Spatial pattern recognition for near-surface high temperature increases in mountain areas using MODIS and SRTM DEM

Abrupt near-surface temperature changes in mountainous areas are a special component of the mountain climate system.Fast and accurate measurements of the locations,intensity,and width of the near-surface changes are necessary but highly difficult due to the complicated environmental conditions and instrumental issues.This paper develops a spatial pattern recognition method to measure the near-surface high temperature increase(NSHTI),one of the lesser-attended changes.First,raster window measurement was proposed to calculate the temperature lapse rate using MODIS land surface temperature and SRTM DEM data.It fully considers the terrain heights of two neighboring cells on opposite or adjacent slopes with a moving window of 3×3 cell size.Second,a threshold selection was performed to identify the NSHTI cells using a threshold of-0.65℃/100 m.Then,the NSHTI strips were parameterized through raster vectorization and spatial analysis.Taking Yunnan,a mountainous province in southwestern China,as the study area,the results indicate that the NSHTI cells concentrate in a strip-like pattern along the mountains and valleys,and the strips are almost parallel to the altitude contours with a slight northward uplift.Also,they are located mostly at a 3/5 height of high mountains or within 400 m from the valley floors,where the controlling topographic index is the altitude of the terrain trend surface but not the absolute elevation and the topographic uplift height and cutting depth.Additionally,the NSHTI intensity varies with the geographic locations and the proportions increase with an exponential trend,and the horizontal width has a mean of about 1000 m and a maximum of over 5000 m.The result demonstrates that the proposed method can effectively recognize NSHTI boundaries over mountains,providing support for the modeling of weather and climate systems and the development of mountain resources.

Optimizing Spatial Pattern Analysis in Serial Remote Sensing Images through Empirical Mode Decomposition and Ant Colony Optimization

Serial remote sensing images offer a valuable means of tracking the evolutionary changes and growth of a specific geographical area over time.Although the original images may provide limited insights,they harbor considerable potential for identifying clusters and patterns.The aggregation of these serial remote sensing images(SRSI)becomes increasingly viable as distinct patterns emerge in diverse scenarios,such as suburbanization,the expansion of native flora,and agricultural activities.In a novel approach,we propose an innovative method for extracting sequential patterns by combining Ant Colony Optimization(ACD)and Empirical Mode Decomposition(EMD).This integration of the newly developed EMD and ACO techniques proves remarkably effective in identifying the most significant characteristic features within serial remote sensing images,guided by specific criteria.Our findings highlight a substantial improvement in the efficiency of sequential pattern mining through the application of this unique hybrid method,seamlessly integrating EMD and ACO for feature selection.This study exposes the potential of our innovative methodology,particularly in the realms of urbanization,native vegetation expansion,and agricultural activities.

Effect of Quadrat Shape on Spatial Point Pattern Performance of Haloxylon ammodendron

In this study, we investigated the natural growth of Haloxylon ammodendron forest in Moso Bay, southwest of Gurbantunggut Desert. Random sample analysis was used to analyze the spatial point pattern performance of Haloxylon ammodendron population. ArcGIS software was used to summarize and analyze the spatial point pattern response of Haloxylon ammodendron population. The results showed that: 1) There were significant differences in the performance of point pattern analysis among different random quadrants. The paired t-test for variance mean ratio showed that the P values were 0.048, 0.004 and 0.301 respectively, indicating that the influence of quadrat shape on the performance of point pattern analysis was significant under the condition of the same optimal quadrat area. 2) The comparative analysis of square shapes shows that circular square is the best, square and regular hexagonal square are the second, and there is no significant difference between square and regular hexagonal square. 3) The number of samples plays a decisive role in spatial point pattern analysis. Insufficient sample size will lead to unstable results. With the increase of the number of samples to more than 120, the V value and P value curves will eventually stabilize. That is, stable spatial point pattern analysis results are closely related to the increase of the number of samples in random sample square analysis.

Enhancing transboundary natural tourism resources governance:unveiling the spatial pattern and its influencing factors

Tourism resources that span provincial boundaries in China play a pivotal role in regional development,yet effective governance poses persistent challenges.This study addresses this issue by constructing a comprehensive database of transboundary natural tourism resources(TNTR)through amalgamation of diverse data sources.Utilizing the Getis-Ord Gi^(*),kernel density estimation,and geographical detectors,we scrutinize the spatial patterns of TNTR,focusing on both named and unnamed entities,while exploring the influencing factors.Our findings reveal 7883 identified TNTR in China,with mountain tourism resources emerging as the predominant type.Among provinces,Hunan boasts the highest count,while Shanghai exhibits the lowest.Southern China demonstrates a pronounced clustering trend in TNTR distribution,with the spatial arrangement of biological landscapes appearing more random compared to geological and water landscapes.Western China,characterized by intricate terrain,exhibits fewer TNTR,concurrently unveiling a significant presence of unnamed natural tourism resources.Crucially,administrative segmentation influences TNTR development,generating disparities in regional goals,developmental stages and intensities,and management approaches.In response to these variations,we advocate for strengthening the naming of the unnamed transboundary tourism resources,constructing a geographic database of TNTR for government and establishing a collaborative management mechanism based on TNTR database.Our research contributes to elucidating the intricate landscape of TNTR,offering insights for tailored governance strategies in the realm of cross-provincial tourism resource management.

Tuning the diffusion constant to optimize the readout of positional information of spatial concentration patterns

Positional information encoded in spatial concentration patterns is crucial for the development of multicellular organisms.However,it is still unclear how such information is affected by the physically dissipative diffusion process.Here we study one-dimensional patterning systems with analytical derivation and numerical simulations.We find that the diffusion constant of the patterning molecules exhibits a nonmonotonic effect on the readout of the positional information from the concentration patterns.Specifically,there exists an optimal diffusion constant that maximizes the positional information.Moreover,we find that the energy dissipation due to the physical diffusion imposes a fundamental upper limit on the positional information.

Temporal and spatial evolution of global major grain trade patterns

The complex and volatile international landscape has significantly impacted global grain supply security. This study uses a complex network analysis model to examine the evolution and trends of the global major grain trade from 1990 to 2020, focusing on network topology, centrality ranking, and community structure. There are three major findings. First, the global major grain trade network has expanded in scale, with a growing emphasis on diversification and balance. During the study period, the United States, Canada, China, and Brazil were the core nodes of the network. Grain-exporting countries were mainly situated in Asia, the Americas, and Europe, and importing countries in Asia, Africa, and Europe. Second, a significant increase in the number of high centrality countries with high export capacity occurred, benefiting from natural advantages such as fertile land and favorable climates. Third, the main global grain trade network is divided into four communities, with the Americas-Europe community being the largest and most widespread. The formation of the community pattern was influenced by geographic proximity, driven by the core exporting countries. Therefore, the world needs to enhance the existing trade model, promote the multi-polarization of the grain trade network, and establish a global vision for the future community. Countries and regions should participate actively in global grain trade security governance and institutional reform, expand trade links with other countries, and optimize import and export policies to reduce trade risks.

Salinity fronts shape spatial patterns in zooplankton distribution in Hangzhou Bay

Ocean fronts play important roles in nutrient transport and in the shaping ecological patterns.Frontal zones in small bays are typically small in scale,have a complex structure,and they are spatially and temporally variable,but there are limited data on how biological communities respond to this variation.Hangzhou Bay,a mediumsized estuary in China,is an ideal place in which to study the response of plankton to small-scale ocean fronts,because three water masses(Qiantang River Diluted Water,Changjiang River Diluted Water,and the East China Sea current) converge here and form dynamic salinity fronts throughout the year.We investigate zooplankton communities,and temperature,salinity and chlorophyll a(Chl a) in Hangzhou Bay in June(wet perio d) and December(dry period) of 2022 and examine the dominant environmental factors that affect zooplankton community spatial variability.We then match the spatial distributions of zooplankton communities with those of salinity fronts.S alinity is the most important explanatory variable to affect zooplankton community spatial variability during both wet and dry periods,in that it contributes>60% of the variability in community structure.Furthermore,the spatial distributions of zooplankton match well with salinity fronts.During December,with weaker Qiantang River Diluted Water and a stronger secondary Changjiang River Plume,zooplankton communities occur in moderate salinity(MS,salinity range 15.6±2.2) and high salinity(HS,22.4±1.7) regions,and their ecological boundaries closely match the Qiantang River Diluted Water front.In June,different zooplankton communities occur in low salinity(LS,3.9±1.0),MS(11.7±3.6) and HS(21.3±1.9) regions.Although the LS region occurs abnormally in the central bay rather than its apex because of the anomalous influence of rising and falling tides during the sampling perio d,the ecological boundaries still match salinity interfaces.Low-salinity or brackish-water zooplankter taxa are relatively more abundant in LS or MS regions,and the biomass and abundance of zooplankton is higher in the MS region.

Spatial Patterns and Drivers of Intelligent Manufacturing from a‘Glob-allocal'Perspective:A Study of China's Industrial Robotics

The advancement of the intelligent manufacturing industry(IMI)represents the future direction for the world's manufactur-ing sector,offering a promising avenue to bolster national competitiveness and enhance industrial manufacturing efficiency.In this study,we took the industrial robot industry(IRI)as a case study to elucidate the spatial distribution and interconnections of IMI from a geographical perspective,and the modified diamond model(DM)was used to analyze the influencing factors.Results show that:1)the spatial pattern of IRI with various investment attributes in different industrial chain links is generally similar,centered in the southeast.Key investment areas are in the east and south.The spatial distribution of China's IRI covers a multitude of provinces and obtains differ-ent scales of investment in different countries(regions).2)The spatial correlation between foreign investors and China's provincial-level administrative regions(PARs)forms a network,and the network of foreign-invested enterprises is more stable.Different countries(regions)have distinct location preferences in China,with significant spatial differences in correlation degrees.3)Overall,the interac-tion of these factors shapes the location decisions and correlation patterns of industrial robot enterprises.This study not only contributes to our theoretical knowledge of the industrial spatial structure and industrial economy but also offers valuable references and sugges-tions for national IMI planning and relevant industry investors.

Spatial Distribution Pattern and Influencing Factors of Bed-and-breakfasts(B&Bs)from the Perspective of Urban-rural Differences:A Case Study of Jiaodong Peninsula,China

There are significant differences between urban and rural bed-and-breakfasts(B&Bs)in terms of customer positioning,economic strength and spatial carrier.Accurately identifying the differences in spatial characteristics and influencing factors of each type,is essential for creating urban and rural B&B agglomeration areas.This study used density-based spatial clustering of applications with noise(DBSCAN)and the multi-scale geographically weighted regression(MGWR)model to explore similarities and differences in the spatial distribution patterns and influencing factors for urban and rural B&Bs on the Jiaodong Peninsula of China from 2010 to 2022.The results showed that:1)both urban and rural B&Bs in Jiaodong Peninsula went through three stages:a slow start from 2010 to 2015,rapid development from 2015 to 2019,and hindered development from 2019 to 2022.However,urban B&Bs demonstrated a higher development speed and agglomeration intensity,leading to an increasingly evident trend of uneven development between the two sectors.2)The clustering scale of both urban and rural B&Bs continued to expand in terms of quantity and volume.Urban B&B clusters characterized by a limited number,but a higher likelihood of transitioning from low-level to high-level clusters.While the number of rural B&B clusters steadily increased over time,their clustering scale was comparatively lower than that of urban B&Bs,and they lacked the presence of high-level clustering.3)In terms of development direction,urban B&B clusters exhibited a relatively stable pattern and evolved into high-level clustering centers within the main urban areas.Conversely,rural B&Bs exhibited a more pronounced spatial diffusion effect,with clusters showing a trend of multi-center development along the coastline.4)Transport emerged as a common influencing factor for both urban and rural B&Bs,with the density of road network having the strongest explanatory power for their spatial distribution.In terms of differences,population agglomeration had a positive impact on the distribution of urban B&Bs and a negative effect on the distribution of rural B&Bs.Rural B&Bs clustering was more influenced by tourism resources compared with urban B&Bs,but increasing tourist stay duration remains an urgent issue to be addressed.The findings of this study could provide a more precise basis for government planning and management of urban and rural B&B agglomeration areas.

Spatial patterns of Picea crassifolia driven by environmental heterogeneity and intraspecifi c interactions

Research on the spatial patterns of tree populations is critical for understanding the structure and dynamic processes of forests.However,little is known about how the underlying drivers shape these patterns and species interactions in forest systems.In this study,spatial point pattern analysis investigated the combined eff ects of intraspecifi c interactions and environmental heterogeneity on the spatial structure and internal maintenance mechanisms of Picea crassifolia in the Qilian Mountain National Nature Reserve,China.Data were obtained from a 10.2-ha dynamic monitoring plot(DMP)and sixteen 0.04-ha elevation gradient plots(EGPs).Under complete spatial randomness,both mature trees and saplings in the DMP demonstratedlarge-scale aggregation with negative correlations.In EGPs,saplings were clustered in small mesoscales,mature trees were randomly distributed,and the interactions of saplingstrees at all elevations were not correlated.By eliminating the interference of environmental heterogeneity through the inhomogeneous Poisson process,saplings in the DMP and EGPs were clustered in small scales and trees randomly distributed.Intraspecifi c associations were negatively correlated,in the DMP and at low elevations,and no correlations in high elevations of EGPs.In the vertical scale,saplings showed a small-scale aggregation pattern with increase in elevation,and the aggregation degree fi rst decreased and then increased.The interactions of saplings-trees and saplings–saplings showed inhibitions at small scales,with the degree of inhibition gradually decreasing.Spatial patterns and associations of adults–adults did not change signifi-cantly.The results revealed that intraspecifi c interactions and environmental heterogeneity regulated the spatial patterns of P.crassifolia at small and large scales,respectively.Environmental heterogeneity might be the most decisive factor aff ecting the spatial patterns of saplings,while trees were more aff ected by intraspecifi c interactions.Moreover,competition between trees in this area could be more common than facilitation for the growth and development of individuals.

Spatial and Temporal Change of Habitat Quality of Poyang Lake Basin in China at Small Watershed Scale and Its Multidimensional Response to Landscape Pattern

The health and function of ecosystems are largely determined by the quality of habitat,and the optimal regulation of landscape patterns has become an important way to improve regional habitat quality.This article takes the Poyang Lake Basin of China as a case,reveals the spatial and temporal change of its habitat quality at the small watershed scale,and attempts to examine the multidimensional response of habitat quality to landscape pattern changes with respect to landscape compositions and landscape configuration.The results show:1)from 2000 to 2020,the overall landscape fragmentation of the basin decreased,the landscape aggregation in the central small watersheds changed significantly,and the spatial distribution of landscape elements in the central and southern small watersheds were relatively homogeneous.2)The overall habitat quality of the Poyang Lake Basin is at a middle to high level,with significant spatial differentiation,showing the distribution characteristics of‘high in the periphery,low in the center,high in the south and low in the north’.3)Both landscape compositions and landscape configurations influenced habitat quality,but there were obvious differences in the response degree.From the landscape composition and configuration dimension,the influence of landscape composition on habitat quality was greater than that of landscape configuration;from the multilevel landscape configuration dimension,compared to landscape level,landscape configuration at the class level impacted habitat quality more deeply.To the end,this article proposes a differentiated regulation strategy for habitat quality conservation in small watersheds from the perspective of landscape patterns to improve the ecological service level of Poyang Lake Basin.

Spatial and temporal patterns of the sensitivity of radial growth response by Picea schrenkiana to regional climate change in the Tianshan Mountains

Climate change significantly impacts forest ecosystems in arid and semi-arid regions.However,spatiotemporal patterns of climate-sensitive changes in individual tree growth under increased climate warming and precipitation in north-west China is unclear.The dendrochronological method was used to study climate response sensitivity of radial growth of Picea schrenkiana from 158 trees at six sites during 1990-2020.The results show that climate warming and increased precipitation significantly promoted the growth of trees.The response to temperature first increased,then decreased.However,the response to increased precipitation and the self-calibrating Palmer Drought Severity Index(scPDSI)increased significantly.In most areas of the Tianshan Mountains,the proportion of trees under increased precipitation and scPDSI positive response was relatively high.Over time,small-diameter trees were strongly affected by drought stress.It is predicted that under continuous warming and increased precipitation,trees in most areas of the Tianshan Mountains,especially those with small diameters,will be more affected by precipitation.

Classification and Spatial Pattern of Township Development in Liaoning Province, China

The classification of township development types is an urgent problem that requires solution to enable the township to choose an appropriate development path.Using a township development classification method,we determine the township development types and their spatial patterns in Liaoning Province,China.The results showed that the patterns of township development types based on their general advantages had significant spatial differentiations.The planting,and livestock and poultry breeding township development types based on general advantages were mainly distributed across the central plain of Liaoning Province,China,and also concentrated in Dandong City−Dalian City along Yellow Sea coast,and in the northwest of Chaoyang City.The business and tourism,industry and mining,and residence township development types based on general advantages were distributed mainly along the Shenyang–Dalian Economic Belt in the central and southern Liaoning Province.The ecology township development type based on general advantages was mainly distributed in the eastern and western Liaoning Province to maintain regional ecological security.Township development types based on non-advantages were sporadically distributed in the middle and western Liaoning Province.Based on the classification and spatial patterns,the differences between the distribution of twonship development types and the plan for the major functional areas of Liaoning Province were proposed which could provide the basis for the optimization of the major functional areas.

Spatial patterns of the main trees in a plant micro-reserve constructed in the 2022 Beijing Winter Olympics mountainous competition zone

Several plant micro-reserves were established to preserve the vegetation in local mountain areas during the construction of the Yanqing competition zone of the 2022 Beijing Winter Olympics.The spatial patterns of the main species in one of the micro-reserves and the factors affecting these patterns were characterized in this study.The distribution of arbor species was found to be mostly aggregated,especially at fine scales(<5 m).Minor species were found to be more aggregated than the major species in each forest layer.The spatial patterns were found to be affected by habitat heterogeneity,intraspecific relationships,interspecific competition,and seed dispersal limitation.Habitat heterogeneity was found to affect large-scale spatial patterns,and its effects were observed throughout population development.Interspecific competition is another factor affecting the distribution of the species,and its effects were stronger during the later stages of population development.Habitat heterogeneity was found to affect competition among species and is key for species coexistence.Both these processes are affected by the seed dispersal limitation,and intraspecific relationships are a legacy of seed dispersal.The point patterns can be used as a tool for the initial assessment of the status of communities within micro-reserves.The consideration of these relationships in the development,management,and formulation of policies for micro-reserves in mountainous areas will facilitate the achievement of conservation goals.The careful consideration of habitat conditions when selecting sites for micro-reserves establishment can promote species conservation.

Spatial Distribution Pattern and Influencing Factors of Physical Bookstores of Large Cities:A Case Study of Three National Central Cities in Western China

As cultural facilities,physical bookstore is an important part of urban infrastructure.Influenced by the development of social economy and the internet,physical bookstores also have become a combination of cultural space and tourism experience.In this case,it is necessary to explore the spatial characteristics and influencing factors of physical bookstores.This study uses Density-Based Spatial Clustering of Applications with Noise(DBSCAN),spatial analysis and geographical detectors to calculate the spatial distribution pattern and factors influencing physical bookstores in national central cities/municipality(hereafter using cities)in western China.Based on spatial data,population density,road density and other data,this study constructed a data set of the influencing factors of physical bookstores,consisting of 11 factors along 6 dimensions for 3 national central cities in western China.The results are as follows:first,the spatial distribution pattern of physical bookstores in Xi’an,Chengdu,and Chongqing is unbalanced.The spatial distribution of physical bookstores in Xi’an and Chongqing is from southwest to northeast and are relatively clustered,while those in Chengdu are relatively discrete.Second,the spatial distribution pattern of physical bookstores has been formed under the influence of different factors.The intensity and significance of influencing factors differ in the case cities.However,in general,the social factor,business factor,the density of research facilities,tourism factor and road density are the main driving factors in the three cities.There is a synergistic relationship between public libraries and physical bookstores.Third,the explanatory power becomes stronger after the interaction between various factors.In Xi’an and Chengdu,the density of communities and the density of research facilities have stronger explanatory power for the dependent variable after interacting with other factors.However,in Chongqing,the traffic factors have stronger explanatory power for the dependent variable after interacting with other factors.The results could provide a practical reference for the sustainable development of physical bookstores and encourage a love of reading among the public.

Spatial Pattern and Distribution of Crime in Suleja Lga, Niger State, Nigeria

The study examines the Spatial Pattern and Distribution of Crime in Suleja LGA, Niger State, Nigeria. The study used GIS and statistical methods to analyse the pattern and distribution of crime incidence in the study area. The records of each crime incidence were geocoded. Microsoft Excel was used to collate and organise the crime entries before they were imported into the ArcGIS Pro 2.0 environment. A geodatabase was created where the spatial and aspatial data were encoded and geospatial analysis was performed. The study reveals that the crime distribution pattern is generally clustered with a Global Moran’s I index of 0.097, a Z-score of 1.87, and a P-value < 0.06. Furthermore, the study reveals that armed robbery (61), kidnapping (40), car theft (33), culpable homicide (31), rape (29), and robbery (13) cases rank the highest in crime rate. Equally, findings of the study show that Chaza, Kwamba, Madalla, Suleja central, and Gaboda are the major crime hotspot zones at 90% confidence, as analysed using the Getis-Ord Gi* (Hot spot analysis) spatial statistics tool in ArcGIS Pro 2.0. The research therefore recommends that more effort be put into fighting crime, especially in areas where there are low-security formations, as they mostly have the highest record of crimes committed. Also, the patrol units should be equipped with GPS for better surveillance and real-time tracking of criminal activities.

Spatial pattern of urban-rural integration in China and the impact of geography

Urban-rural integration (URI) is a global challenge that is highly related to inequalities, poverty, economic growth, and other Sustainable Development Goals (SDGs). Existing research has evaluated the extent of URI and explored its influencing factors, but urban-rural linkages are seldom incorporated in evaluation systems, and geographical factors are rarely recognized as the influencing factors. We construct a URI framework including regional economy, rural development, urban-rural linkage, and urban-rural gap. Based on a dataset consisting of 1,669 counties in China in 2020, we reveal the spatial pattern of URI and find a high correlation between the spatial pattern of URI and the relief degree of land surface (RDLS). Using structural equation modeling, we discover that topography has direct ( − 0.18, p < 0.001) and indirect ( − 0.17, p < 0.001) effects on URI. The indirect negative effects are mediated through the infrastructure, and the combination of localized advantages and modern technical conditions could mitigate the negative impact of topography. Finally, we identify 742 counties as lagging regions in URI, which can be clustered into eight types. Our findings could facilitate policy designing for those countries striving for integrated and sustainable development of urban and rural areas.

The Spatial Patterns of Road Traffic Crash Black Spots and Emergency Facilities in Federal Capital City

This study aimed at determining the spatial patterns of Road Traffic Crash (RTC) black spots, Federal Road Safety Commission (FRSC) zebra points and emergency health care facilities in Federal Capital City (FCC). The aim was to provide stakeholders with information that will aid their understanding of accident prone locations and accessible rescue possibilities for accident victims on the roads in FCT. GPS Map 76S Mark (GARMIN) was used to locate and pick coordinates of data in the study area. A total of 16 possible emergency health care facilities, seventy (70) RTC black spots and Five Zebra point locations were obtained from FRSC. ArcGIS 10.0 was used to compute the data by plotting the coordinates to produce maps of the spatial relationship and to carry out Nearest Neighbour Analysis (NNA). The result was further used to determine the spatial patterns of RTC black spots as well as patterns of the emergency facilities. Generally, the result shows that the spatial trend is turning towards dispersion. However, there is less than 1% likelihood that the dispersed patterns could be the result of random chance. It was recommended that, the Federal Road Safety Commission should be staffed with trained professionals that can be responsible for accident data surveillance and analysis using geospatial techniques.

Migration Networks Pattern of China’s Floating Population from the Perspective of Complex Network

Since China’s reform and opening-up,the growing disparity between urban and rural areas and regions has led to massive migration.With China’s Rural Revitalization Strategy and the industrial transfer from the eastern coastal areas to the inland,the migration direction and pattern of the floating population have undergone certain changes.Using the 2017 China Migrants Dynamic Survey(CMDS),excluding Hong Kong,Macao,and Taiwan regions of China,organized by China’s National Health Commission,the relationship matrix of the floating population is constructed according to the inflow place of the interviewees and their outflow place(the location of the registered residence)in the questionnaire survey.We then apply the complex network model to analyze the migration direction and network pattern of China’s floating population from the city scale.The migration network shows an obvious hierarchical agglomeration.The first-,second-,third-and fourth-tier distribution cities are municipalities directly under the central government,provincial capital cities,major cities in the central and western regions and ordinary cities in all provinces,respectively.The migration trend is from the central and western regions to the eastern coastal areas.The migration network has‘small world’characteristics,forming nine communities.It shows that most node cities in the same community are closely linked and geographically close,indicating that the migration network of floating population is still affected by geographical proximity.Narrowing the urban-rural and regional differences will promote the rational distribution this population.It is necessary to strengthen the reform of the registered residence system,so that the floating population can enjoy urban public services comparable to other populations,and allow migrants to live and work in peace.

Spatial analysis of the osteoarthritis microenvironment: techniques, insights, and applications

Osteoarthritis(OA)is a debilitating degenerative disease affecting multiple joint tissues,including cartilage,bone,synovium,and adipose tissues.OA presents diverse clinical phenotypes and distinct molecular endotypes,including inflammatory,metabolic,mechanical,genetic,and synovial variants.Consequently,innovative technologies are needed to support the development of effective diagnostic and precision therapeutic approaches.Traditional analysis of bulk OA tissue extracts has limitations due to technical constraints,causing challenges in the differentiation between various physiological and pathological phenotypes in joint tissues.This issue has led to standardization difficulties and hindered the success of clinical trials.Gaining insights into the spatial variations of the cellular and molecular structures in OA tissues,encompassing DNA,RNA,metabolites,and proteins,as well as their chemical properties,elemental composition,and mechanical attributes,can contribute to a more comprehensive understanding of the disease subtypes.Spatially resolved biology enables biologists to investigate cells within the context of their tissue microenvironment,providing a more holistic view of cellular function.Recent advances in innovative spatial biology techniques now allow intact tissue sections to be examined using various-omics lenses,such as genomics,transcriptomics,proteomics,and metabolomics,with spatial data.This fusion of approaches provides researchers with critical insights into the molecular composition and functions of the cells and tissues at precise spatial coordinates.Furthermore,advanced imaging techniques,including high-resolution microscopy,hyperspectral imaging,and mass spectrometry imaging,enable the visualization and analysis of the spatial distribution of biomolecules,cells,and tissues.Linking these molecular imaging outputs to conventional tissue histology can facilitate a more comprehensive characterization of disease phenotypes.This review summarizes the recent advancements in the molecular imaging modalities and methodologies for in-depth spatial analysis.It explores their applications,challenges,and potential opportunities in the field of OA.Additionally,this review provides a perspective on the potential research directions for these contemporary approaches that can meet the requirements of clinical diagnoses and the establishment of therapeutic targets for OA.