw materials for fodder, weather and climatic conditions for maintenance, factors of proximity to the consumer, and logistical support for the sale of products. However, with the modernization of enterprises and the concentration of production in large volumes, the improvement of communication routes and the increase in the speed of vehicles, the improvement of refrigeration equipment, the change in the location of crop production due to warming and the decrease in the amount of precipitation, the zonal specialization of livestock production is gradually losing its clarity, but this practically does not affect the efficiency of production. Accommodation remains a significant factor in the efficiency of poultry farming due to the focus on grain farming of the meat direction of cattle, sheep breeding, and horse breeding due to the focus on natural pastures. However, with the war, many animals and poultry, premises and equipment, property, and finished products were lost, and the industry suffered colossal losses. Among the factors of placement of livestock enterprises, safety was the first. Therefore, shifts in the location and branch structure of the industry have changed zonal priorities to regional ones and directions from north-south vectors to east-west. The priority is to prevent the destruction of livestock enterprises and their functioning in the relatively safe central and western regions. Keywords: structure, livestock, enterprises, households, agricultural zones, fodder production, purchase prices, innovations, territorial and industry specialization, risks, security, priorities, and prospects.

Enhanced Routing Protocol for Health Monitoring in Urban Traffic Networks via VANET: Analyzing IW-GeoR Against WGeoR

The primary aim of this initiative is to establish an effective routing system for managing both human-induced and natural calamities using a Wireless Body Sensor Network (WBSN) integrated with VANET within urban traffic networks. This is accomplished through a comparison between the Weighed Geographic Routing (WGeoR) protocol and the Improved Weighed Geographical Routing (IWGeoR) protocol, targeting minimized packet loss and reduced delay. The IWGeoR protocol stands out by efficiently selecting cluster heads to mitigate delay and packet loss, considering various factors such as differences in vehicle speeds, inter-vehicle distances, and traffic mobility. The protocol’s effectiveness relies on innovative node degrees, channel quality, proximity factors, and communication connection expiration times. For this study, twenty samples (n=20) were gathered from each of the two groups using diverse vehicles. Clinical.com established a pre-test power of 80% (G-power) for each group, with alpha and beta coefficients set at 0.05 and 0.2 respectively, in order to assess the protocol’s performance in terms of packet loss and average latency. The simulation outcomes indicate that the IWGeoR protocol surpasses the WGeoR protocol in crucial metrics such as packet loss and mean delay. Specifically, the IWGeoR protocol reduces packet loss by 10.77% and delay by 31.03%. The Independent Sample T-test calculates a significant p-value of 0.001 for the IWGeoR and WGeoR protocols, demonstrating statistical significance (p<0.005). Based on experimental findings and the Independent Sample T-test, the proposed IWGeoR protocol has demonstrated superior performance compared to the WGeoR protocol.

Comparative Study for Understanding the Spatial Growth Pattern of Pune and Jaipur City from 1990 to 2020

Understanding the urban form, conducting spatial change analysis of an urban area using time-series data, and identifying urban growth drivers play a crucial part in framing policies for sustainable planning practices. In this research, an inverse S-curve function is employed to examine Urban Land Densities (ULD) derived from concentric divisions of urban regions in Pune and Jaipur. The inverse S-curve quantitatively describes variations in Urban Land Density (ULD) from the urban center to the outskirts. Consequently, the parameters identified during the curve-fitting process offer information about the urban form of the cities, shedding light on their rate of expansion, level of compactness, and the nature of sprawl. Built-up area is determined from the Landsat datasets for the years 1991, 1996, 2001, 2006, 2011,2016, and 2021. The analysis confirmed that Pune revealed an increase in sprawling, expansive, and low-density development. As a city that has grown linearly, Jaipur has experienced more constrained growth than Pune. Additionally, the fitted ULD equation provided an accurately fitted radius for Jaipur, but not for Pune, highlighting the equation's shortcomings. The direction analysis and understanding of the change in the slopes of the S curve further led to identifying growth drivers, broadly classified into proximity, government intervention, socioeconomic, and physical factors. The study can help achieve future research objectives in simulating and modeling urban growth and creating policies to deal with related problems.

Application of Remote Sensing and Geographic Information System in Identification of Urban Growth nodes: A Case of Surat City, India

With the passage of time, the city's growth behavior will not change unless and until the government intervenes, and thus its identity will shift from monocentric to polycentric to meet the needs of citizens. As a result, this study is being conducted to identify emerging growth nodes within a selected area of Surat City, as well as their growth drivers over a 30-year period. Quantified built-up area within a patch size of 1km x 1km was used to compute patch density at five-year intervals from 1991 to 2021. In addition, the spatial changes that occurred within patches over the same time period were examined. Both analyses aid in determining the emerging growth nodes over a 30-year period. From 1991 to 2021, the city was driven by socioeconomic criteria such as land price, availability of good health and educational facilities, water and sewerage networks, fire stations, proximity factors such as proximity to major roads, bridges, bus stations, metro, railway stations, airport, environmental factors such as the development of riverfront and linear park, bio-diversity park, and government interventions in terms of Town Planning Schemes. This study thus aids urban planners and decision-makers in selecting which growth nodes to plan for new development and type of development, what to connect, and what to protect in the years to come.

Examining proximity factors in public-private collaboration models for sustainable agri-food system transformation: a comparative study of two rural communities

Collaboration among local actors is considered an important precondition for a sustainable transformation of the regional agri-food system. To date, however, little is known about the social innovation capacity of local farmers when it comes to the establishment of alternative food networks. This is especially true for farmers’ collaborations with local government officials, which are becoming increasingly important in establishing sustainability-oriented markets for local products and services, especially in the agricultural sector that is often rooted in rigid logics, supply chains and institutions. Therefore, this paper aims to explore the concept of proximity as an analytical lens to understand private-public collaboration models that aim at facilitating sustainable transformation in rural areas. Drawing on concepts from innovation geography, this paper considers the influence of geographic, cognitive, institutional, organizational and social determinants of collaboration, enabling an evaluation of the social innovation capacity of local private and public actors. This theoretical approach helps to disassemble and differentiate social innovation processes to determine success strategies. The paper studies two rural communities where mayors aim to establish local food stores with regional products and, therefore, seek collaboration with local farmers. While the empirical aspect of the study is limited, the two cases provide an opportunity to test the theoretical framework. The proximity approach can be significant for regional agri-food system transformation and steering social innovation processes by considering the distinct capacities of actors. Our conclusion is that the examination of the absence and degree of proximity facilitates a better understanding of practical recommendations to promote agri-food system transformation.

The Determinants of Trade in Services With the Focus on Tourism

This paper estimates the gravity of bilateral trade in services at an aggregate level. The aim of the paper is to assess the effects of social and economic factors on the export of services. A large part of the trade-in services is directly or indirectly related to the tourism industry; therefore, the results of this research are mainly discussed from the perspective of the tourism industry. To achieve the paper's aim, we estimate the augmented gravity model on a sample of 5,359 trading partners from 2000 to 2018. We evaluate our model using the Poisson Pseudo Maximum Likelihood estimator. We include various fixed-effect dummies in our model to address the endogeneity issues. Our results show that standard gravity variables such as economic size and distance are significant for service trade. From the tourism industry perspective, it can be concluded that trade in tourism services is determined by the similarity in economic size, proximity, and common socio-economic factors such as common first language and shared religious beliefs. In contrast, common colonial history has no significant effects on service trade.

Shaping online and offline informal learning networks in off-site construction projects: a proximity perspective

PurposeInformal learning networks are critical to response to calls for practitioners to reskill and upskill in off-site construction projects. With the transition to the coronavirus disease 2019 (COVID-19) pandemic, social media-enabled online knowledge communities play an increasingly important role in acquiring and disseminating off-site construction knowledge. Proximity has been identified as a key factor in facilitating interactive learning, yet which type of proximity is effective in promoting online and offline knowledge exchange remains unclear. This study takes a relational view to explore the proximity-related antecedents of online and offline learning networks in off-site construction projects, while also examining the subtle differences in the networks' structural patterns.Design/methodology/approachFive types of proximity (physical, organizational, social, cognitive and personal) between projects members are conceptualized in the theoretical model. Drawing on social foci theory and homophily theory, the research hypotheses are proposed. To test these hypotheses, empirical case studies were conducted on two off-site construction projects during the COVID-19 pandemic. Valid relational data provided by 99 and 145 project members were collected using semi-structured interviews and sociometric questionnaires. Subsequently, multivariate exponential random graph models were developed.FindingsThe results show a discrepancy arise in the structural patterns between online and offline learning networks. Offline learning is found to be more strongly influenced by proximity factors than online learning. Specifically, physical, organizational and social proximity are found to be significant predictors of offline knowledge exchange. Cognitive proximity has a negative relationship with offline knowledge exchange but is positively related to online knowledge exchange. Regarding personal proximity, the study found that the homophily effect of hierarchical status merely emerges in offline learning networks. Online knowledge communities amplify the receiver effect of tenure. Furthermore, there appears to be a complementary relationship between online and offline learning networks.Originality/valueProximity offers a novel relational perspective for understanding the formation of knowledge exchange connections. This study enriches the literature on informal learning within project teams by revealing how different types of proximity shape learning networks across different channels in off-site construction projects.

DESIGN AND BUILD AN OUTDOOR EQUIPMENT RENTAL INFORMATION SYSTEM BY APPLYING THE FIRST IN FIRST OUT ALGORITHM

The rental system currently implemented at Mandala Outdoor Equipement requires customers who want to rent outdoor equipment to come directly or via telephone/WA communication to Mandala Outdoor Equipement. Rental of outdoor equipment sometimes causes problems, such as customers who are disappointed because they were previously confirmed to be able to rent outdoor equipment, after receiving information that they could not or that it was canceled by the provider because it turned out the stock had run out, even though this happens. After all, sometimes they prioritize renters with proximity factors because of the courage of new renters. with higher rental costs. Based on these existing problems, this research aims to build a website-based information system by applying the First in First Out (FIFO) algorithm for outdoor equipment rental at Mandala Outdoor Equipment. The method used in this research is the waterfall method. The result of this research is an outdoor equipment rental application by applying the FIFO algorithm to the web-based Mandala Outdoor Equipment so that rentals can be done via the website (anywhere and at any time) and rentals are served according to the rental time sequence (First in First Out). From the results of testing the application using a separate black box, seen from the functional aspect, both features and access rights, everything went well, successfully, and validly.

A study on the multidimensional driving mechanism of cross-regional scientific collaboration network in China

ABSTRACT Multidimensional proximity has been considered the important factor affecting regional scientific collaboration network (RSCN) and has received extensive attention from scholars. This paper constructs a cross-RSCN based on co-authorship across 31 regions in China, incorporates traditional multidimensional proximity and network embedding proximity into a unified framework and proposes a five-factor driving model for dissecting the mechanism of cross-regional scientific collaborative innovation (CI). The results show that the scientific collaboration network is gradually becoming saturated with connections, and the polarisation of cross-regional collaboration is increasingly serious. The collaboration intensity is affected by the co-movement of several proximity factors, among which geographic proximity (GP) is the main but not the sole one that positively affects the cross-regional scientific collaboration. Based on the decision tree model, we conclude that structure proximity may compensate for low GP and low economic proximity (EP) on cross-regional CI performance. The positive effect of network location proximity on regional scientific collaboration is more sensitive when the EP is not high. The findings provide significant guidance for scientific research institutions, universities and regional policy-makers to select appropriate partners as well as network embedding strategies, and further contribute to long-term improvement of scientific innovation performance and stable regional economic growth.

Study on the spatio-temporal evolution and influencing factors of farmland abandonment on a county scale.

The protection of farmland resources is related to world food security, as well as the smooth realization of the goals of UN 2030 Agenda for Sustainable Development and China's Rural Revitalization Plan. As the most active region in the global economic development and one of the main grain producing areas, the problem of farmland abandonment in the Yangtze River Delta gradually appears with the rapid development of urbanization. Therefore, based on the interpretation data of remote sensing images and field survey data of three periods in 2000, 2010, and 2018, this study used Moran's I and geographical barycenter model to reveal the spatiotemporal evolution law of farmland abandonment in Pingyang County of the Yangtze River Delta. And then, this study selected 10 indicators covering four categories: geography, proximity, distance, and policy, and used random forest model to reveal the main influencing factors of farmland abandonment in the study area. The results showed that the abandoned farmland increased from 441.58 hm2 in 2000 to 5797.40 hm2 in 2018. The hot spot and barycenter of land abandonment gradually shifted from the western mountainous areas to the eastern plain areas. Altitude and slope were the main factors affecting the farmland abandonment. The high the altitude and the great the slope, the farmland abandonment in mountainous areas were serious. The proximity factors had a greater impact on the expansion of farmland abandonment from 2000 to 2010, and then the impact weakened. In view of the above analysis, the countermeasures and suggestions for maintaining food security were finally put forward.

Application of urban growth boundary delineation based on a neural network approach and landscape metrics for Khulna City, Bangladesh

The rapid and unprecedented urban growth in Khulna, Bangladesh is making it difficult to implement measures to limit further expansion and define clear administrative boundaries, which is posing a significant threat to the environment and ecological sustainability. Using an Artificial Neural Network (ANN) based urban growth simulation model and landscape metrics, this study aims to evaluate the spatial extent and direction of urban growth and demarcate an Urban Growth Boundary (UGB) by examining the future contiguous expansion of the city for implementing effective land use provision. Utilizing data on biophysical, proximity, neighborhood, and market factors over the past twenty years, the neural network with Markov chain model allocates the land demand for buildup area by 2020 and 2030, concerning twelve explanatory variables. The simulated map of the urban area is further used by landscape metrics to quantify local-level urban patch information viz. landscape pattern, size, aggregation, etc. The compact patch characteristics are mostly found under the Kotwali thana, while, fragmented and unstructured patches are prevailing between urban–rural interfaces. Finally, there has around 95 km2 gap between the existing service provided by KCC and the future demand of Khulna city, creating an imbalance between the supply and demand of urban services. Hence, restricted urban growth would make government investment in service facilities cost-effective and enable planners and decision-makers to intend a feasible trade-off between future land demand and the protection of natural resources.

A machine learning-based spatial statistical method for modelling different phases of gully development in South-Eastern Nigeria

Gully erosion is an environmental problem that causes topsoil loss and ecological damage. Understanding its formative processes and predisposing factors is crucial for effective management. However, few studies have focused on the spatial modelling of these factors. In the current study, machine learning (ML)-based spatial statistics were used to identify the predisposing factors of gully formation processes in south-eastern Nigeria. A geospatial database was created using 262 gully locations, fragmented into four categories as dependent variables. A total of 20 predictor variables, including geomorphometry, terrain, geo-environmental factors, proximity, and climate, were fused into a multinomial logistic regression model. The model was then fitted using the nnet package in R after testing for multi-collinearity and spatial autocorrelation. The results revealed that the study area, which is a 7th-order drainage basin, is vulnerable to gullying, as expressed by Horton's laws of geomorphometry. The highest simulated probability range was observed for moderate gully (0.5512–0.7175), with terrain and climate as predictive factors, whereas the lowest simulated probability range was observed for full gully (0.1136–0.4107) as influenced by basin geomorphometry. Soil creep (0.3869–0.5317) and low gully (0.1025–0.5107), both driven by climate, proximity, and terrain factors, exhibited a moderate tendency for occurrence. The findings reported in this paper can aid in decision-making for sustainable environmental management of gully cycles and provide a novel approach for global gully-active drainage basins.

History, neighborhood, and proximity as factors of land-use change: A dynamic spatial regression model

Spatio-temporal land-use change (LUC) modeling provides vital information about land development dynamics. However, accounting for such dynamics faces methodological challenges. This research introduces a Dynamic Spatial Panel Data (DSPD) modeling framework for LUC, incorporating spatial and temporal dependencies. A continuous response variable is introduced to take advantage of traditional spatial regression models. The DSPD model is applied to balanced spatial panel data at the block-group level covering Florida between 2010 and 2019 and incorporating both new and previously used proxy variables. The urban growth impacts of site-specific, proximity, neighborhood, socio-economic, and transportation factors are investigated. This study contributes to the literature by providing extensive insights into spatial autocorrelation, spillover, heterogeneity, and temporal lag effects in urban growth. Also, the study reveals the importance of mobility and mortgage financing in land development. The proposed modeling framework achieves high accuracy. The dynamic structure of this model provides an opportunity to predict future urban growth without the need for a land development scenario. Such predictions provide insights about future land development to practitioners and policymakers.