Spatial Units Research Articles

Hierarchical modelling of epibenthic communities on the Scotian Shelf and Gulf of Maine (Atlantic Canada) in support of conservation planning

Identification of ecologically significant units at different spatial scales is essential for management of biodiversity attributes. This case study illustrates a coupled methodological approach to delineate benthic habitats and associated assemblages at different spatial scales. Two complementary analyses were employed based on the occurrences of 99 epibenthic invertebrate species in Atlantic Canada, sampled during depth-stratified random trawl sets. To identify epibenthic assemblage types, isometric feature mapping and partitioning around medoids (ISOPAM) was used in combination with a joint species distribution model (JSDM), which also produced continuous distribution surfaces across the spatial domain. ISOPAM identified nine significantly spatially coherent assemblages (biotopes) at spatial scales of hundreds of meters to hundreds of kilometers, with associated diagnostic species. Those assemblages were closely matched by eight regions of common profile using the JSDM, indicating strong environmental influences on their distributions. These smaller-scale assemblages were grouped into three biophysical units. The results from the JSDM were validated with independent data showing good congruence. This indicates that the spatial units ascribed to identified assemblages are robust and suitable for marine spatial planning and monitoring.

Multi-Head Attention Affinity Diversity Sharing Network for Facial Expression Recognition

Facial expressions exhibit inherent similarities, variability, and complexity. In real-world scenarios, challenges such as partial occlusions, illumination changes, and individual differences further complicate the task of facial expression recognition (FER). To further improve the accuracy of FER, a Multi-head Attention Affinity and Diversity Sharing Network (MAADS) is proposed in this paper. MAADS comprises a Feature Discrimination Network (FDN), an Attention Distraction Network (ADN), and a Shared Fusion Network (SFN). To be specific, FDN first integrates attention weights into the objective function to capture the most discriminative features by using the proposed sparse affinity loss. Then, ADN employs multiple parallel attention networks to maximize diversity within spatial attention units and channel attention units, which guides the network to focus on distinct, non-overlapping facial regions. Finally, SFN deconstructs facial features into generic parts and unique parts, which allows the network to learn the distinctions between these features without having to relearn complete features from scratch. To validate the effectiveness of the proposed method, extensive experiments were conducted on several widely used in-the-wild datasets including RAF-DB, AffectNet-7, AffectNet-8, FERPlus, and SFEW. MAADS achieves the accuracy of 92.93%, 67.14%, 64.55%, 91.58%, and 62.41% on these datasets, respectively. The experimental results indicate that MAADS not only outperforms current state-of-the-art methods in recognition accuracy but also has a relatively low computational complexity.

Scrutinizing the Statistical Distribution of a Composite Index of Soil Degradation as a Measure of Early Desertification Risk in Advanced Economies

Using descriptive and inferential techniques together with simplified metrics derived from the ecological discipline, we offer a long-term investigation of the Environmental Sensitive Area Index (ESAI) as a proxy of land degradation vulnerability in Italy. This assessment was specifically carried out on a decadal scale from 1960 to 2020 at the province (NUTS-3 sensu Eurostat) level and benefited from a short-term forecast for 2030, based on four simplified assumptions grounded on a purely deterministic (‘what … if’) approach. The spatial distribution of the ESAI was investigated at each observation year (1960, 1970, 1980, 1990, 2000, 2010, 2020, 2030) calculating descriptive statistics (central tendency, variability, and distribution shape), deviation from normality, and the increase (or decrease) in diversification in the index scores. Based on nearly 300 thousand observations all over Italy, provinces were considered representative spatial units because they include a relatively broad number of ESAI measures. Assuming a large sample size as a pre-requisite for the stable distribution of the most relevant moments of any statistical distribution—because of the convergence law underlying the central limit theorem—we found that the ESAI scores have increased significantly over time in both central values (i.e., means or medians) and variability across the central tendency (i.e., coefficient of variation). Additionally, ecological metrics reflecting diversification trends in the vulnerability scores delineated a latent shift toward a less diversified (statistical) distribution with a concentration of the observed values toward the highest ESAI scores—possibly reflecting a net increase in the level of soil degradation, at least in some areas. Multiple exploratory techniques (namely, a Principal Component Analysis and a two-way hierarchical clustering) were run on the two-way (data) matrix including distributional metrics (by columns) and temporal observations (by rows). The empirical findings of these techniques delineate the consolidation of worse predisposing conditions to soil degradation in recent times, as reflected in a sudden increase in the ESAI scores—both average and maximum values. These trends underline latent environmental dynamics leading to an early desertification risk, thus representing a valid predictive tool both in the present conditions and in future scenarios. A comprehensive scrutiny of past, present, and future trends in the ESAI scores using mixed (parametric and non-parametric) statistical tools proved to be an original contribution to the study of soil degradation in advanced economies.

Exploring the Spatial Patterns of Accessibility to Metro Services Considering the Locations of Station Entrances/Exits

Accessibility to metro services is often evaluated based on the locations of stations. However, compared to the location of station itself, focusing on its entrances/exits offers a more accurate approach to assessing station supply and demand levels. Despite this, research focusing on the supply of and demand for metro services concerning metro entrances and exits remains limited. This study employed multi-source geospatial data from Xiamen, China, to examine the supply and demand dynamics of metro stations with a particular emphasis on entrances/exits. In the first phase, we treated entrances/exits as supply facilities and used land plot boundaries as the fundamental spatial units for accessibility calculations. Taking into account the layout characteristics of entrances/exits, along with the traffic generation of various land-use types, we employed the Gaussian two-step floating catchment area (G2SFCA) method to gauge the supply and demand levels of plots. Subsequently, we computed the spatial supply-and-demand relationships of station entrances/exits for both station-level and shared usage level of entrances/exits. We found that the accessibility from plots to entrances/exits diverged from previously observed spatial distribution trends, being higher in city centers, regional boundaries, and terminal stations and lower in transitional areas. Moreover, “metro accessibility” and the “imbalance index of entrances/exits” are associated with the primary functions of stations and the surrounding urban development; yet they exhibit spatial heterogeneity. The stations with a high value for “imbalanced index of entrances/exits” were always near some business parks, and “metro accessibility” seemed to be more easily affected by location factors. Based on two metrics, stations were categorized into four types, each displaying unique characteristics regarding location distributions, entrance/exit configurations, and commuting passenger sources. This research aims to identify the phenomenon of unfair transport in metro service from the perspective of their entrances, inform the optimization of metro station designs, and tailor planning recommendations, ultimately enhancing transport equity and contributing to sustainable urban built environments.

Nationwide analysis of air pollution hotspots across India: A spatiotemporal PM2.5 trend analysis (2008–2019)

IntroductionIndia experiences high levels of air pollution as measured by fine particulate matter <2.5 μm (PM2.5) across the country. With limited resources, it is imperative to identify the most impacted areas. We aimed to identify air pollution hotspots in India and analyze temporal trends. MethodsWe conducted a geospatial analysis using Getis-Ord Gi statistics on gridded-annual levels of PM2.5 disaggregated for every state/UT and three largest cities [Delhi, Kolkata & Mumbai] of India from 2008 to 2019. The annual average PM2.5 was derived from a validated and robust nationwide spatiotemporal model(1kmx1km). Hotspots were identified annually using Gi∗ score and p-value and temporal trends across 2 periods [T1:2008–2013 & T2:2014–2019] for each spatial unit. We classified temporal trends based on the number of occurrences of hotspots in T1 and in T2 as consistent (similar in T1 & T2), declining (decreasing in T2) and emerging (increasing in T2) hotspots. ResultsWe identified consistent hotspots in 9.9% followed by emerging hotspots in 2.6% of the country where 16% and 4.9% people live. In addition, we identified declining hotspots in 2.6% area with 3.4% of the population. Rajasthan had largest share of area identified as consistent hotspots while Uttar Pradesh had densely populated consistent hotspots. Maharashtra had both higher number of areas identified as emerging and declining hotspots. Among the largest cities, Kolkata had highest proportion of consistent hotspots. We identified 170 additional cities with either consistent or emerging hotspots beyond the non-attainment cities as defined by the National Clean Air Programme. ConclusionIndia continues to have large areas of consistent and emerging hotspots of air pollution where close to a fifth of India's population live. Identifying hotspots can inform strategic approach for targeted action in air quality management, appropriate resource allocation and a baseline for assessing intervention effectiveness and future programs and policies, including health.

Forecasting Africa’s fertility decline by female education groups

While female education has long been recognized as a key driver of fertility decline during the process of demographic transition and most population projection models consider it implicitly or explicitly in their forecasts of overall fertility, there still is need for a method to forecast education-specific fertility trends directly. Here we propose a method for projecting education-specific fertility declines for cohorts of women in Sub-Saharan Africa based on all available demographic and health surveys data for African countries (including 1.03Mio cases). We study at different levels of aggregation (sample clusters, strata, and national) the associations between ideal family size and completed cohort fertility for education groups, on the one hand, and the average level of education in those units, on the other. The consistently very strong empirical associations suggest a plausible narrative by which a higher prevalence of educated women in a spatial unit influences the fertility levels of women in all specific education categories. Empirical associations between education-specific cohort fertility trends at the national level and newly available quality-adjusted human capital data for these cohorts are then operationalized to produce education-specific population projections as they are needed for-among other uses-the shared socioeconomic pathways scenarios that are widely used in the climate change research community. Sensitivity analyses including out-of-sample projections support the validity of the proposed method which is then applied to 37 African countries.

The performance of two urban flood regulation models using different input data

Increasing climate change has led to an increase in urban flood events. Events with a return period of twenty years become events with a period of two to three years. The primary objectives of this study are to evaluate the performance of flood regulation models using different input data and compare their performance for flood regulation supply (FRS) assessment. The assessment of the ecosystem services of urban FRS was performed using two models: Urban Flood Risk Mitigation (UFRM) by Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) and IMECOGIP Waterflow Regulation (Retention Model) (WRRM). They rely on relatively accessible open-source data, require a short time to model each scenario and provide opportunities to interpret results in different spatial and measurement units. We performed several improvements to the input data, including updating the national ecosystem-type assessment for the case study area by connecting it with the Urban Atlas typology and proposing an approach for soil data refinement. The outputs of the two models show significant differences in FRS values for each land-use/land-cover (LULC) class. The UFRM assesses different impervious urban classes with different values, varying from very low to medium. In contrast, the WRRM assesses the FRS in densely sealed areas with one fixed value for low supply (where nearly all rainfall is transformed into runoff).

GIS and statistics to analyze scale change consequences on medical desert classification in France

Abstract Background Accessibility is a crucial way to overcome geographical restrictions and provides a path for alleviating the medical desert. Less attention has been paid to the choice of spatial unit in the effect of healthcare multi-accessibility. Inside the OASES European program, the France team proposes to create medical desert classification at the municipality level. The contribution of this study is to highlight the consequences of the choice of spatial scale on spatial distribution and the discrepancies between medical desert classifications. Methods Analyses were carried out in metropolitan France and overseas departments, at the commune scale and two other supra-communal scales used by public policies in France. For each spatial unit, a multidimensional and professional classification has been conducted using a three-step method: scores, principal component analysis and clustering. To demonstrate the presence of spatial dependence and to measure homogeneity between classifications joint count autocorrelation indicators and Theil’s indexes were applied. Results Our results present 7 classes of municipalities, and 5 classes of EPCI or living territories according to their level of healthcare accessibility, the dynamic of supply, and the needs of the population. Spatial representation of the classification demonstrates greater variability at the municipality level than other supra scales. Informative contrasted classes at the municipality level disappeared in favor of the middle class in all accessibility at supra scales. The results demonstrate that spatial dependence between areas at the municipality level reduced the scale effect. Conclusions Spatial approaches have become increasingly popular since they enable decision-makers to identify priority areas and allocate resources. Recognizing the instability across various scales is therefore crucial for thinking about the optimal unit of data aggregation when investigating the medical desert. Key messages • Choice of the scale is a mix between public policy decisions and data availability. • Contiguity constraint is a way to reduce the loss of information after aggregation.

Spatial patterns of vascular plant species richness in Poland: Relations among species group richness and hot spot distribution

Knowledge of spatial patterns of species richness (SR) is highly relevant for theoretical research in ecology and the development of conservation plans. In Poland, despite a long tradition of botanical surveys, vascular plant SR has not been mapped, nor have the correlations in richness among different plant species groups been explored at the entire country scale. Here, we used a recently published data set to examine spatial patterns and relationships among the joined SR of vascular plant species, including native species, archaeophytes, neophytes, and species with high conservation value (i.e., red-list species). The basic spatial unit employed was a 10 × 10 km grid from the Atlas of Distribution of Vascular Plants in Poland (ATPOL). We found that SR of native species and archaeophytes, neophytes, and red-list species were positively correlated. The main patterns of SR and the percentage of particular groups in the joined SR were based on three components: (1) gradient of overall SR, (2) invasion level, and (3) peculiarity of flora in some regions resulting from the high number and proportion of rare species that often have high conservation value (red-list species). In general, northeastern Poland was species-poor, while the Carpathian Mountain range, the uplands in southern Poland, and some parts of Wisła River valley had the highest SR concentrations. e location of SR hotspots usually did not overlap with the existing national parks system. The correlations among native SR, high conservation value species, and neophyte SR suggest that biological invasions are among the most important threats to vascular plant diversity in Poland. Finally, despite likely biases in SR assessments, we demonstrated that the presented maps seem to reflect general ecological gradients influencing vascular plant distribution in Poland.

Evaluation of Çankırı and Iğdır salt caves within the scope of health and recreation tourism

With the increasing popularity of healing methods involving salt, the importance of salt mines has grown significantly over time. Today, as salt therapy (Speleotherapy) becomes more widespread, salt caves in many countries around the world have begun to transform into salt therapy centers. Salt caves in Spain (Cardona), Belarus (Soliharsk), Romania (Slanic, Salina Turda, and Praid), Poland (Wieliczka), Pakistan (Khewra), Germany (Berchtesgaden), Azerbaijan (Nakhchivan), and Turkey (Çankırı and Iğdır Tuzluca) are used for therapeutic purposes. Particularly, Çankırı Salt Cave and Iğdır Tuzluca Salt Cave are important resources in Turkey that can be evaluated within the scope of health tourism. In this study, a literature review, official website analysis, and on-site observations were conducted. In the literature review, previous research on the subject was examined. Through website analysis, the spatial units and recreational activities of the salt caves were identified. The spatial units and recreational activities of each salt cave were determined using the collected data. Subsequently, on-site observations were made by visiting the salt caves in Çankırı and Iğdır. During these observations, the spatial units within the salt caves were visited and photographed individually. Then, the data obtained from the literature, official website analyses, and on-site observations were combined. Finally, the characteristics of all the examined caves were tabulated, and based on the created table, the caves were evaluated. As a result of the evaluation, the Cardona Salt Mine in Spain stands out for its historical and cultural activities; the Nakhchivan in Azerbaijan, Soligorsk in Belarus, Slanic and Praid Salt Mines in Romania are treatment centers offering salt therapy services; the Salina Turda Salt Mine in Romania is an underground theme park; the Wieliczka Salt Mine in Poland, Khewra Salt Mine in Pakistan, and Berchtesgaden Salt Mine in Germany are primarily used for recreational purposes; while the developing Çankırı and Iğdır Tuzluca Salt Mines in Turkey are also noted for their recreational use.

Climate-Driven vs Human-Driven Land Degradation? The Role of Urbanization and Agricultural Intensification in Italy, 1960–2030

Climate warming, agricultural intensity, and urban growth are main forces triggering land degradation in advanced economies. Being active over different spatial and temporal scales, they usually reflect—at least indirectly—the impact of additional factors, such as wellbeing, demographic dynamics, and social development, on land quality. Using descriptive statistics and a multiple regression analysis, we analyzed the impact of these three processes comparatively over a decadal scale from 1960 to 2020 at the provincial level (Nuts-3 sensu Eurostat) in Italy. We enriched the investigation with a short-term forecast for 2030, based on four simplified assumptions grounded on a purely deterministic approach. Land degradation was estimated adopting the Environmental Sensitive Area Index (ESAI) measured at the spatio-temporal scale mentioned above. Computing on multiple observations at nearly 300,000 locations all over Italy, provinces were regarded as representative spatial units of the territorial pattern of land degradation. Between 1960 and 1990, the three predictors (climate, agriculture, and urbanization) explained a relatively high proportion of variance, suggesting a modest role for any other (unobserved) factor. All of these factors were found to be highly significant predictors of land degradation intensity across provinces, the most impactful being farming intensity. The highest adjusted-R2 coefficient was observed in both 1990 and 2000, and suggests that the three predictors still reflect the most powerful drivers of land degradation in Italy at those times, with a marginal role for additional (unobserved) factors. The impact of farming intensity remained high, with the role of urbanization increasing moderately, and the role of climate aridity declining weakly between 2000 and 2010. In more recent times (2010 and 2020), and in future (2030) scenarios, the adjusted R2 diminished moderately, suggesting a non-negligible importance of external (unobserved) factors and the rising role of spatial heterogeneity. The climate factor became progressively insignificant over time, while increasing the role of urbanization systematically. The impact of farming intensity remained high and significant. These results underlie a latent shift in the spatial distribution of the level of land vulnerability in Italy toward a spatially polarized model, influenced primarily by human pressure and socioeconomic drivers and less intensively shaped by biophysical factors. Climate aridity was revealed to be more effective in the explanation of land degradation patterns in the 1960s rather than in recent observation times.

Robot Grasping Detection Method Based on Keypoints

ABSTRACTThis study introduces a novel keypoint‐based grasp detection network, denoted as GKSCConv‐Net, which operates on n‐channel input images. The network architecture comprises three SCConv2D layers and three SCConvT2D layers. The SCConvT2D layers facilitate upsampling to maintain consistent dimensions between the output and input images. The resultant output consists of maps indicating left grasp points, right grasp points, and grasp center keypoints. The accuracy of predictions is enhanced through the incorporation of the keypoint refinement module and feature fusion module. To validate the model's generalization and applicability, comprehensive training, testing, and evaluation are conducted on diverse data sets, including the Cornell data set, Jacquard data set, and others representing real‐world scenarios. Furthermore, ablation experiments are employed to substantiate the efficacy of the spatial reconstruction unit (SRU) and channel reconstruction unit (CRU) within the SCConv, exploring their impact on grasp keypoint detection outcomes. Real robotic grasping experiments ultimately affirm the model's outstanding performance in practical settings.

SCKansformer: Fine-Grained Classification of Bone Marrow Cells via Kansformer Backbone and Hierarchical Attention Mechanisms.

The incidence and mortality rates of malignant tumors, such as acute leukemia, have risen significantly. Clinically, hospitals rely on cytological examination of peripheral blood and bone marrow smears to diagnose malignant tumors, with accurate blood cell counting being crucial. Existing automated methods face challenges such as low feature expression capability, poor interpretability, and redundant feature extraction when processing highdimensional microimage data. We propose a novel finegrained classification model, SCKansformer, for bone marrow blood cells, which addresses these challenges and enhances classification accuracy and efficiency. The model integrates the Kansformer Encoder, SCConv Encoder, and Global-Local Attention Encoder. The Kansformer Encoder replaces the traditional MLP layer with the KAN, improving nonlinear feature representation and interpretability. The SCConv Encoder, with its Spatial and Channel Reconstruction Units, enhances feature representation and reduces redundancy. The Global-Local Attention Encoder combines Multi-head Self-Attention with a Local Part module to capture both global and local features. We validated our model using the Bone Marrow Blood Cell FineGrained Classification Dataset (BMCD-FGCD), comprising over 10,000 samples and nearly 40 classifications, developed with a partner hospital. Comparative experiments on our private dataset, as well as the publicly available PBC and ALL-IDB datasets, demonstrate that SCKansformer outperforms both typical and advanced microcell classification methods across all datasets.

Varying size and shape of spatial units

When an analysis over a specific geographic area is performed, the way that area is divided into regions can affect the outcome of the analysis. Results obtained based on different geographic units can be conflicting. This issue is known as the Modifiable Areal Unit Problem (MAUP). The objective of this paper is to understand the extent to which the regional setting influences the results of an analysis with spatially aggregated variables, with a focus on agglomeration effects, in the case of Germany. Relying on a sample of manufacturing firms over 7 years we estimate a fixed effects model to explain the firm-specific total factor productivity in dependence of region-based agglomeration variables. We simulate 1000 regional settings of Germany on three scales and overtake thereby some characteristics of the administrative units, which are used as the baseline. We infer that the spatial scale and shape matter in the case of Germany.

ADT: Person re-identification based on efficient attention mechanism and single-channel dual-channel fusion with transformer features aggregation

In person re-identification(Re-ID) tasks, using only convolutional neural network(CNNs) makes it challenging to fully exploit the global key information in person images, while using only the Transformer structure fails to adequately capture the multi-scale local detail information in person images. Therefore, to address the more challenging person Re-ID tasks, effectively integrating CNN and Transformer into an algorithmic framework capable of fusing multi-dimensional features has become one of the current focal points of research. Due to the ability of the attention mechanism to suppress the extraction of non-salient features by convolutional neural networks, and the capability of the dual-channel multi-scale mechanism to facilitate the extraction of finer-grained deep information by the Transformer, we propose a network Based on Efficient Attention Mechanism and Single-channel Dual-channel Fusion with Transformer Features Aggregation (ADT). First, we introduce the Convolutional Block Attention Module(CBAM) and Spatial Reconstruction Unit (SRU) into the CNN network to construct a Multi-dimensional Attention Features Re-fusion Network(MCF). By weighting the local features extracted in the convolutional network, attention to person detail features in the model is enhanced. Then, we propose the Single and Dual-channel Integrated Transformer Features Aggregator(SDT) and overlap it in MCF. This integration is designed to more effectively aggregate the multi-scale high-dimensional global features from each layer of the CNN network. Experimental results on the Market1501, DukeMTMC, MSMT17, and CUHK03 datasets demonstrate that our model significantly enriches features representation compared to most methods.

Community centre for sustainable communities in kampung kota: a literature review

Abstract Sustainable development’s basic principle requires a balance between Three Es: to meet economic, environmental, and social equity needs. This paper intends to outline how sustainable communities are supported, among other things, by community engagement, which is facilitated by community centres. Kampung kota (urban villages) are in the spotlight of discussion, considering that these spatial units are real and contribute to the urban community’s network, especially in Indonesia, but are often ignored in terms of priorities for physical and social development. The first part of this paper will explain the importance of community centres in forming community social capital. The second part describes the character of kampung kota community life. The third section analyses the potential and problems of community centres as a basis for sustainable community development in kampung kota.

Surprisingly good fit of pressure-based cropland condition map and bird census data at the national scale

As the world is struggling to halt the rapid decline of biodiversity, the assessment and mapping of ecosystem condition is getting ever increasing attention. Croplands are artificial ecosystems, but as they occupy a large portion of land, they significantly influence biodiversity. Yet, there is a knowledge gap about their suitability to support and maintain wildlife on a national scale. Large-scale condition mapping is meant to address this gap; the good condition of croplands includes their ability to support biodiversity. However, the lack of suitable databases is often a challenge when creating such maps. As there is a strong causal relation between pressures on the ecosystem and its condition, pressure indicators can be used as proxies to approximate condition when more direct indicators are lacking. The validation of condition maps based on pressure proxies is a key but challenging step. In this study, we tested a previously designed pressure-based cropland condition map for Hungary using bird census data. Besides validating the composite condition indicator, we also tested some key elements of the mapping process, such as the choice of variables and thresholds.Using multiple comparisons of means by Tukey’s contrast and Random Forest modelling, we examined the relationship of (1) the continuous pressure-based cropland condition variables, (2) their rescaled, ordinal version (called sub-indicators), and (3) the composite cropland condition indicator (sum of the sub-indicators) with a biodiversity measure, the standardised relative richness of characteristic farmland bird species (rRRCS). To get a picture of the spatial patterns of the examined relationships across Hungary, individual Random Forest models were constructed for all the spatial units of the bird census database, using focal analysis with a 30 km radius moving window.We found significant differences in the mean rRRCS for nearly all sub-indicator categories, signifying that the literature-derived thresholds were mostly sound. Categories with higher (better) condition scores had higher mean rRRCS; the differences are significant in the mid-range but not in the extreme categories, indicating a need for a meaningful simplification of the categories. The goodness-of-fit (R2) of the Random Forest models was found to be high, but it is spatially heterogeneous (ranged from 0.69 to 0.89, with a median value of 0.81), similarly to the variable importance. The proportion of semi-natural areas proved to be the most important condition variable. The proportion of maize and alfalfa were more important than parcel size. Our results show that condition maps based on pressure proxies can reflect patterns of biodiversity surprisingly well. They also highlight the spatial context dependence of the uncertainty of condition maps.

Suicide variations between English neighbourhoods over 2017-21: The role of spatial scale

Geographic studies of suicide variation typically focus on predictors at the same level as the event rates, and the possible interplay between different spatial scales does not generally figure. In this paper we focus on suicide variations between 6856 small area census units in England, but against a background provided by nine regions, broad urban-rural categories, and 155 local labour markets. Suicide death totals vary considerably between the small areas, with more areas than expected having no deaths, so we apply zero inflated regression. With this framework, we consider the relative contribution of factors at higher and lower spatial scales in explaining small area suicide contrasts, and why some areas have unduly elevated or unduly low suicide rates. We find significantly lower suicide levels in English metropolitan regions, after allowing for neighbourhood influences, but considerable heterogeneity in risks within broader spatial units. Varying incidence in general is associated significantly with all observed neighbourhood risk factors (social fragmentation, socioeconomic status, mental ill-health, ethnic mix), but low fragmentation and low psychiatric morbidity are the only significant influences on unduly low incidence.

Spatial patterns and drivers of micro, small and medium-sized enterprises (MSMEs) within and across Indonesian cities: Evidence from highly granular data

The importance of micro-, small-, and medium-sized enterprises (MSMEs) for economies and livelihood is widely recognized, especially in emerging economies. However, there are scarcely any studies on their spatial determinants, particularly in Indonesia, where MSMEs account for more than 95 % of enterprises. This paper aims to assess the spatial drivers of MSMEs within and between Indonesian cities using multilevel modeling techniques that incorporate district (n=98) and nested village-level (n=4995) spatial units. The study's findings have implications for policy interventions, suggesting the need to enhance infrastructure and digital connectivity, promote employment accessibility, and consider planning mechanisms to influence MSMEs' spatial configuration.

Hydro-Climatic Factors and Health Problems in the Town of Ouéllé (Central-Eastern Côte d'Ivoire)

This article deals with hydro-climatic factors and their health impacts in the town of Ouéllé in a context of climate change. The aim of this study is to show the impact of hydro-climatic determinants on the spatial distribution of pathologies in the town of Ouéllé. The methodological approach used was based on data collection techniques ranging from documentary research to field surveys. Interviews were conducted with experts, while questionnaires were administered to 100 people living in the urban area of Ouéllé. In addition, photographs of the spatial units were taken using a Redmi 9A smartphone, followed by the collection of geospatial data using the UTM Geo map mobile application for data collected in KML (Keyhole Markup Language) format on the Android operating system. We also used statistical data analysis techniques to process the data. The results showed that the main hydro-climatic factors in Ouéllé are rainfall, temperature and wind. The diseases linked to these hydro-climatic factors are malaria, which is the main health risk, accounting for 63% of consultations in 2023, followed by typhoid fever (8%) and acute respiratory infections (ARI) (3%).