Local Spatial Model Research Articles

Understanding the county-level relationship between population change and ecological environment quality dynamic in China, 2000–2020

Global population growth has increasingly intensified human interferences with natural ecosystems. These interferences often exhibit complexity and diversity characteristics. Understanding the relationship between population growth and environmental issues has become a focus in the current scientific community. This study, leveraging multi-source remote sensing and census data from China for the years 2000, 2010, and 2020, utilized multiple spatial econometric models to investigate the pressure of population dynamics on Ecological Environment Quality (EEQ). A bivariate local spatial autocorrelation model was applied to pinpoint regions that experienced significant population pressure in China over the past two decades. Results showed that there was a rise in population density, with larger cities attracting more migrants to become residents and EEQ declining in most counties. The analysis revealed a strong spatial dependence between changes in EEQ and population. Spatial regression models were more effective than non-spatial models in explaining population pressure on EEQ. Population growth was significantly associated with EEQ decline. Based on the spatial regression model, EEQ decreased by 0.029% for every 1% increase in population from 2000 to 2010. During 2010–2020, this impact was more pronounced, with a 1% population associated with a 0.069% decline in EEQ. Pressure zones analysis showed the most widespread regions were unsustainable zones (decreases in population and EEQ), followed by areas with both population increases and EEQ declines, with fewer regions demonstrating coordinated development improvements. Effective solutions to address the challenges of population pressure on EEQ involve government regulation, strategic planning, cross-regional management, and the sharing of responsibilities and benefits.

On the calibration of multiscale geographically and temporally weighted regression models

Spatiotemporal analysis and modeling have long been key foci of geographical information science. In a recent paper, Wu et al. expanded the local spatial modeling technique of multiscale geographically weighted regression (MGWR) to incorporate a temporal component. This new model is named multiscale geographically and temporally weighted regression (MGTWR). Despite the utility of expanding MGWR to incorporate a temporal weighting function in addition to the existing spatial one, the approach developed by Wu et al. has two limitations: the bandwidth selection algorithm cannot guarantee an optimal result and no formulation for the effective number of parameters (ENPs) in the model is given. To address these issues, this paper describes a procedure to derive an optimal temporal and an optimal spatial bandwidth for MGTWR and also develops a formula for the ENPs in the model. The former ensures the reliability of the model outputs while the latter is essential for making reliable inferences from the local parameter estimates generated in the calibration of models by MGTWR. These advances in the MGTWR framework are demonstrated through applications to simulated and real-world data.

Contribution of soundscape appropriateness to soundscape quality assessment in space: A mediating variable affecting acoustic comfort

Soundscape appropriateness (SA) provides supplemental information on the matching degree between auditory information and the surrounding scene in soundscape perception. This indicator has been integrated into the standard ISO process for collecting soundscape data, forming a component of the sound quality assessment questionnaire. However, its role in soundscape quality assessment has not been fully understood. Herein, we present the findings from soundscape data collected from Beiling Park in Shenyang, China. A method was developed that integrates mediation effect models with multiscale geographically weighted regression models to explore the mediating role of SA in the impact of sound source types on soundscape quality, as well as the spatial heterogeneity of this mediation effect. The results confirm that SA does mediates the influence of sound source types on acoustics comfort (AC). Specifically, natural sounds (indirect effect/total effect = .19/.19), traffic sounds (indirect effect/total effect = −.46/−.65), and commercial sounds (indirect effect/total effect = −.25/−.12) impact the perception of AC by either enhancing or reducing SA. Moreover, the relationships among variables depicted in this model demonstrate spatial heterogeneity, demonstrating that in urban open spaces with complex constructures, local spatial models may be needed for soundscape assessment. The research reaffirms the significance of SA in urban open spaces. In terms of practical implications for urban and landscape planners, when sound sources cannot be controlled or altered, coordinating between the sound and the surrounding environment through landscape optimisation could also improve the quality of the soundscape through enhancing SA and help achieve the goal of creating healthy urban open spaces.

Local and global self-attention enhanced graph convolutional network for skeleton-based action recognition

The current successful paradigm for skeleton-based action recognition is the combination of Graph Convolutional Networks (GCNs) modeling spatial correlations, and Temporal Convolution Networks (TCNs), extracting motion features. Such GCN-TCN-based approaches usually rely on local graph convolution operations, which limits their ability to capture complicated correlations among distant joints, as well as represent long-range dependencies. Although the self-attention originated from Transformers shows great potential in correlation modeling of global joints, the Transformer-based methods are usually computationally expensive and ignore the physical connectivity structure of the human skeleton. To address these issues, we propose a novel Local-Global Self-Attention Enhanced Graph Convolutional Network (LG-SGNet) to simultaneously learn both local and global representations in the spatial–temporal dimension. Our approach consists of three components: The Local-Global Graph Convolutional Network (LG-GCN) module extracts local and global spatial feature representations by parallel channel-specific global and local spatial modeling. The Local-Global Temporal Convolutional Network (LG-TCN) module performs a joint-wise global temporal modeling using multi-head self-attention in parallel with local temporal modeling. This constitutes a new multi-branch temporal convolution structure that effectively captures both long-range dependencies and subtle temporal structures. Finally, the Dynamic Frame Weighting Module (DFWM) adjusts the weights of skeleton action sequence frames, allowing the model to adaptively focus on the features of representative frames for more efficient action recognition. Extensive experiments demonstrate that our LG-SGNet performs very competitively compared to the state-of-the-art methods. Our project website is available at https://github.com/DingYyue/LG-SGNet.

Spatial pattern of congenital toxoplasmosis incidence and its relationship with vulnerability and national health indicators in Brazil

There is a gap in evidence regarding spatial clusters of the congenital toxoplasmosis (CT) and its association with social and health indicators in the Brazilian territory. Thus, we aimed herein to identify CT risk areas in Brazil and its association with social vulnerability and health indicators. An ecological and population-based study was conducted. The CT incidence coefficient was calculated and smoothed using the Local Empirical Bayesian method. Global regression models and local spatial regression model were applied. High-incidence clusters of the disease were identified throughout the country. Additionally, a positive association was observed between the incidence of congenital toxoplasmosis and the Social Vulnerability Index, coverage of community health agents, and the percentage of prenatal consultations. This association was stronger the further south in the country. Herewith, the implementation and strengthening of public strategies, with focus on priority intersectoral actions for prevention, early diagnosis, and prompt treatment, is urgently required for the effective control of CT in Brazilian municipalities.

Exploring the influencing factors of scrub typhus in Gannan region, China, based on spatial regression modelling and geographical detector

Scrub typhus is a significant public health issue with a wide distribution and is influenced by various determinants. However, in order to effectively eradicate scrub typhus, it is crucial to identify the specific factors that contribute to its incidence at a detailed level. Therefore, the objective of our study is to identify these influencing factors, examine the spatial variations in incidence, and analyze the interplay of two factors on scrub typhus incidence, so as to provide valuable experience for the prevention and treatment of scrub typhus in Gannan and to alleviate the economic burden of the local population.This study employed spatial autocorrelation analyses to examine the dependent variable and ordinary least squares model residuals. Additionally, spatial regression modelling and geographical detector were used to analyze the factors influencing the annual mean 14-year incidence of scrub typhus in the streets/townships of Gannan region from 2008 to 2021. The results of spatial1 autocorrelation analyses indicated the presence of spatial correlation. Among the global spatial regression models, the spatial lag model was found to be the best fitting model (log likelihood ratio = −319.3029, AIC = 666.6059). The results from the SLM analysis indicated that DEM, mean temperature, and mean wind speed were the primary factors influencing the occurrence of scrub typhus. For the local spatial regression models, the multiscale geographically weighted regression was determined to be the best fitting model (adjusted R2 = 0.443, AICc = 726.489). Further analysis using the MGWR model revealed that DEM had a greater impact in Xinfeng and Longnan, while the southern region was found to be more susceptible to scrub typhus due to mean wind speed. The geographical detector results revealed that the incidence of scrub typhus was primarily influenced by annual average normalized difference vegetation index. Additionally, the interaction between GDP and the percentage of grassland area had a significant impact on the incidence of scrub typhus (q = 0.357). This study illustrated the individual and interactive effects of natural environmental factors and socio-economic factors on the incidence of scrub typhus; and elucidated the specific factors affecting the incidence of scrub typhus in various streets/townships. The findings of this study can be used to develop effective interventions for the prevention and control of scrub typhus.

A nonparametric approach for detecting urban polycentric spatial structure in China using remote sensing nighttime light and point of interest data

ABSTRACT Effectively identifying urban polycentric spatial structure (UPSS) is essential for data-driven evaluation of urban performance, and it serves as a scientific basis for urban spatial planning. However, existing identification methods have limitations such as subjectivity, poor spatial continuity, and a narrow application scale. Thus, from a morphology perspective, our study proposed a rapid, highly applicable, and spatiotemporally comparable nonparametric approach for detecting morphological urban polycentric spatial structure (MUPS) by integrating remote sensing nighttime light (NTL) and point of interest (POI) data. Taking China as an object, on the basis of recognizing urban entities using NTL data, a wavelet transform was initially introduced to fuse multi-source geospatial data, thereby enhancing the spatial intricacy within a city. Then, a local spatial autocorrelation model was utilized to identify pixel clusters. Finally, post-processing was performed to optimize urban centers, subsequently calculating MUPS. Results reveal that urban entities identified based on NTL have a significant advantage in characterizing the concentration of human activities, which can ensure the accuracy of extracting MUPS. Compared with existing relevant methods, the proposed nonparametric approach avoids the misalignment of multi-temporal urban center distribution, enhancing accuracy and stability. Differentiated spatiotemporal patterns were found in the evolutionary trajectories of MUPS in China, with a progressive intensification in the degree of spatial decentralization. Our study provides valuable insights into spatiotemporal analyses of MUPS at multiple scales and serves as a foundational resource for urban spatial planning.

Clustering data analytics of urban land use for change detection

In this study, the author proposes and details a workflow for the spatial-temporal demarcation of urban areal features in 8 cities of Tamilnadu, India. During the inception phase, functional requirements and non-functional parameters are analyzed and designed, within a suitable pixel area and object-oriented derived paradigm. Land use categories are defined from OpenStreetMap (OSM) related works with the scope of conducting climate change, using multispectral sensors onboard Landsat series. Furthermore, we augment the bands dataset with Spatially Invariant Feature Transform (SIFT), Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), Leaf Area Index (LAI), and Texture base indices, as a means of spatially integrating auto-covariance to stationarity patterns. In doing so, change detection can be pursuit by scaling up the segmentation of regional/zonal boundaries in a multi-dimensional environment, with the aid of Wide Area Networks (WAN) cluster computers such as the BEOWULF/Google Earth Engine clusters. GeoAnalytical measures are analyzed in the design of local and zonal spatial models (GRID, RASTER, DEM, IMAGE COLLECTION). Finally, multi variate geostatistical works are ensued for precision and recall in predictive data analytics. The author proposes reusing machine learning tools (filtering by attribute-based indexing in PaaS clouds) for pattern recognition and visualization of features and feature collection.

Spatial analysis of geographical disparities in pedestrian safety

Investigating pedestrian safety disparities across sociodemographic groups is essential for enhancing traffic safety. This study examines the impact of sociodemographic and built environment characteristics on pedestrian crashes. It introduces a comprehensive macro spatial analysis framework that includes a global regression model, spatial autoregressive models, and a local spatial regression model. Three measures of pedestrian injury are analyzed. The findings reveal that a higher percentage of the high-income population significantly correlates with lower rates of pedestrian injuries across all three measures. Conversely, a higher percentage of the low-income population shows a significant positive correlation with the proportion of crashes involving the Black population, and with the proportion of severe pedestrian crashes involving the Black population. Pedestrian-oriented network density is negatively associated with fatal or severely injurious crashes involving the Black population. These results emphasize the need to account for spatial variations and equity when addressing pedestrian safety disparities.

Clustering data analytics of urban land use for change detection

In this study, the author proposes and details a workflow for the spatial-temporal demarcation of urban areal features in 8 cities of Tamilnadu, India. During the inception phase, functional requirements and non-functional parameters are analyzed and designed, within a suitable pixel area and object-oriented derived paradigm. Land use categories are defined from OpenStreetMap (OSM) related works with the scope of conducting climate change, using multispectral sensors onboard Landsat series. Furthermore, we augment the bands dataset with Spatially Invariant Feature Transform (SIFT), Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), Leaf Area Index (LAI), and Texture base indices, as a means of spatially integrating auto-covariance to stationarity patterns. In doing so, change detection can be pursuit by scaling up the segmentation of regional/zonal boundaries in a multi-dimensional environment, with the aid of Wide Area Networks (WAN) cluster computers such as the BEOWULF/Google Earth Engine clusters. GeoAnalytical measures are analyzed in the design of local and zonal spatial models (GRID, RASTER, DEM, IMAGE COLLECTION). Finally, multi variate geostatistical works are ensued for precision and recall in predictive data analytics. The author proposes reusing machine learning tools (filtering by attribute-based indexing in PaaS clouds) for pattern recognition and visualization of features and feature collection.

Unveiling the Impact of Urbanization on Net Primary Productivity: Insights from the Yangtze River Delta Urban Agglomeration

Urbanization has significantly altered the carbon cycle of the terrestrial environment, particularly in relation to net primary productivity (NPP). Gaining a more comprehensive comprehension of how NPP is affected by urbanization is crucial for obtaining fresh perspectives on sustainable urban landscape design and decision making. While there is a significant body of research examining the geographical and temporal patterns of NPP supply capacity, there are only a few studies that have investigated the spatial relationships between NPP and urbanization, particularly at the grid scale. This research investigated the temporal and geographical features and patterns of NPP and their impact mechanisms. In order to estimate NPP and the level of urbanization in the Yangtze River Delta Urban Agglomeration (YRDUA), we used a combination of different models and datasets. To evaluate the geographical correlations and dependence between NPP and urbanization, we utilized local bivariate autocorrelation methods and spatial regression models to describe and visualize these relationships. The findings revealed that there was a consistent negative relationship between NPP and urbanization on a global scale from 1990 to 2020. However, when examining the local scale, the geographical correlations could be classified into four distinct categories: areas with both low NPP and low urbanization, areas with high NPP and high urbanization, areas with low NPP and high urbanization, and areas with high NPP and low urbanization. Our analysis showed that spatial regression models are more suitable for quantifying the spatial relationship between NPP and urbanization due to their ability to include the impacts of spatial Moran’s I techniques. Due to the growing urbanization, the highest NPP value was recorded in 2005, followed by 2000, 2020, and 2010. Conversely, the smallest association was observed in 2015. Examining the geographical connection between NPP and urbanization offers theoretical and practical insights for urban planning that prioritizes human needs and promotes sustainable development. It also aids in the development of reasonable methods for organizing ecological functional systems.

Modeling the Creative Milieu of Cosmopolitan Cities: Shanghai, 1912–1948

This research paper gleans insights from significant historical cases to unravel the essence of a ‘creative milieu.’ Our study scrutinizes the factors that shaped artists’ settlement patterns in Shanghai during the Republican era (1912–1948) by constructing both global and local spatial regression models across three pivotal years, thus unveiling seven distinct variable categories. We argue that paramount among these attributes are individual art intermediaries, art schools, and art organizations. Furthermore, our inquiry acknowledges the diverse and idiosyncratic characteristics inherent to artistic communities and delineates between attributes of particular relevance to the Chinese context and those adaptable within a Western paradigm.

Exploring the Relationships between Land Surface Temperature and Its Influencing Determinants Using Local Spatial Modeling

In recent years, a growing body of research has investigated the factors influencing land surface temperature (LST) in different cities, employing diverse methodologies. Our study aims to be one of the few to examine the socio-environmental variables (SV) of LST with a holistic approach, especially in primate cities in developing countries, which are particularly vulnerable to the impacts of climate change. In this context, the study preliminarily identifies the SV of LST while investigating the most vulnerable areas related to extreme LST at the neighborhood level. The combined 11 variables are analyzed using spatial modeling methods (GWR and MGWR). The MGWR model outperforms the GWR model with an adjusted R2 of 0.96. The results showed that: (1) the 65+ population is negatively associated with LST in 95% of neighborhoods; the socioeconomic index–LST relationship is negative in 65% of neighborhoods. (2) In 90% of the neighborhoods where the relationship between LST and the built environment ratio is positive, the socioeconomic level decreases while household size increases in 98% of the neighborhoods. (3) In 62% of the neighborhoods where the relationship between the 65+ population and LST is negative, the relationship between the socioeconomic level and LST is negative. This study aids decision-makers and planners in managing urban resources to reduce extreme LST exposure region by region and recommending multiscale policies to control determinant influences on LST.

Great transition and new pattern: Agriculture and rural area green development and its coordinated relationship with economic growth in China

Agricultural and Rural Green Development (ARGD) and economic growth are major challenges prevalent in China, but also in other developing countries. A notable gap in current literature lies in the limited holistic approach to agriculture and rural areas, with scant attention being paid to the spatiotemporal evolution of ARGD and its coordination relationship with economic growth. This paper first offers a theoretical analysis of the interactive relationship between ARGD and economic growth and subsequently examines the policy implementation process in China in this regard. It observes 31 provinces in China from 1997 to 2020 to uncover the spatiotemporal evolution of Agricultural and Rural Green Development Efficiency (ARGDE). Using the coupling coordination degree (CCD) model and the local spatial autocorrelation model, this paper analyses the coordination relationship and spatial correlation between ARGDE and economic growth. The results show that ARGDE in China exhibited a phased growth trend and was greatly affected by policies during 1997–2020. The interregional ARGD produced a hierarchical effect. However, provinces with a higher ARGDE did not necessarily exhibit faster growth, leading to a differential pattern of optimization that involved continuous optimization, phased optimization, and continuous deterioration. Over a long period, ARGDE exhibited a trend of significant upward leaps or jumps. Finally, the CCD between ARGDE and economic growth improved, with a clear trend of high-high agglomeration characteristics that shifted from the eastern and northeastern provinces to the central and western ones. This suggests that promoting “quality agriculture” and “green agriculture” can have practical significance in accelerating the development of ARGD. In the future, it is vital to promote ARGD’s transformation while mitigating the risk of degrading the coordinated relationship between ARGD and economic growth.

Measurement and spatio-temporal heterogeneity analysis of the coupling coordinated development among the digital economy, technological innovation and ecological environment

In-depth exploration of the coupled and coordinated relationship among regional digital economy (DE), technological innovation (TI) and ecological environment (EE) is a vivid embodiment of implementing sustainable development. In order to show the spatiotemporal features of the coupling coordination degree (CCD) from 2011 to 2020, this study builds an evaluation index system from three target levels, namely, the digital economy DE, science and technology innovation TE, and ecological environment EE. Based on this, global and local spatial econometric models, namely the global Moran's I index and the spatio-temporal geographically weighted regression (GTWR) model, are used to identify the spatio-temporal heterogeneity features of each explanatory variable on the CCD. The study results include: (1) The comprehensive evaluation index shows a rising trend, but the development is uneven among systems. (2) The CCD continues to rise steadily, and the regional disparity is widening; the transformation from the near-disorder level to the primary coordination level is realized over the research period. Spatially, the coupling coordination is higher in the eastern and southern regions, while the western and northern regions are relatively low. (3) The GTWR model demonstrates that human capital, urbanization rate, and openness to the outside world promote the CCD. In contrast, the social unemployment rate inhibits CCD, among which human capital is the main force behind coupled and coordinated development.

Investigating the Synergistic Evolution Mechanism of Multi-Scale Cities: A Case Study of Three Urban Agglomerations in Eastern China

Urban growth has evolved from cities to metropolitan areas and urban agglomerations, exhibiting a multi-scale pattern. Urban scaling law can reflect the nonlinear relationship between urban indicators and population size, which is very important for urban planning through analyzing the evolution of urban system characteristics. However, existing studies mainly focused on scalar law within countries, neglecting the multi-scale synergistic evolution of complex urban systems. The purpose of this study is to investigate the scalar relationship between urban indicators and population size at multiple scales from the perspective of individual cities, metropolitan areas, and urban agglomerations, using data from 45 cities in three urban agglomerations in eastern China. Based on the urban scaling law, local spatial autocorrelation model is used to analyze and explore the collaborative evolution of multiple scales. Results show that from the perspective of time evolution, the three urban agglomerations exhibit greater scaling effects than metropolitan areas, with a scaling exponent (β) greater than 1 for urban indicators including economy, land, infrastructure, ecological pressure, and innovation. From the perspective of spatial differences, the spatial development gap between the metropolitan area scale and the urban agglomeration scale is relatively small compared with the city scale. In addition, the Beijing–Tianjin–Hebei urban agglomeration (BTH) mainly displays the synergistic development of the dual-core structure of Beijing and Tianjin. The Yangtze River Delta (YRD) exhibits significant disparities between its cities, resulting in a low degree of overall synergy. In contrast, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) leads in terms of synergistic evolution. This study is crucial to help understand the development of urban systems at different scales and to support regional planning and the achievement of coordinated development.

Provincial town as a spatial model in the satirical and humorous literature

The article deals with the artistic functions of local spatial models in the Russian satirical and humorous prose of the 20th century. It gives an idea of the typology of comic evaluation objects (character, situation, event, space). Particular attention is paid to the city as a multidimensional cultural text, as a universal metaphor for the state world order. The work reveals a contradiction between the real scale of the county town and the ridiculously grandiose ambitions of its inhabitants. The article discusses the semantics of the province objective world in the satirical and humorous prose, the semantic content of distinguishing between one's own and another's. In this respect, the satirists and comedians development of the humorous specific technique is essential (the motives of rumor, the introduction of imaginary figures, playing with scales, elements of mystification).

Updating Local Anisotropies with Template Matching During Geostatistical Seismic Inversion

Iterative geostatistical seismic inversion methods are widely used to predict petro-elastic rock properties from seismic reflection data. When the model perturbation technique uses two-point geostatistics, these methods struggle to reproduce complex and nonstationary geological environments such as faults, folds and highly variable depositional systems. These limitations are often due to the use of a global variogram model to express the expected spatial continuity pattern of the property of interest. In complex geological environments a global variogram model might be unable to detect local heterogeneities and rapid variations of lithology, and result in nonrealistic geological models. Local heterogeneities might be predicted from the data using seismic attribute analysis, which can be imposed during geostatistical seismic inversion as local anisotropy models. In these approaches, the information about the local spatial continuity patterns is fixed and will guide and condition the entire inversion procedure, which can lead to errors and uncertainty in areas where this approach is not appropriate due to high local uncertainty of geological features, given the poor signal-to-noise ratio of the data or the presence of important geological features below the seismic resolution. This work proposes an iterative geostatistical seismic inversion method which iteratively updates the local spatial continuity models based on the trace-by-trace misfit between observed and predicted seismic data. The update of the local spatial continuity models aims at surpassing the limitations of the seismic inversion methods that use a fixed a priori variogram model. The method is successfully illustrated in a challenging two-dimensional synthetic data set and in a real case application. The results demonstrate the benefit of updating iteratively the imposed local spatial continuity patterns based on the data misfit. The inverted models are capable of better predicting the location of faults and reproducing the continuity of sinuous channels.

Analysis of the energy justice in natural gas distribution with Multiscale Geographically Weighted Regression (MGWR)

Energy justice is violated when particular customers and locations are excluded from a variety of urban energy service distribution. This study explores energy justice in terms of natural gas distribution by providing empirical evidence from 356 neighborhoods of Izmir Metropolitan Area (IMA). The aim is to reveal driving factors of natural gas investment and the spatial reflections of the relationships between investment, and socio-economic and physical characteristics. A global regression model, OLS, and two local spatial regression models, GWR and MGWR, are conducted. Population, income, employment and disadvantaged areas are the significant determinants of natural gas investments. Due to the presence of spatial autocorrelation in OLS residuals, GWR and MGWR are utilized to account for spatial variation in the response variable. Local models are superior to the global model according to AICc values, which are 607.3, 586.7, and 558.5, in OLS, GWR, and MGWR, respectively. MGWR further improves the overall fit with higher R-sq and lower AICc values. The local R-sq values indicate at least 70% variability is explained in 85% of the study area in MGWR, and in 73% of IMA in GWR. Parameters are slightly overestimated in GWR at the mean level. None of the local models are subject to multicollinearity according to local condition numbers, but local variance decomposition proportions indicate the effects of multicollinearity in some observations. Spatial modeling of investments helps to demonstrate local variations of energy injustice and to develop site-specific policies. Population and employment are related to potential customers, which lead to higher investments. Income depends on purchasing power, and there are economic barriers that need to be regulated through subsidies and incentives for the low-income households. Awareness raising policies can also be developed to better inform households about energy alternatives. Disadvantaged areas, either declared as urban transformation areas or currently under urban transformation, lack the natural gas service in general, while the investments can be considered in those areas along with the new development plans.

Local Governance, Ecological Knowledge, and Spatial Models: Assessing Resource Access in a Forest Commons

Local Governance, Ecological Knowledge, and Spatial Models: Assessing Resource Access in a Forest Commons