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.

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.

基于 T(X )参与度的负co-location模式挖掘算法

空间co-location模式是一组在空间中频繁并置的空间特征的子集.负co-location模式从非频繁的空间co-location模式中产生.一般来说很难计算和挖掘频繁的负co-location模式.频繁负co-location模式中有较强的应用价值,如发现外来物种入侵,自然界植被生长规律等.现有对负co-location模式研究不全面且挖掘算法的数量屈指可数.针对该问题,提出了T(X)下的负co-location模式的参与度度量方法,并分析了此度量的合理性、可行性和简便性;其次,利用此度量,可以发现负模式中隐含的“团爆炸”现象,而之前的度量方式不能发现此现象.提出了基于T(X)参与度度量的负co-location模式挖掘算法.最后,实验结果表明,在其他条件不变的情况下,该算法可以挖掘数量更少且更具负相关性的频繁负co-location模式.

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 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.

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 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.

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.

Evaluation of COVID-19 Cases and Vaccinations in the State of Georgia, United States: A Spatial Perspective

This study evaluates the distribution of COVID-19 cases and mass vaccination campaigns from January 2020 to April 2023. There are over 235,000 COVID-19 cases and over 733,000 vaccinations across the 159 counties in the state of Georgia. Data on COVID-19 was acquired from usafact.org while the vaccination records were obtained from COVID-19 vaccination tracker. The spatial patterns across the counties were analyzed using spatial statistical techniques which include both global and local spatial autocorrelation. The study further evaluates the effect of vaccination and selected socio-economic predictors on COVID-19 cases across the study area. The result of hotspot analysis reveals that the epicenters of COVID-19 are distributed across Cobb, Fulton, Gwinnett, and DeKalb counties. It was also affirmed that the vaccination records followed the same pattern as COVID-19 cases’ epicenters. The result of the spatial error model performed well and accounted for a considerable percentage of the regression with an adjusted R squared of 0.68, Akaike Information Criterion (AIC) 387.682 and Breusch-Pagan of 9.8091. ESDA was employed to select the main explanatory variables. The selected variables include vaccination, population density, percentage of people that do not have health insurance, black race, Hispanic and these variables accounted for 68% of the number of COVID-19 cases in the state of Georgia during the study period. The study concludes that both COVID-19 cases and vaccinated individuals have spatial peculiarities across counties in Georgia state. Lastly, socio-economic variables and vaccination are very important to reduce the vulnerability of individuals to COVID-19 disease.

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.

Spatiotemporal Differentiation of Urban Spatial Form and Carbon Emissions in Poyang Lake City Group

In response to the inherent requirements of low-carbon land spatial planning in Jiangxi Province and the lack of existing research,this paper explored the mechanism of spatial form elements of Poyang Lake urban agglomeration on urban carbon emissions.Based on generalized linear regression and geographically weighted regression models,this paper analyzed the spatiotemporal distribution characteristics of carbon emissions,the spatiotemporal relationship between urban form index and carbon emissions,and the spatial differentiation of the intensity of dominant factors from 63 county-level administrative units in the Poyang Lake city group from 2005 to 2020.The results showed that:①The carbon emissions of urban agglomerations around Poyang Lake are generally increasing,and the spatial distribution of carbon emissions is characterized by high-value concentration in the middle and low-value agglomeration in pieces;②The main driving factor for the spatial heterogeneity of carbon emissions was the expansion of built-up area;③Improving urban compactness and optimizing urban form could effectively reduce urban carbon emissions.The results showcased the correlation between urban spatial landscape pattern and the spatiotemporal distribution of carbon emissions,which could make the low-carbon land spatial planning in the Poyang Lake city group more reasonable and practical.

基于指定特征的加权co-location模式挖掘方法

co-location模式是空间特征集的一个子集,其特征实例在地理空间中频繁出现互相近邻,基于特征参与率进行模式挖掘,特征参与率定义为模式表实例中不重复的实例个数与特征总实例数的比率。针对基于指定特征的模式特征实例并置程度满足模式指导性要求,但部分特征总实例数过多而导致特征参与率小于阈值,模式被界定为非频繁模式的情况,提出一种基于指定特征的加权co-location模式挖掘方法。定义特征的权以及特征加权参与率计算规则,可以有效挖掘基于指定特征的加权co-location模式,其加权参与度随着模式阶数的增大而单调递减。实验结果证明了该算法在挖掘结果及算法运行时间上的有效性。

含关键特征的显著Co-location模式挖掘研究

空间Co-location模式是一组在空间中频繁并置的空间特征的子集。空间Co-location模式挖掘通常假设空间实例之间相互独立,然而,在实际应用中,不同空间特征、不同实例之间往往相互作用或依赖。空间Co-location关键特征是指对模式具有主导作用的特征。在频繁模式中,识别含关键特征的Co-location模式并摘取模式中的关键特征,为用户提供更精简的挖掘结果,提高Co-location模式的可用性,对Co-location模式挖掘具有重要意义。本文首先定义了含有关键特征的显著频繁Co-location模式新概念,以及一系列度量指标以识别显著频繁Co-location模式中的关键特征;其次,给出了一个挖掘显著频繁Co-location模式和关键特征的算法;最后,在模拟和真实数据集上进行了大量的实验,验证了所提出算法的效果及性能。

一种基于U-AHC的不确定空间co-location模式挖掘算法

不确定数据在一些重要应用领域中是固有存在的,如传感器网络和移动物体追踪等.如何快速、方便、有效地从不确定数据库中发现潜在的、有价值的和人们感兴趣的信息变得越来越重要.首先,把传统的凝聚层次聚类算法(AHC)扩展到不确定的凝聚层次聚类算法(U-AHC),然后在聚类结果的基础上计算候选co-location模式的粗表实例,并对参与度小于最小参与度阈值的候选模式进行剪枝.接着展开其粗表实例并动态地实施剪枝,最后生成频繁的co-location模式.实验证明这个算法是正确的,而且效率较高.

模糊对象的空间Co-location模式挖掘研究

空间co-location模式表示的是空间对象的实例在一个相同的区域内频繁地空间并置.过去人们已经对确定及不确定数据的co-location模式挖掘问题进行了一些研究,但是针对模糊对象上进行的研究还没有.模糊对象在许多领域里都有着非常重要的应用,比如生物医学图像数据库和GIS.该文研究模糊对象的空间co-location模式挖掘问题.首先,定义模糊对象上空间co-location模式挖掘的相关概念,包括模糊参与率、模糊参与度等.其次,提出FB算法挖掘模糊对象的co-location模式.接着,提出了3种改进算法,包括剪枝对象、减少实例间连接、改进剪枝步,以提高挖掘性能、加快co-location规则的产生.最后通过大量的实验说明FB算法及其改进算法的效果和效率.

空间极大co-location模式挖掘研究

空间co-location模式代表了一组空间特征的子集,它们的实例在空间中频繁地关联。挖掘空间co-location模式的研究已经有很多,但是针对极大co-location模式挖掘的研究非常少。提出了一种新颖的空间极大co-location模式挖掘算法。首先扫描数据集得到二阶频繁模式,然后将二阶频繁模式转换为图,再通过极大团算法求解得到空间特征极大团,最后使用二阶频繁模式的表实例验证极大团得到空间极大co-location频繁模式。实验表明,该算法能够很好地挖掘空间极大co-location频繁模式。

基于凝聚层次聚类的co-location模式挖掘

空间的co-location模式代表一组空间对象的子集,它们的实例在空间中频繁地关联,它是空间数据挖掘的重要研究方向。本文首先介绍co-location模式挖掘的基本算法,然后提出一种新的挖掘算法,算法先对空间数据进行凝聚层次聚类,在聚类结果上挖掘co-location模式,最后对这种新的算法作实验评估。

基于加权欧氏距离的空间Co-location模式挖掘算法研究

空间Co_location模式挖掘关注空间对象实例在一定区域内同时出现的关系。目前大多数研究都是把空间对象的各个实例按同等权重对待,但现实中容易发现同一类对象的不同实例其大小规模、重要程度或是影响力覆盖范围都是不一样的。因此考虑空间对象实例的影响力因素,引入加权欧氏距离阈值参与计算,能发现更具实际价值的Co_location模式。

基于特征效用参与率的空间高效用co-location模式挖掘方法

空间co-location模式是指其实例在空间邻域内频繁一起出现的空间特征子集.与传统的空间co-location模式挖掘不同,在空间高效用co-location模式挖掘中,不再将参与度(PI)作为有趣模式的度量指标,而是将效用值作为挖掘有趣模式的兴趣度量指标.现有的空间高效用co-location模式挖掘方法分为特征带效用和实例带效用两类.特征带效用的现有方法没有考虑不同特征效用之间的差异,挖掘的结果往往包含了许多不尽合理的"高效用"模式;而实例带效用的现有方法,则考虑了不同特征对模式效用的影响,但没有客观地度量这种影响.该文提出了一种确定特征在模式中的效用权重ω(fi,c)的方法,定义了更为合理的空间高效用co-location模式概念,设计了一个有效的挖掘算法.大量的实验表明提出的高效用co-location模式度量方法和相应的挖掘算法能够处理特征效用差异性和特征间的相互影响问题,能更有效地挖掘到空间高效用co-location模式.

空间亚频繁co-location模式的主导特征挖掘

空间co-location模式是一组空间特征的子集,它们的实例在邻域内频繁并置出现。通常,空间co-location模式挖掘方法假设空间实例相互独立,并采用空间实例参与到模式实例的频繁性(参与率)来度量空间特征在模式中的重要性,采用空间特征的最小参与率(参与度)来度量模式的有趣程度,忽略了空间特征间的某些重要关系。因此为了揭示空间特征间的主导关系而提出主导特征co-location模式。现有主导特征模式挖掘方法是基于传统频繁模式及其团实例模型进行挖掘,然而,团实例模型可能会忽略非团的空间特征间的主导关系。因此,基于星型实例模型,研究空间亚频繁co-location模式的主导特征挖掘,以更好地揭示空间特征间的主导关系,挖掘更有价值的主导特征模式。首先,定义了两个度量特征主导性的指标;其次,设计了有效的主导特征co-location模式挖掘算法;最后,在合成数据集和真实数据集上通过大量实验验证了所提算法的有效性以及主导特征模式的实用性。