Spatial Analysis Research Articles

Relationship between Advanced Manufacturing Agglomeration and Regional Economic–Innovative–Social Coupling Coordination Development: A Spatial Analysis in Liangjiang New Area, China

Relationship between Advanced Manufacturing Agglomeration and Regional Economic–Innovative–Social Coupling Coordination Development: A Spatial Analysis in Liangjiang New Area, China

COVID-19 vaccination at a hospital in Paris: Spatial analyses and inverse equity hypothesis

BackgroundVaccination against SARS-CoV-2 has been deployed in France since January 2021. Without specific action for different population subgroups, the inverse equity hypothesis postulates that people in the most deprived neighbourhoods will be the last to benefit. The article aims to study whether the inverse care law has been verified in the context of vaccination against SARS-CoV-2 from a vaccination centre of a hospital in the Paris region. MethodsWe performed a spatial analysis using primary data from the vaccination centre of the Avicenne Hospital in Bobigny from January 8th to September 30th, 2021. Primary data variables include the vaccinated person's date, age, and postal address. Secondary data calculates access times between residential neighbourhoods and the vaccination centre and social deprivation index. We performed flow analysis, k-means aggregation, and mapping. Results32,712 people were vaccinated at the study centre. Vaccination flow to the hospital shows that people living in the most disadvantaged areas were the last to be vaccinated. The number of people immunized according to the level of social deprivation then scales out with slightly more access to the vaccination centre for the most advantaged. The furthest have travelled more than 100 kilometres, and more than 1h45 of transport time to get to this vaccination centre. ConclusionThe study confirms the inverse equity hypothesis and shows that vaccination preparedness strategies must consider equity issues. Public health interventions should be implemented according to proportionate universalism and use community health, health mediation, and outreach activities for more equity.

How to restore ecological impacts from wind energy? An assessment of Zhongying Wind Farm through MSPA-MCR model and circuit theory

The wind energy industry is developing rapidly in China with a maximum capacity worldwide. Meanwhile, the stability of the ecological network is challenged by extensive renewable energy expansion. There are still research deficiencies in wind farm ecological impacts at project scale from an ecological restoration perspective. It is of great necessity to monitor the ecological impacts with time going and explore ecological restoration strategies for wind power development. The research case of Zhongying Wind Farm is selected based on investigation and interviews regarding its ecological impact in the prior study. It employs a comparative methodology by comparing the ecological quality before and after the construction of Zhongying Wind Farm. Raw data from Landsat 8, the Digital Elevation Model (DEM), the Normalized Difference Vegetation Index (NDVI), and wind turbine statistics originating from open data platforms are analyzed and visually represented with ArcGIS. This paper establishes an ecological network based on morphological spatial pattern analysis (MSPA) and the Minimum Cumulative Resistance (MCR) model to quantify wind farms’ impacts on ecological corridors. The circuit theory and Pajek software are used to identify and categorize the ecological nodes into weak and obstacle points. The results reveal that ecological impacts on ecological corridors decay with distance, reflecting changes in length, connection strength and cumulative values of corridors. While the impacts on ecological nodes depend on nodes’ attributes: the number of weak points increased by 18.5 %, and obstacle points increased by 350 %, with their locations shifting from the edges of urban construction sites to the edges of wind farm. Differentiated strategies are proposed according to the impact magnitude of each ecological corridor and node: such as dynamic inspection mechanism, construction prohibition for protection; corridor structure and function recovering, stepping stones addition for restoration; buffer belts, warning radar, and monetary schemes for compensation. This research fills the gap of ecological impact assessment under the challenges of wind energy expansion. It helps to balance wind energy development and ecological restoration at the regional level.

Akirin2 enhances antibacterial ability via interacting with 14-3-3ζ in V. splendidus-challenged Apostichopus japonicus

Akirin2 is pivotal for regulating host immunological responses in vertebrates, including antibacterial immunity and inflammation. However, the functional significance of Akirin2 in invertebrates remains largely unexplored. In this study, we cloned the complete cDNA sequence of Akirin2 from A. japonicus (AjAkirin2) and elucidated its immunological mechanism upon pathogen infection. The whole AjAkirin2 cDNA sequence spanned 1014 bp, which comprised a 630 bp open reading frame encoding 209 amino acids, a 230 bp 5'-untranslated region (UTR), and a 154 bp 3'-UTR. Spatial expression analysis displayed constitutive expression of AjAkirin2 in all examined tissues. Both mRNA and protein expression abundance of the AjAkirin2 showed considerably high in coelomocytes of sea cucumbers challenged with Vibrio splendidus or stimulated with lipopolysaccharide. In addition, we found that sea cucumbers with 107 CFU/mL V. splendidus infection had a lower survival rate upon AjAkirin2 knockdown. Mechanistically, the result of GST-pull down and co-IP assays indicated that AjAkirin2 directly interacted with Aj14-3-3ζ. Moreover, we also detected that AjAkirin2 positively regulated Aj14-3-3ζ expression in sea cucumber coelomocytes. Furthermore, the knockdown of AjAkirin2 or Aj14-3-3ζ resulted in increasing intracellular bacteria load and suppressed the expression of key genes of the NF-κB signaling pathway (p65 and p105) and inflammatory cytokines including IL-17, VEGF, and MMP-1. In summary, these results confirmed the critical role of AjAkirin2 in mediating innate immune responses against V. splendidus infection via interaction with Aj14-3-3ζ and thereby exerting antibacterial function.

Environmental footprints of the data center service sector in Sweden

The global data center (DC) sector has expanded rapidly during the last decades, due to the rising demand for digital services. In the Nordic region, Sweden has emerged as a global hub, attracting leading technology companies like Amazon, Facebook, Microsoft, and Google. Server halls of DCs are energy intensive buildings, which puts pressure on local water resources and contributes to global greenhouse gas emissions. This study aims to, firstly, quantify the environmental impact of DCs, based on energy usage, water consumption, and greenhouse gas (GHG) emissions. Secondly, it develops a planning tool by employing a multi-criteria approach to optimally locate new DCs and to assess the site suitability of existing ones in Sweden. Data of various performance indicators (geographical data on renewable energy accessibility, free cooling conditions, excess heat receivers, and resilience to water shortages) of DCs was collected through different means, e.g., questionnaire surveys, permit applications, company websites, and other open online data repositories. ArcGIS Pro was employed for spatial analysis, and 68 DCs with a site suitability index (SSI) ≤ 45% were identified as less ideally located. The principal findings are centered on Sweden, and thereby primarily benefit stakeholders engaged in decision-making for evaluating existing or strategic planning of new DCs by incorporating a comprehensive environmental perspective. Given the rapidly changing climate, strategically siting DCs will become crucial for minimizing the sector's environmental impact.

Geo-spatial epidemiology of gallbladder cancer in Bihar, India

Recently, a substantial increase in gallbladder cancer (GBC) cases has been reported in Bihar, India. The region's groundwater can naturally contain harmful concentrations of arsenic, which appears to be epidemiologically linked to the unusually high incidence. However, the root causes remain largely unexplored. Recent findings of uranium in the state's groundwater may also have associations. This study investigates the geo-spatial epidemiology of GBC in Bihar, India—with a focus on the correlation between environmental carcinogens, particularly arsenic and uranium in groundwater, and the incidence of GBC. Utilizing data from 8460 GBC patients' registration records over an 11-year period at a single health center, the research employs Semi-parametric Geographically Weighted Poisson Regression (S-GWPR) to account for non-stationarity associations and explores significant factors contributing to GBC prevalence at a subdistrict level. The S-GWPR model outperformed the standard Poisson regression model. The estimates suggest that arsenic and uranium concentrations in groundwater did not present significant associations; however, this could be due to the lower resolution of this data at the district level, necessitating higher resolution data for accurate estimates. Other socio-environmental factors included demonstrated significant regional heterogeneity in their association with GBC prevalence. Notably, each 1 % increase in the coverage of well- and canal-irrigated areas is associated with a maximum of 3.0 % and 5.2 % rise in the GBC incidence rate, respectively, likely attributable to carcinogen exposure from irrigation water. Moreover, distance to the health center and domestic electricity connections appear to influence the number of reported GBC cases. The latter suggests that access to electricity might have facilitated the use of groundwater pumps—increasing exposure to carcinogens. The results underscore the necessity for targeted health policies and interventions based on fine-resolution spatial analysis, as well as ongoing environmental monitoring and research to better understand the multifaceted risk factors contributing to GBC.

Precise Wireless Camera Localization Leveraging Traffic-Aided Spatial Analysis

Precise Wireless Camera Localization Leveraging Traffic-Aided Spatial Analysis

Fundamental LCZ Mapping with ArcGIS-Based Spatial and Correlational Analyses to Study the Impacts of Infrastructural Area- Maximum Volume (A-MV) Attributes on OTC – An Urban Aw Summer Case

The aim of this study is to map the study location stretches into 100mx100m local climate zones (LCZs) on the basis of primary observations and secondary photographic data for spatial analysis of various urban infrastructural elements (grey(G)-built structures, blue(B)-water bodies and green (Gr)-natural vegetation) and impact assessment of their estimated area (EA) and maximum volume (EMV)) on the air temperature (Ta) that characterizes the outdoor thermal comfort (OTC). Climate Modification by Urban Infrastructure, Fundamental Concepts of Urban Infrastructure Mensuration Parameters, Fundamental Concepts of Urban Grey Blue Green Infrastructure (UGBGI) and Concepts Of OTC, Koppen Classification and LCZs are the linked areas. Study locations are based on socio-economic and geophysical attributes. Initial 200 pilot survey responses regarding thermal sensation votes and other subjective details were used. Preferred values of Physiological Equivalent Temperature (PET) and optimum Ta have been used. After the mapping in ArcGIS and polygonal delineation of the areas, EA values were compiled in tabular format. Based on primary measurements and secondary studies, a maximum height (hmax) value was assigned to each sub-category for EMV evaluation. Statistically, the EA and estimated perimeter (EP) values were analyzed using QQ, scatter plots, histogram and generate near table tools in ArcGIS. EA and EMV values were correlated with mean Ta values by linear regression. The R-square values generated gave a clear idea about the extent of their impacts on Ta. From the established relationships and preferred PET and optimum Ta values, optimum EA, EMV and hmax values of the broader categories(G,B,Gr) required for preferred OTC levels were finally evaluated.

Early Injury Landscape in Vein Harvest by Single-Cell and Spatial Transcriptomics.

Vein graft failure following cardiovascular bypass surgery results in significant patient morbidity and cost to the health care system. Vein graft injury can occur during autogenous vein harvest and preparation, as well as after implantation into the arterial system, leading to the development of intimal hyperplasia, vein graft stenosis, and, ultimately, bypass graft failure. Although previous studies have identified maladaptive pathways that occur shortly after implantation, the specific signaling pathways that occur during vein graft preparation are not well defined and may result in a cumulative impact on vein graft failure. We, therefore, aimed to elucidate the response of the vein conduit wall during harvest and following implantation, probing the key maladaptive pathways driving graft failure with the overarching goal of identifying therapeutic targets for biologic intervention to minimize these natural responses to surgical vein graft injury. Employing a novel approach to investigating vascular pathologies, we harnessed both single-nuclei RNA-sequencing and spatial transcriptomics analyses to profile the genomic effects of vein grafts after harvest and distension, then compared these findings to vein grafts obtained 24 hours after carotid-cartoid vein bypass implantation in a canine model (n=4). Spatial transcriptomic analysis of canine cephalic vein after initial conduit harvest and distention revealed significant enrichment of pathways (P<0.05) involved in the activation of endothelial cells (ECs), fibroblasts, and vascular smooth muscle cells, namely pathways responsible for cellular proliferation and migration and platelet activation across the intimal and medial layers, cytokine signaling within the adventitial layer, and ECM (extracellular matrix) remodeling throughout the vein wall. Subsequent single-nuclei RNA-sequencing analysis supported these findings and further unveiled distinct EC and fibroblast subpopulations with significant upregulation (P<0.05) of markers related to endothelial injury response and cellular activation of ECs, FBs, and vascular smooth muscle cells. Similarly, in vein grafts obtained 24 hours after arterial bypass, there was an increase in myeloid cell, protomyofibroblast, injury-response EC, and mesenchymal-transitioning EC subpopulations with a concomitant decrease in homeostatic ECs and fibroblasts. Among these markers were genes previously implicated in vein graft injury, including VCAN, FBN1, and VEGFC, in addition to novel genes of interest such as GLIS3 and EPHA3. These genes were further noted to be driving the expression of genes implicated in vascular remodeling and graft failure, such as IL-6, TGFBR1, SMAD4, and ADAMTS9. By integrating the spatial transcriptomics and single-nuclei RNA-sequencing data sets, we highlighted the spatial architecture of the vein graft following distension, wherein activated and mesenchymal-transitioning ECs, myeloid cells, and fibroblasts were notably enriched in the intima and media of distended veins. Finally, intercellular communication network analysis unveiled the critical roles of activated ECs, mesenchymal-transitioning ECs, protomyofibroblasts, and vascular smooth muscle cells in upregulating signaling pathways associated with cellular proliferation (MDK, PDGF [platelet-derived growth factor], VEGF), transdifferentiation (Notch), migration (ephrin, semaphorin), ECM remodeling (collagen, laminin, fibronectin), and inflammation (thrombospondin), following distension. Vein conduit harvest and distension elicit a prompt genomic response facilitated by distinct cellular subpopulations heterogeneously distributed throughout the vein wall. This response was found to be further exacerbated following vein graft implantation, resulting in a cascade of maladaptive gene regulatory networks. Together, these results suggest that distension initiates the upregulation of pathological pathways that may ultimately contribute to bypass graft failure and presents potential early targets warranting investigation for targeted therapies. This work highlights the first applications of single-nuclei and spatial transcriptomic analyses to investigate venous pathologies, underscoring the utility of these methodologies and providing a foundation for future investigations.

Groundwater suitability assessment for irrigation and drinking purposes by integrating spatial analysis, machine learning, water quality index, and health risk model.

An in-depth understanding of nitrate-contaminated surface water and groundwater quality and associated risks is important for groundwater management. Hydrochemical characteristics and driving forces of groundwater quality and non-carcinogenic risks of nitrate were revealed by the integrated approaches of self-organizing map analysis, spatial visualization by geography information system, entropy and irrigation water quality indices, and human health risk model. Groundwater samples were categorized into two clusters by SOM analysis. Cluster I including three samples were Ca-SO4 type and cluster II of remaining 136 samples were Ca-HCO3 type. Hydrochemical compositions of two cluster samples were dominated by water-rock interaction: (1) calcite and gypsum dissolution for cluster I samples and (2) calcite dissolution, silicate weathering, and positive cation exchange for cluster II samples. Nitrate contamination occurred in both cluster I and II samples, primarily induced by agricultural nitrogen fertilizer. The EWQI results showed that 90.97% in total groundwater samples were suitable for drinking purpose, while the IWQI results demonstrated that 65.03% in total groundwater samples were appropriate for irrigation purpose. The HHR model and Monte Carlo simulation indicated that the non-carcinogenic nitrated risk was highest in children. Exposure frequency was the most sensitive factor (86.33% in total) influencing the total non-carcinogenic risk, indicated by sensitivity analysis. Compared with the two clusters of groundwater, surface water has a shorter circulation cycle and lower ion concentrations resulting in better water quality. This study can provide scientific basis for groundwater quality evaluation in other parts of the world.

Spatial analysis and machine learning: Towards integrated predictive modeling advancements

This paper presents a comprehensive exploration of the integration of spatial analysis with machine learning techniques, aiming to enhance predictive modeling capabilities across various domains. Spatial analysis, a methodological approach for understanding geographic patterns and relationships, when combined with the computational power of machine learning, offers unprecedented opportunities for analyzing complex spatial datasets. Through quantitative analysis and the application of mathematical models, this study demonstrates the effectiveness of this integration in improving the accuracy and efficiency of predictive models. The research encompasses a range of applications, from environmental monitoring to urban planning and public health, showcasing the versatility and potential of combining spatial analysis with machine learning.

The Hidden Reserve of Nurses in The Netherlands: A Spatial Analysis

Worldwide, nurse shortages constitute a problem, including in the Netherlands. Every region in the country has a shortage of all types of nurses. At the same time, there are large hidden reserves: persons who have been trained as a nurse but do not work in the healthcare sector. The size of the hidden reserve exceeds the shortage of nurses. Until now, the literature has not paid much attention to spatial aspects of the nursing shortage problem. In this paper, we analyze the size of the hidden reserves and how they are distributed over the country, across large and smaller cities, and across different nurse categories. We find that especially densely populated areas have relatively small shares of trained nurses as well as large hidden reserves relative to their population. These areas are also facing the largest nurse shortages. As nurse labor markets are local, policies that are more focused on local situations are necessary to activate these hidden reserves.

Spatial variation and predictors of incomplete pneumococcal conjugate vaccine (PCV) uptake among children aged 12–35 months in Ethiopia: spatial and multilevel analyses

BackgroundDespite the Ethiopian government included the Pneumococcal Conjugate Vaccine (PCV) in the national expanded program for immunization in 2011, only 56% of children aged 12–23 months received the full dose of PCV. Despite some studies on PCV uptake in Ethiopia, there was a dearth of information on the geographical distribution and multilevel factors of incomplete PCV uptake. Hence, this study aimed to identify the spatial variations and predictors of incomplete PCV uptake among children aged 12–35 months in Ethiopia.MethodsThe study was based on an in-depth analysis of 2016 Ethiopia Demographic Health Survey data, using a weighted sample of 3,340 women having children aged 12–35 months. Arc-GIS version 10.7 and SaTScan version 9.6 statistical software were used for the spatial analysis. To explore spatial variation and locate spatial clusters of incomplete PCV, the Global Moran's I statistic and Bernoulli-based spatial scan (SaTScan) analysis were carried out, respectively. A multilevel mixed-effect multivariable logistic regression was done by STATA version 16. Adjusted odds ratio (AOR) with its corresponding 95% CI was used as a measure of association, and variables with a p &amp;lt; 0.05 were deemed as significant determinants of incomplete PCV.ResultsThe overall prevalence of incomplete PCV in Ethiopia was found to be 54.0% (95% CI: 52.31, 55.69), with significant spatial variation across regions (Moran's I = 0.509, p &amp;lt; 0.001) and nine most likely significant SaTScan clusters. The vast majority of Somali, southeast Afar, and eastern Gambela regions were statistically significant hot spots for incomplete PCV. Lacking ANC visits (AOR = 2.76, 95% CI: 1.91, 4.00), not getting pre-birth Tetanus injections (AOR = 1.84, 95% CI: 1.29, 2.74), home birth (AOR = 1.72, 95% CI: 1.23, 2.34), not having a mobile phone (AOR = 1.64, 95% CI: 1.38, 1.93), and residing in a peripheral region (AOR = 4.63; 95% CI: 2.34, 9.15) were identified as statistically significant predictors of incomplete PCV.ConclusionThe level of incomplete PCV uptake was found to be high in Ethiopia with a significant spatial variation across regions. Hence, the federal and regional governments should collaborate with NGOs to improve vaccination coverage and design strategies to trace those children with incomplete PCV in peripheral regions. Policymakers and maternal and child health program planners should work together to boost access to maternal health services like antenatal care and skilled delivery services to increase immunization coverage.

Spatial clustering of domestic violence attitudes toward women in Ghana.

Violence against women is a global public health issue associated with increased morbidity and mortality. The United Nations defines violence against women as "any act of gender-based violence that results in, or is likely to result in, physical, sexual, or mental harm or suffering to women, including threats of such acts, coercion or arbitrary deprivation of liberty, whether occurring in public or in private life". There is paucity of data on the spatial distribution and predictors of violence against women in sub-Saharan Africa. The objective of this study was to investigate the geographical distribution of attitudes toward wife beating in Ghana, a sub-Saharan African country, utilizing data from the 2014 Ghana Demographic and Health Survey (DHS). Participants from over eleven thousand households were surveyed on topics of demographics and justification of wife beating in at least one of five different scenarios. The identification of geographic clusters of men and women who endorsed wife beating was performed using Ripley K functions. The comparison of the spatial distributions of women and men justifying wife beating were performed using spatial relative risk surfaces. The spatial analysis indicated the presence of clusters in women and men's approval for wife beating compared to those who do not approve of wife beating, with a statistical significance level set at p < 0.01. Major spatial clusters of approval of wife beating were in the Northern region, for both men and women, and in the Upper West region of Ghana for the men participants. This is the first study to explore the geographical distribution of attitudes toward wife beating in Ghana, and revealed evidence of several regional heterogeneous clusters where wife beating was more commonly justified by both men and women. Targeted intervention for reducing the justification of wife beating in Ghana should be focused in these regions.

The spatial spillover effect of green technology innovation on water pollution–evidence from 283 Chinese cities

While economic development brings serious environmental problems, technological advances can effectively reduce pollution, which helps to achieve the Sustainable Development Goals. Although the impact of green technology innovation on atmospheric pollutants and carbon emissions has been extensively studied, the effect of such innovation on pollutant reduction varies due to the diverse regional distribution characteristics of different pollutants. Thus, this paper contributes to the literature by examining the influence of green technology innovation on water pollution from a regional perspective, with a particular emphasis on the pronounced clustering of wastewater pollution in China's coastal areas. Both Fixed-Effect model and spatial analysis are adopted. Our findings reveal a significant U-shaped relationship between technology innovation and water pollution, as measured by both industrial wastewater and the ratio of unprocessed sewage. Interestingly, this pollution reduction effect also exhibits a U-shaped spatial spillover. Given the rapid development of the digital economy, it can further amplify the spatial spillover effect of green innovation, especially in eastern regions. This study also provides recent empirical evidence from China to the Environmental Kuznets Curve.

Coordinate descent algorithm for generalized group fused Lasso

AbstractWe deal with a model with discrete varying coefficients to consider modeling for heterogeneity and clustering for homogeneity, and estimate the varying coefficients by generalized group fused Lasso (GGFL). GGFL allows homogeneous groups to be joined together based on one-to-many relationships among groups. This makes GGFL a powerful technique, but to date there has been no effective algorithm for obtaining the solutions. Here we propose an algorithm for obtaining a GGFL solution based on the coordinate descent method, and show that a solution for each coordinate direction converges to the optimal solution. In a simulation, we show our algorithm is superior to ADMM, which is one of the popular algorithms. We also present an application to a spatial data analysis.

Spatio-temporal variations of habitat quality in the Greater Bay Area around Hangzhou Bay, based on LUCC and simulation

Context Habitat quality (HQ) is vital for achieving sustainable regional development. Understanding the spatial patterns and temporal evolution of HQ in the context of land use–cover change (LUCC) is crucial for promoting ecological civilisation and high-quality growth, especially for regions with rapid economic development. Aims In order to analyse the impact of LUCC on habitat quality in the coastal areas with frequent human activities. Methods This study focused on the Greater Bay Area around Hangzhou Bay and analysed the HQ on the basis of LUCC data during 2010–2020 by using the InVEST model and spatial autocorrelation analysis. Additionally, land-use data for multiple scenarios in 2030 were predicted using the PLUS model, and the variations in land use and HQ in the study region during 2020–2030 were assessed. Key results During 2010–2020, the construction land in the region expanded by 1932.79 km2, primarily at the expense of cropland and water areas. The mean HQ values were 0.6287, 0.6181 and 0.6037 for 2010, 2015 and 2020 respectively, indicating a continuous decline. Spatially, HQ exhibited strong clustering during this period. However, there was a clear trend of fragmentation and reduction in ‘high–high’ cluster areas along the coast, mostly owing to the transformation of water areas and wetlands into construction land and cropland. In the projected scenarios (natural development, ND; economic development, ED; crop protection, CP; and ecological protection, EP) for 2030, the mean HQ values are estimated to be 0.5881, 0.5837, 0.5915 and 0.5965 respectively. Compared with 2020, there will be a certain decrease in HQ, with the EP scenario showing the lowest decrease of 0.0052. Conclusions The HQ changes were closely linked to LUCC, the construction-land expansion was the main cause of HQ destruction in the Greater Bay Area around Hangzhou Bay. To alleviate the trend of declining HQ, it is essential to select appropriate development scenarios for each city in the region and coordinate the development of the cities. Implications These findings provide valuable insights for promoting sustainable economic growth in the Greater Bay Area around Hangzhou Bay.

Spatial patterns and trends of inter-city population mobility in China—Based on Baidu migration big data

Based on Baidu migration big data from the Spring Festival travel rush periods in 2022 and 2023, this study employs a combination of GIS-based spatial statistical analysis and social network analysis to analyze over 270,000 travel data records covering 297 prefecture-level cities. The research reveals that the population flow during the 2023 Spring Festival travel rush increased both in overall quantity and in the proportion of inter-provincial population flow compared to the same period in 2022. The distribution of population movement shows a distinct “southeast dense, northwest sparse” pattern, forming a diamond-shaped structure with the Bohai Sea Rim, Yangtze River Delta, Pearl River Delta, and Sichuan-Chongqing regions as the vertices. Among the most significant inter-city mobility routes are Guangzhou ↔ Foshan, Shenzhen ↔ Dongguan, Shanghai ↔ Suzhou, Xi'an ↔ Xianyang, and Beijing ↔ Langfang, which constitute the five routes with the highest flow volumes, each with its own distinctive patterns and characteristics. Significant agglomeration effects in population movement are observed, with the majority of the 40 cities experiencing higher net inflows situated in the Yangtze River Delta and Pearl River Delta regions. On the other hand, many cities in the western and northeastern regions, including some third-tier and even second-tier cities, are facing net population outflows. As the working population and major economic activities increasingly concentrate in large-scale central cities, the future development of prefecture-level cities may gradually diverge.

Detecting spatial and spatio-temporal clusters of rape in India, 2011–2020

The study aims to identify the spatial and spatio-temporal patterns of rape in India from 2011 to 2020 using crime data aggregated at the district level. The study also tries to understand persistent patterns in the spatial variation of rape incidence across the country during the study period. The annual rape data from 2011 to 2020 were extracted from the National Crime Records Bureau. Retrospective spatial and temporal cluster analyses were performed using the purely spatial (Kulldorff’s and Mann–Whitney scan statistic) and space–time scan statistic (Kulldorff’s based on the discrete Poisson model). Cluster frequency analysis was performed. Results showed the spatial heterogeneity in the pattern of rape crime across the country as well as its localization among geographically contiguous regions across time and space. The detected primary spatial clusters (P < 0.05) persisted mainly in the Central, North, and North Eastern zones of the country over the ten year study period. The primary spatio-temporal cluster (LLR = 5560.09, P < 0.001) appeared between the time frame 2014 to 2016 and was concentrated mainly in the Northern zone. Thirty-seven districts of India (5.78%) contributed to spatial clusters in all years of the study period. The heterogeneous distribution of rape across India could be due to the spatio-temporal variations in the determinants of rape (such as socioeconomic status, demographics, urbanisation, and gender equality) across the country. Identification of the localized rape clusters could result in understanding the contextual factors of rape and, thereby be beneficial to national and regional rape control strategies.

Morphological and molecular preservation through universal preparation of fresh-frozen tissue samples for multimodal imaging workflows.

The landscape of tissue-based imaging modalities is constantly and rapidly evolving. While formalin-fixed, paraffin-embedded material is still useful for histological imaging, the fixation process irreversibly changes the molecular composition of the sample. Therefore, many imaging approaches require fresh-frozen material to get meaningful results. This is particularly true for molecular imaging techniques such as mass spectrometry imaging, which are widely used to probe the spatial arrangement of the tissue metabolome. As high-quality fresh-frozen tissues are limited in their availability, any sample preparation workflow they are subjected to needs to ensure morphological and molecular preservation of the tissues and be compatible with as many of the established and emerging imaging techniques as possible to obtain the maximum possible insights from the tissues. Here we describe a universal sample preparation workflow, from the initial step of freezing the tissues to the cold embedding in a new hydroxypropyl methylcellulose/polyvinylpyrrolidone-enriched hydrogel and the generation of thin tissue sections for analysis. Moreover, we highlight the optimized storage conditions that limit molecular and morphological degradation of the sections. The protocol is compatible with human and plant tissues and can be easily adapted for the preparation of alternative sample formats (e.g., three-dimensional cell cultures). The integrated workflow is universally compatible with histological tissue analysis, mass spectrometry imaging and imaging mass cytometry, as well as spatial proteomic, genomic and transcriptomic tissue analysis. The protocol can be completed within 4 h and requires minimal prior experience in the preparation of tissue samples for multimodal imaging experiments.