Spatial Units Of Analysis Research Articles

Combination of macroscopic and microscopic crash prediction models with multiple modeling approaches: A highway case study

Road traffic crashes are a global problem and represent a major cause of mortality among children and young people worldwide. Crash prediction models (CPM) play a crucial role in identifying crash-prone areas by predicting the expected number of crashes, thus facilitating the implementation of proactive measures. Previous studies have explored various CPM based on spatial analysis or modeling approaches. Nonetheless, a substantial gap still exists in exploring CPM that simultaneously address the combination of spatial units and modeling approaches, using of heterogeneous data at both the macroscopic and microscopic levels to develop models using Multi-Criteria Decision-Making and Statistical methods. Thus, the objective of this study is to develop multiple CPM using various spatial units of analysis and employing different modeling approaches, combine them, and conduct comparisons to determine the final model that best fits the data. A total of 7 models have been developed: three at the macroscopic level, utilizing Analytic Hierarchy Process (AHP), Negative Binomial (NB), and a combination of both; one at the microscopic level using Geographic Weighted Poisson Regression (GWPR). Additionally, three combined models bring together the previous from both levels, resulting in AHP+GWPR, NB+GWPR, and AHP+NB+GWPR models. These combined and individual models have been compared, and the best-performing has been selected through a validation process. The models were calibrated using data from a case study of a highway located in the northwest of Spain for the period 2016 to 2020. Subsequently, predictions for the year 2021 were made and compared to the actual crash occurrences during that year. The results indicate that the best model is the one that combines the macroscopic and microscopic levels using the approaches AHP+NB+GWPR, which presents a Mean Absolute Deviation (MAD) of 1.55, a Mean Square Error (MSE) of 2.89, does not underestimate any segments and shows a recall of 0.88. The results obtained confirm that the combination of models at the macroscopic and microscopic levels yields the best crashes predictions. This approach offers the advantage that our model, in addition to learning from historical data using statistical methods, also fits to the importance weights assigned by knowledgeable experts on the crashes propensity of the highway in the case study area using the Analytical Hierarchical Process.

Home sharing or crime sharing? Evidences of the relationship between Airbnb, crime and structural factors in Malaga, Spain

ABSTRACT Negative externalities stemming from overtourism have been widely reported in the literature. However, a consequence less explored in scientific research is the increase in crime rates derived from high levels of tourism. This article analyses the relationship between the concentration of Airbnb accommodation and crime density in the city of Malaga (Spain); both the social disorganization and the routine activities theories are used as the theoretical basis. The region under study has been defined by removing the non-urbanized part of the city and the unit of spatial analysis is the census tract. The relationship between Airbnb and crime has been measured by controlling for other factors traditionally related to crime. Two models that take into account the spatial nature of the data have been employed: a two-stage spatially weighted spatial least squares model and a geographically weighted regression model. Results show that Airbnb is significantly related to an increase in crime density in the census tract, as is the proportion of people born in countries with a Human Development Index below .825. Nightlife likewise shows a significant relationship with crime in the city, but this relationship is inverse. Nevertheless, the variable that shows the strongest link is crime itself in neighboring census tracts. An increase in crime in a given region leads to an increase in crime in the surrounding census tracts. However, the local model reveals that the impact of crime in adjacent units is not related in the same way to local crime in region analyzed as a whole. The policy implications of these results and other findings contrary to the original criminological theories are discussed.

A Novel Framework for Joint Learning of City Region Partition and Representation

The proliferation of multimodal big data in cities provides unprecedented opportunities for modeling and forecasting urban problems, such as crime prediction and house price prediction, through data-driven approaches. A fundamental and critical issue in modeling and forecasting urban problems lies in identifying suitable spatial analysis units, also known as city region partition. Existing works rely on subjective domain knowledge for static partitions, which is general and universal for all tasks. In fact, different tasks may need different city region partitions. To address this issue, we propose JLPR , a task-oriented framework for J oint L earning of region P artition and R epresentation. To make partitions fit tasks, JLPR integrates the region partition into the representation model training and learns region partitions using the supervision signal from the downstream task. We evaluate the framework on two prediction tasks (i.e., crime prediction and housing price prediction) in Chicago. Experiments show that JLPR consistently outperforms state-of-the-art partitioning methods in both tasks, which achieves above 25% and 70% performance improvements in terms of mean absolute error for crime prediction and house price prediction tasks, respectively. Additionally, we meticulously undertake three visualization case studies, which yield profound and illuminating findings from diverse perspectives, demonstrating the remarkable effectiveness and superiority of our approach.

Points, cells, or polygons? On the choice of spatial units in forest conservation policy impact evaluation

A fast-growing literature uses remotely sensed land-cover data along with quasi-experimental statistical methods to assess the efficacy of forest conservation interventions. A critical modeling choice is the spatial unit of analysis—points, grid cells, and polygons are all commonly used. Yet little is known about the implications of this choice for treatment effect estimates and for their interpretation. We demonstrate that point-level data can generate treatment effect estimates substantially different from those based on polygon-level data when (i) a disproportionate share of sample points is drawn from relatively large, treated polygons as a result of random or quasi-random spatial sampling, and (ii) the intervention analyzed has heterogeneous effects that depend on treatment polygon size. Our paper has four parts. First, using real-world data (on the award of timber extraction permits to forest management units in Mexico) that meet the two aforementioned criteria, we demonstrate that point- and polygon-level data generate qualitatively different results, and we propose a simple method for weighting the point-level data to recover the polygon-level results. Second, we conduct a Monte Carlo simulation to clarify the mechanism that causes this phenomenon and to provide reassurance that it is not driven by unobserved confounding factors. Third, we present new evidence (on Mesoamerican and Dominican protected areas) suggesting this phenomenon is not uncommon. Finally, we discuss the implications of our findings for the design and interpretation of spatial evaluations of forest conservation interventions. Although our analysis focuses on point- versus polygon-level data, the mechanism we describe also applies to grid cell- versus polygon-level data.

Environmental Injustice in Peel Region: A Cross-Sectional Analysis of Air Pollution and Social Vulnerability

Background: Nitrogen dioxide (NO2), a traffic-related air pollutant, is co-emitted with greenhouse gasses that contribute to climate change and negatively affect health. This research examines the relationship between air pollution exposure and social vulnerability to determine whether communities at a social disadvantage experience environmental injustice. Methods: The 2016 census measures for Peel Region were assessed. Peel Region has 1,381,739 people across Mississauga, Brampton, and Caledon; the spatial unit of analysis was the dissemination area (DA) with an average size of 837 people in Peel. Average ambient NO2 concentrations for 2016 came from the Canadian Urban Environmental Health Research Consortium and resampled to DAs. Social disadvantage was defined by the Ontario Marginalization Index (ON-Marg). Measures within index components came from the Canadian census. Environmental injustice was identified through correlation analysis between ON-Marg and NO2 exposure and an analysis of index components with exposure. Results: The 2016 annual NO2 concentrations were positively correlated with the overall ON-Marg quintiles (Spearman: 0.34, p < .001, 95% CI 0.29 to 0.37). The components and their correlations with NO2 were residential instability (Spearman: 0.37, p < .001, 95% CI 0.33 to 0.42), material deprivation (Spearman: 0.15, p < .001, 95% CI 0.10 to 0.19), dependency (Spearman: 0.31, p < .001, 95% CI 0.26 to 0.35), and ethnic concentration (Spearman: −0.06, p = .02, 95% CI −0.11 to −0.01). Conclusions: In Peel Region, a correlation exists between social vulnerability and air pollution, indicating inequitable exposure to NO2. Marginalized communities may not be able to choose their living environment. Environmental justice research in the context of NO2 will support policy aiming to reduce inequitable exposure.

Inner areas matter: A place-sensitive approach for the identification of the Daily Life Spaces in the Italian Abruzzo region

Despite the institutionalization of the issue of inner areas with the National Strategy for Inner Areas (Strategia Nazionale per le Aree Interne, SNAI), a reflection on their spatial organisation is still missing. Our paper aims at filling this gap by providing a methodology for identifying the citizens' Daily Life Spaces (DLS) in the Italian Abruzzo region, which can be used as the spatial unit of analysis of cohesion policies. Their identification results from a multi-step algorithm based on an original definition of central places and on the notions of geographical and organised proximity which consider both citizens’ isochrones and commuting flows. Our methodology is able to provide the internal spatial organisation of both Labour Market Areas and Project Areas and is consistent with a historical perspective. Finally, it questions the SNAI classification, calling for a revision of its methodology of identification.

Accommodating spatio-temporal dependency in airline demand modeling

The objective of the current study is to examine monthly air passenger departures at the airport level considering spatial interactions between airports. In this study, we develop a novel spatial grouped generalized ordered probit (SGGOP) model system of monthly air passenger departures at the airport level. Specifically, we estimate two variants of spatial models including spatial lag model and spatial error model. In the presence of repeated demand measures for the airports, we also consider temporal variations of spatial correlation effects among proximally located airports by employing space and time-based weight matrix. The proposed model is estimated using monthly air passenger departures for five years for 369 airports across the US. The proposed spatial model is implemented using composite marginal likelihood (CML) approach that offers a computationally feasible framework. From the estimation results, it is evident that air passenger departures at the airport level are influenced by different factors including MSA specific demographic characteristics, built environment characteristics, airport specific factors, spatial factors, and temporal factors. Moreover, spatial autocorrelation parameter is found to be significant validating our hypothesis of the presence of common unobserved factors associated with the spatial unit of analysis. In this study, we also perform a validation analysis to examine the predictive performance of the proposed spatial models. The results highlight the superiority of spatial error model compared to spatial lag model and the independent model that ignores the spatial interactions. Finally, we undertake an elasticity analysis to quantify the impact of the independent variables.

Australian Youth Self-Harm Atlas: spatial modelling and mapping of self-harm prevalence and related risk and protective factors to inform youth suicide prevention strategies.

Suicide prevention strategies have shifted in many countries, from a national approach to one that is regionally tailored and responsive to local community needs. Previous Australian studies support this approach. However, most studies have focused on suicide deaths which may not fully capture a complete understanding of prevention needs, and few have focused on the priority population of youth. This was the first nationwide study to examine regional variability of self-harm prevalence and related factors in Australian young people. A random sample of Australian adolescents (12-17-year-olds) were recruited as part of the Young Minds Matter (YMM) survey. Participants completed self-report questions on self-harm (i.e., non-suicidal self-harm and suicide attempts) in the previous 12months. Using mixed effects regressions, an area-level model was built with YMM and Census data to produce out-of-sample small area predictions for self-harm prevalence. Spatial unit of analysis was Statistical Area Level 1 (average population 400 people), and all prevalence estimates were updated to 2019. Across Australia, there was large variability in youth self-harm prevalence estimates. Northern Territory, Western Australia, and South Australia had the highest estimated state prevalence. Psychological distress and depression were factors which best predicted self-harm at an individual level. At an area-level, the strongest predictor was a high percentage of single unemployed parents, while being in an area where ≥30% of parents were born overseas was associated with reduced odds of self-harm. This study identified characteristics of regions with lower and higher youth self-harm risk. These findings should assist governments and communities with developing and implementing regionally appropriate youth suicide prevention interventions and initiatives.

Application of the Australian Bureau of Statistics Socio-Economic Indexes for Areas in cardiovascular disease research: a scoping review identifying implications for research.

Objective To scope how the Australian Bureau of Statistics Socio-Economic Indexes for Areas (SEIFA) has been applied to measure socio-economic status (SES) in peer-reviewed cardiovascular disease (CVD) research. Methods The Joanna Briggs Institute's scoping review methodology was used. Results The search retrieved 2788 unique citations, and 49 studies were included. Studies were heterogeneous in their approach to analysis using SEIFA. Not all studies provided information as to what version was used and how SEIFA was applied in analysis. Spatial unit of analysis varied between studies, with participant postcode most frequently applied. Study quality varied. Conclusions The use of SEIFA in Australian CVD peer-reviewed research is widespread, with variations in the application of SEIFA to measure SES as an exposure. There is a need to improve the reporting of how SEIFA is applied in the methods sections of research papers for greater transparency and to ensure accurate interpretation of CVD research.

Analyzing the effect of depopulation on the spatial structure of the city of Łódź, Poland: Development and application of an integrated land use and transportation model

This article evaluates the effect of depopulation, as a common trend in European countries and post-industrial cities, on the selected components of urban spatial structure, as an example of Łódź (Poland). An integrated land use and transportation model was adapted and calibrated based on the available data in the study area and modeled the evolution of the city structure from 2020 to 2040. For the land use part, the spatial input-output concept and economic base theory are combined to compute the number of activities in the study area. First, the economic sectors (i.e., land, population, base, and service classes) are defined. Second, the production and consumption of each sector in Łódź is calculated. Third, productions and consumptions are allocated to spatial units for determining the level of activities and flow between them. This process, which was programmed in Python, iterates until it reaches equilibrium in the activity system. For the transportation part, the PTV Visum simulator does the tasks of network representation and traffic assignment. Following the existing literature, changes in the distribution of employment, population, and land consumption within the activity system, along with trip length, trip production, and traffic loading in the mobility system, are regarded as urban structure factors and used for assessing the integrated model outputs. Getis-Ord and Theil's indices are also calculated to obtain the auxiliary results. Decentralization and dispersion in the spatial distribution of activities were identified as the chief characteristics of the city form. As the population reduces in Łódź, the average land consumption and service activities per spatial analysis units drop in the simulation period, which lies in the correlation between population decline and the economic fall. In addition, depopulation leads to flattened spatial structure and lesser development intensity due to the scattered and de-intensified pattern of land consumption. Besides, the population shrinkage causes a decrease in inequality level by 60% and 25% from 2020 to 2030 and 2030 to 2040, respectively, which might root in the reduced and lower difference between population and service simultaneously. Moreover, computations showed that while the number of trips decreases, the trip length increases, which means after depopulation, people travel less between spatial units to fulfill their needs because the city shifts to the borderlines. Findings also assist decision- and policy-makers in recognizing the effect of population loss on Łódź’s spatial structure and offer implications of integrated models for addressing such phenomena.

Downscaling future land cover scenarios for freshwater fish distribution models under climate change

The decreasing freshwater biodiversity trend can be attributed to anthropogenic impacts in terms of climate and land cover change. For targeted conservation efforts, mapping and understanding the distribution of freshwater organisms consists of an important knowledge gap. Spatial modelling approaches offer valuable insights into present-day biodiversity patterns and potential future trajectories, however methodological constraints still hamper the applicability of addressing future climate and land cover change concurrently in one modelling workflow. Compared to climate-only projections, spatially explicit and high-resolution land cover projections have seen less attention, and the lack of such data challenges modelling efforts to predict the possible future effects of land cover change especially on freshwater organisms. Here we demonstrate a workflow where we downscale future land cover projection data from the Shared Socioeconomic Pathway (SSP) scenarios for South America at 1 km2 spatial resolution, to then predict the future habitat suitability patterns of the Colombian fish fauna. Specifically, we show how the land cover data can be converted from plain numbers into a spatially explicit representation for multiple SSP scenarios and at high spatial resolution, employing freshwater-specific downscaling aspects when spatially allocating the land cover category grid cells, and how it can be fitted into an ensemble species distribution modelling approach of 1209 fish species. Our toolbox consists of a suite of open-source tools, including Dinamica EGO, R, GRASS GIS and GDAL, and we provide the code and necessary steps to reproduce the workflow for other study areas. We highlight the feasibility of the downscaling, but also underline the potential challenges regarding the spatial scale and the size of the spatial units of analysis.

Influence of the built environment on taxi travel demand based on the optimal spatial analysis unit.

When discussing the influence of the built environment on taxi travel demand, few studies have considered the effect of the modifiable areal unit problem (MAUP) or the influence of the "5D" dimensions of the built environment (It refers to the consideration of the built environment from five dimensions of density, diversity, design, destination accessibility and distance to transit.) on taxi travel demand. Moreover, discussion of the nonlinear and linear relationships between taxi demand and environment variables is also lacking. To address these gaps, we constructed a "5D" dimension index system of built environment variables. The influence of the MAUP on the model results was discussed using the optimal parameter-based geographical detector (OPGD) model, and the optimal spatial analysis unit was selected. The OPGD and multiscale geographically weighted regression (MGWR) models were used to reveal the influence of different dimensions of the built environment on taxi travel demand from global and local perspectives, respectively. Finally, the central urban area of Xi'an was analyzed as an example. The results show the following: (1) Most built environment variables are sensitive to the influence of MAUP. (2) It is better to divide the space into regular hexagons than squares, and the optimal spatial analysis unit in this study is a regular hexagon grid with sides of 900m. (3) From a global perspective, the distance to the city center, commercial residence POI density, transportation facility POI density, and population density have the greatest influence on the demand for taxi travel. (4) From a local perspective, the MGWR model considering spatial heterogeneity and scale differences is superior to the GWR model, and the influence of built environment variables exhibited spatial heterogeneity. The proposed optimal spatial analysis unit can provide a basis for taxi demand forecasting and scheduling. This study provides a reference for urban planners and traffic managers to offer optimization strategies related to the built environment, promote healthy development of the taxi industry, and solve the problems of the urban transportation system.

Integrated water security and coupling of social-ecological system to improve river basin sustainability

The river basin sustainability depends on both the coordinated development of socio-ecological systems and resilience to water resources. However, the lack of integrating them on spatial and temporal scales compromises our capacity to develop precise interventions towards sustainable river basins. We developed an approach by integrating water security and social-ecological coupling to assess the river basin sustainability. We divided it into four categories including highly sustainable (secure and coordinated), insecure, uncoordinated, and low sustainable (insecure and uncoordinated). The middle reach of Heihe River (MHR) was taken as the study area with the sub-basin as the spatial analysis unit from 2000 to 2020. The results showed that there was heterogeneity and agglomeration in spatial distribution. 23.8 %, 38.8 %, and 11% of the sub-basins mainly clustered in the north and central areas were found in the state of water insecure and SES uncoordinated, or both respectively. The unsustainable areas (five sub-basins) and lose-lose areas (two sub-basins) should be the priority areas for management interventions. Our approach can provide an important reference for assessing and improving the river basin sustainability.

Are ride-hailing services safer than taxis? A multivariate spatial approach with accommodation of exposure uncertainty

Despite many research efforts on ride-hailing services and taxis, limited studies have compared the safety performance of the two modes. A major challenge is the need for reliable mode-specific exposure data to model their safety outcomes. Moreover, crash frequencies of the two modes by injury severities tend to be spatially and inherently correlated. To fully address these issues, this study proposes a novel multivariate conditional autoregressive model considering measurement errors in mode-specific exposures (MVCARME). More specially, a classical measurement error structure accommodates the uncertainty of estimated mode-specific exposures, and a multivariate spatial specification is adopted to capture potential spatial and inherent correlations. The model estimation is accelerated by an integrated nest Laplace approximation method. The census tracts in the city of Chicago are set as the spatial analysis unit. The mode-specific exposures (vehicle-mile-traveled) in each census tract are estimated by trip assignments using ride-hailing and taxi trip data in 2019. The modeling results indicate that both ride-hailing crashes and taxi crashes are positively associated with transportation factors (e.g., vehicle-mile-traveled, mode-specific vehicle-mile-traveled, and traffic signal numbers), land use factors (i.e., number of educational and alcohol-related sites), and demographic factors (e.g., median household income, transit ratio, and walk ratio). By comparison, the proposed model outperforms the others (i.e., negative binomial models and multivariate conditional autoregressive model) by yielding the lowest deviance information criterion (DIC), Watanabe-Akaike information criterion (WAIC), mean absolute error (MAE), and root-mean-square error (RMSE). According to the results of t-tests, ride-hailing services are found to be prone to a higher risk of minor injury crashes compared with taxis, despite no significant difference between the risks of severe injury crashes. Methodologically, this study adds a robust safety evaluation approach for comparing crash risks of different modes to the literature. At the same time, practically, it provides researchers, practitioners, and policy-makers insights into the safety management of various mobility alternatives.

Spatial Identification and Change Analysis of Production-Living-Ecological Space Using Multi-Source Geospatial Data: A Case Study in Jiaodong Peninsula, China

The significant heterogeneity in the spatial distribution of point of interest (POI) data, the absence of human socio-economic activity information in remote sensing images (RSI), and the high cost of land use (LU) data acquisition restrict their application in PLES spatial identification. Utilizing easily accessible data for detailed spatial identification of PLES remains an urgent challenge, especially when selecting a study area that encompasses both urban built-up areas (UBUA) and non-urban built-up areas (NUBUA). To address this issue, we proposed a PLES spatial identification method that combines POI data and land cover (LC) data in this paper. The proposed method first classified spatial analysis units (SAUs) into agricultural production space (APS), ecological space (ES), and ambiguous space (AS) based on the rich surface physical information from LC data. Subsequently, the AS was further classified into living space (LS) and non-agricultural production space (NAPS) based on the rich human socioeconomic information from POI data. For the AS that contains no POI, a simple rule was established to differentiate it into LS or NAPS. The effectiveness of the method was verified by accuracy evaluation and visual comparison. Applying the method to the Jiaodong Peninsula, we identified the PLES of the Jiaodong Peninsula for 2018 and 2022, further explored their spatial distribution characteristics, and analyzed their changes. Finally, we conducted a discussion on the real-world situations and driving mechanisms of the PLES changes and proposed several policy insights. The results indicated that both the spatial distribution characteristics of PLES and PLES change in the Jiaodong Peninsula were obvious and showed significant differentiation between UBUA and NUBUA. Climatic and natural resource conditions, geographic location, macro-policies, and governmental behaviors drove the PLES changes.

Seasonal variance in electric vehicle charging demand and its impacts on infrastructure deployment: A big data approach

Electric vehicle (EV) charging demand is an essential input of charging facility location models. However, charging demand may vary across seasons. In response, this paper first provided insights into the seasonal variance in charging demand using a unique GPS trajectory dataset which contained travel, parking, and charging information of 2,658 private EVs in Beijing. The dataset was collected in January, April, July, and October 2018, which were representative months in winter, spring, summer, and autumn, respectively. Through statistical and spatiotemporal analyses, we found that in winter, EVs got recharged when their state of charge (SOC) was lower: the average SOCs on working days were 51.96%, 48.39%, 50.86%, and 43.50%, in spring, summer, autumn, and winter, respectively. Furthermore, the central urban areas tended to have a higher charging demand in winter. To further explore how the seasonal variance in charging demand may influence infrastructure deployment, we used the classical p-median model to deploy charging facilities with the charging demands in the four seasons, considering the modifiable areal unit problem (MAUP). The results suggested that the seasonal variance did influence the layout of charging facilities under different spatial analysis units (SAUs). The deployment of charging facilities in the central urban areas and outer suburbs tended to be more sensitive to seasonal variance in charging demand. The findings are expected to be useful for charging infrastructure planning in both the transport and power sectors.

A residential location search model based on the reasons for moving out

ABSTRACT Modeling spatial search processes such as residential location search are challenging, particularly, due to the need to deal with a large dataset and wide array of factors. This introduces a multi-dimensionality challenge to location search modeling. With the motivation to accommodate multi-dimensionality, this paper develops a machine learning–based Gaussian mixture model (GMM) for location search. This study accommodates the effects of several factors including accessibility, land use, dwelling, transportation infrastructure, and neighborhood attributes on location search decisions. The spatial unit of analysis is dwelling-level. This study conceptualizes that households’ search for location based on their reason to move. The pool of alternatives for each household is generated based on probability estimates of GMM. The location choice model considering the reason-based GMM outperforms the model without considering relocation reasons in GMM and random sampling-based model in-terms of predictive performance. The search model has been implemented in an integrated urban model.

Urban agglomeration, city size, and spatial density effects on wage flexibility: New evidence on the wage curve in Brazil

AbstractThis study aims to shed new empirical light on the importance of the wage curve in a developing economy. The main contribution to the empirical literature is related to the analysis being conducted at different regional levels of a developing economy. This indicates that municipal‐level data seems to be more adequate for wage curve evaluation, and that spatial dependency should be considered to adequately control for local labor market characteristics. Results for Brazil show that wage flexibility is higher in less dense local labor markets and in the informal sector. Controlling for unobserved local characteristics is necessary to obtain the ‘true’ elasticity of wages with respect to local unemployment rates, while spatial autocorrelation effects should be accounted for when the spatial unit of analysis is rather small to consider the interactions that happen inside a specific labor market area. Finally, a significant part of the difference in outcomes between formal and informal sectors appears to originate from spatial–economic dependence effects, indicating that labor market areas are more suitable for such analysis.

Active governance of agro-pastoral, forest and protected areas mitigates wildfire impacts in Italy

Wildfire regimes affected by global change have been the cause of major concern in recent years. Both direct prevention (e.g., fuel management planning) and land governance strategies (e.g., agroforestry development) can have an indirect regulatory effect on wildfires. Herein, we tested the hypothesis that active land planning and management in Italy have mitigated wildfire impacts in terms of loss of ecosystem services and forest cover, and burned wildland-urban interface, from 2007 to 2017. At the national scale, we assessed the effect size of major potential fire drivers such as climate, weather, flammability, socio-economic descriptors, land use changes, and proxies for land governance (e.g., European funds for rural development, investments in sustainable forest management, agro-pastoral activities), including potential interactions, on fire-related impacts via Random Forest modelling and Generalized Additive Mixed Model. Agro-forest districts (i.e., aggregations of neighbouring municipalities with homogeneous forest and agricultural characteristics) were used as spatial units of analysis. Our results confirm that territories with more active land governance show lower wildfire impacts, even under severe flammability and climatic conditions. This study supports current regional, national, and European strategies towards “fire resistant and resilient landscapes” by fostering agro-forestry, rural development, and nature conservation integrated policies.

The relationship of land tenure, land use and land cover changes in Lake Victoria basin

Water basins characterise both physical and social environmental aspects such as land tenure. As such, the basins extend beyond spatial units of physical resources and human relations analysis to policy research and reform units. The comprehensive view of water basins in research goes along with an observed increase in anthropogenic-driven changes, such as land use and land cover changes, and cases of ineffective remedial measures to the adverse change, such as through applying integrated watershed management approaches. The human-induced land cover changes affect the water basin’s biodiversity, for instance, contributing to an increase in zoonotic disease outbreaks like coronaviruses. The Lake Victoria basin exhibits similar patterns of change and effects due to, among other factors, land tenure, whose contribution is less known empirically. Therefore, this paper integrates satellite imagery and catchment survey data to examine the relationship between land tenure and land uses and land cover changes in the Lake Victoria basin of Eastern Africa.Additionally, explore the contextual character and role of three land tenure systems of Customary, Native freehold and Mailo found in the Uganda country segment of the basin in explaining the outcomes. The aim is to provide information that, among other benefits, improves water basin management and governance. The results indicate a statistically significant relationship exists between the perceived extent of land use and land cover change; drivers of change; the extent of adopting sustainable land-use practices, and the prevailing land tenure. Though with different tenure systems, the three case study water catchments experienced adverse land use and cover changes. The changes mainly affected land tenure indicative land use and cover classes, prominently on the Customary, Mailo, and Native freehold land tenure systems. However, marginal differences occur among the land tenure systems, as the systems feature both de jure and de facto systems and an orientation towards customary tenure characters. The situation likely explains the observed closeness in perceptions regarding the role and relationship between land tenure and land use and cover changes, tenure systems character, perceived drivers of change and eventual outcomes, including the sustainable land use practices adoption. In addition to explaining the land use and cover change, land tenure is an essential tool for restoration and sustainable basin development and sustainability. We, thus, recommend land tenure responsiveness in water basin management approaches for sustainable societal development.