Point Pattern Analysis Method Research Articles

The clustering of knowledge-intensive business services in Greater Mexico City, 2010 through 2020

This article analyzes knowledge-intensive business services (KIBS) clustering and location patterns in Greater Mexico City. Although there is evidence of the importance of KIBS clustering as a factor that precedes innovation, no empirical work has applied point pattern analysis methods to identify intrametropolitan patterns. Little is known about KIBS firms clustering in Mexico and emerging economies in general. This study responds to both challenges, using the M and m functions, point pattern analysis methods that allow capturing concentration intensity and overdensity of same-type KIBS firms, respectively. Firm-level data are taken from Mexico’s National Statistics and Geography Institute’s (INEGI) open-source databases for 2010 and 2020. Results suggest different clustering patterns given our proposed KIBS classification. Overall, the clustering intensity of KIBS firms by class has increased during the analyzed period (2010–2020). Also, although central Greater Mexico City is the main clustering pole of attraction, urban subcenters display KIBS firms clustering depending on proposed KIBS classes. Clustering patterns are explained given existing intrametropolitan infrastructure and value-added differences, but also within- and between-class concentration variations. Despite the lack of firm-level economic data, results allow inferring possible agglomeration mechanisms behind clustering patterns.

Spatial distribution of porphyry copper deposits in Kerman Belt, Iran

The Kerman Cenozoic Magmatic Arc (KCMA, or Kerman Belt) in the southeastern part of the Urumieh–Dokhtar Magmatic Belt (UDMB) is considered as the most prospective geological province for porphyry copper deposits in Iran. Distance-based point pattern analysis methods, fractal analysis and Fry analysis are applied to porphyry copper deposits in order to gain insights into the geological controls over mineralization in the belt and suggest strategies for future exploration targeting. The results confirmed a significant structural control of strike-slip faults over the spatial distribution of deposits at the regional scale as previous structural studies defined it. The emplacement of porphyries in the Kerman Belt is likely controlled by extensional fault duplexes. The analyses further confirmed that there are two distinctly different sectors of porphyry copper mineralization in the Kerman Belt, namely, Sarduieh–Dahaj sector in the northwest and Jebal-e-Barez sector in the southeast as previous geological studies had demonstrated it. It is found that intrusive rock units, which form the permissive tracts for porphyry copper deposits in the Kerman Belt, are clustered, while the copper deposits within the permissive tracts generally show random distribution. It is recommended that predictive prospectivity modeling can be used to target mineralized intrusive bodies in the Kerman Belt; however direct detection techniques should be used to identify mineral deposits within the intrusive bodies.

Measuring the randomness of micro- and nanostructure spatial distributions: Effects of scanning electron microscope image processing and analysis.

The quantitative characterisation of the degree of randomness and aggregation of surface micro- and nanostructures is critical to evaluate their effects on targeted functionalities. To this end, the methods of point pattern analysis (PPA), largely used in ecology and medical imaging, seem to provide a powerful toolset. However, the application of these techniques requires the extraction of the point pattern of nanostructures from their microscope images. In this work, we address the issue of the impact that Scanning Electron Microscope (SEM) image processing may have on the fundamental metric of PPA, that is, the Nearest Neighbour Index (NNI). Using typical SEM images of polymer micro- and nanostructures taken from secondary and backscattered electrons, we report the effects of the (a) noise filtering and (b) binarisation threshold on the value of NNI as well as the impact of the image finite size effects. Based on these results, we draw conclusions for the safe choice of SEM settings to provide accurate measurement of nanostructure randomness through NNI estimation.

Spatial Distribution and Species Association of Dominant Tree Species in Huangguan Plot of Qinling Mountains, China

The spatial distribution pattern and population structure of trees are shaped by multiple processes, such as species characteristics, environmental factors, and intraspecific and interspecific interactions. Studying the spatial distribution patterns of species, species associations, and their relationships with environmental factors is conducive to uncovering the mechanisms of biodiversity maintenance and exploring the underlying ecological processes of community stability and succession. This study was conducted in a 25-ha Qinling Huangguan forest (warm-temperate, deciduous, broad-leaved) dynamic monitoring plot. We used univariate and bivariate g(r) functions of the point pattern analysis method to evaluate the spatial distribution patterns of dominant tree species within the community, and the intra- and interspecific associations among different life-history stages. Complete spatial randomness and heterogeneous Poisson were used to reveal the potential process of community construction. We also used Berman’s test to determine the effect of three topographic variables on the distribution of dominant species. The results indicated that all dominant species in this community showed small-scale aggregation distribution. When we excluded the influence of environmental heterogeneity, the degree of aggregation distribution of each dominant species tended to decrease, and the trees mainly showed random or uniform distribution. This showed that environmental heterogeneity significantly affects the spatial distribution of tree species. Dominant species mainly showed positive associations with one another among different life-history stages, while negative associations prevailed among different tree species. Furthermore, we found that the associations between species were characterized by interspecific competition. Berman’s test results under the assumption of complete spatial randomness showed that the distribution of each dominant species was mainly affected by slope and convexity.

Lockout, Lockdown and Land Use: Exploring the Spatio-Temporal Evolution Patterns of Licensed Venues in Sydney, Australia between 2012 and 2021 in the Context of NSW Public Policy

The hospitality industry in Sydney, Australia, has been subject to several regulatory interventions in the last decade, including lockout laws, COVID-19 lockdowns and land use planning restrictions. This study has sought to explore the spatial implications of these policies in Inner Sydney between 2012 to 2021. Methods based in spatial analysis were applied to a database of over 40,000 licensed venues. Point pattern analysis and spatial autocorrelation methods were used to identify spatially significant venue clusters. Space-time cube and emerging-hot-spot methods were used to explore clusters over time. The results indicate that most venues are located in the Sydney CBD on business-zoned land and show a high degree of spatial clustering. Spatio-temporal analysis reveals this clustering to be consistent over time, with variations between venue types. Venue numbers declined following the introduction of the lockout laws, with numbers steadily recovering in the following years. There was no discernible change in the number of venues following the COVID-19 lockdowns; however, economic data suggest that there has been a decline in revenue. Some venues were identified as having temporarily ceased trading, with these clustered in the Sydney CBD. The findings of this study provide a data-driven approach to assist policymakers and industry bodies in better understanding the spatial implications of policies targeting the hospitality sector and will assist with recovery following the COVID-19 pandemic. Further research utilising similar methods could assess the impacts of further COVID-19 lockdowns as experienced in Sydney in 2021.

Teachers' professional vision in action

Abstract. In need of simultaneously tackling various tasks at a fast pace, teaching is a job that requires skillful attention allocation. Selective visual attention forms the basis of teacher's professional vision – the expertise of attending to and interpreting classroom features, but it is also a process mostly hidden from direct observation. Eye tracking can capture this otherwise invisible attentional process and has long been used in demonstrating the visual expertise in various skill domains. Yet, the relationship between expertise and teachers' eye movements during real-life teaching remains a seldom explored area. The current study investigated the distinctive features of teachers' gaze in relation to their expertise levels. Specifically, eye movements were collected from 25 pairs of expert and novice teachers, with each pair teaching in the same classroom and with the same content. The eye movements were analyzed using scanpath comparison and point pattern analysis method. Results revealed that compared with novices, expert teachers had overall shorter fixation durations and larger quantity of fixations. They also had smaller proportion of fixations directed to objects irrelevant to teaching and the distribution of their fixations were wider. These results demonstrated that teachers had distinctive eye movement features in relation to their expertise levels. Most importantly, expert teachers exhibited better selective attention – a key component of professional vision. The implications regarding teacher education and instruction were also discussed.

Study on spatial layout optimization of Linpan settlements based on point pattern analysis

According to the problems of lack of planning and disordered layout of rural settlements in China, it is urgent to further improve the methods of optimizing the settlement pattern of towns and villages and the intensive use of land. The main purpose of this paper is to optimize the layout of rural settlements with a single land-use type and a large number of rural settlements to be relocated. This paper takes the Linpan settlements of Deyuan Town, Chengdu City, Sichuan Province as the research object and establishes the suitability evaluation system of Linpan settlements by the Entropy method. The Linpan settlements of research area are divided into the central and the non-central Linpan settlements and determine the search threshold according to the point pattern analysis method and multi-distance spatial cluster analysis. Through the study of regional preliminary central Linpan settlement selection and search threshold determination, central Linpan settlement site selection, the determination of non-central Linpan settlement’s migration direction, the results show that the radiation coverage rate of the central Linpan increased by 17.3%, the number of repeated radiation Linpans decreased by 63.2%, the overlapping area decreased by 49.9%, and finally 179 Linpans were optimized and merged into 36 central Linpans.

Spatial Patterns and Interspecific Associations During Natural Regeneration in Three Types of Secondary Forest in the Central Part of the Greater Khingan Mountains, Heilongjiang Province, China

Seedling and sapling spatial patterns are important in community regeneration, and understanding the natural regeneration mechanisms of tree species in relation to spatial patterns will help improve forest management and community restoration efficiency. Based on data from three fixed plots established in birch forests (BF), coniferous and broadleaved mixed forests (CBMF) and coniferous mixed forests (CMF) in the central Greater Khingan Mountains in Heilongjiang Province, China, in 2017, we used the univariate and bivariate O-ring functions of the point pattern analysis method to evaluate the spatial patterns and associations of the main tree species in these three forest types at different development stages and identified the community successional stages according to the interspecific associations between dominant tree species and other tree species. The results showed that Betula platyphylla and Larix gmelinii in BF exhibited identical spatial distribution patterns and had a tendency to transition from an aggregated to a random distribution from saplings to adult trees, whereas every tree type in CBMF generally showed a random distribution. Adult trees of the main tree species in CMF, i.e., L. gmelinii and Picea koraiensis, mainly showed a random distribution, but P. koraiensis at other size classes generally showed an aggregated distribution. The intraspecific associations of the main tree species in BF and CMF at different development stages were constrained by the spatial scale within a given scope, while those in CBMF at different development stages were not significantly constrained by spatial scale. The results also show that the density of the three forest types was affected by the distance between the individuals of the various tree classes and adult trees, with different levels of influence. We analyzed the interspecific associations between dominant tree species and other tree species and then assessed community succession progress and found that the BF and CMF exhibited medium-term community succession, while the CBMF was in the primary stage. Our results further show that the spatial distribution patterns of the tree species in the small-diameter classes were jointly affected by adjacent habitats and diffusional limitations and that scale dependence existed in the intraspecific and interspecific associations. The analysis of the natural regeneration of spatial distribution and interspecific associations represents an efficient way to explore the stability of forest communities and dynamic changes in interspecific relationships during succession. The study results thus provide a theoretical basis for developing rational forest management measures.

An interactive point pattern analysis web application and teaching exercise

ABSTRACTThis paper introduces a web-based, interactive point pattern analysis “game” that allows users to generate quickly and repeatedly a point pattern on screen and immediately learn whether the pattern is statistically different from random. It uses two point pattern analysis methods: quadrat analysis (QA) and nearest neighbor analysis (NNA). Studies have shown that gaming approaches, digital tools, and interactive instruction for teaching spatial thinking complement and enhance students’ geospatial skills. This game challenges students to achieve randomness, demonstrates the differences between QA and NNA, and easily permits in-class use by running in any web browser.

Quantitative division of succession and spatial patterns among different stand developmental stages in Changbai Mountains

Broadleaf-Korean pine forests exhibit high species richness and distinctive species composition, which are currently becoming more dominant among natural forests in Changbai Mountains of north-eastern China. Understanding the ecological process of restored vegetation is quite important for ecosystem reconstruction. Distinguishing stand development stages and analyzing the dynamic spatial patterns could provide insights into significant community coexistence mechanisms. In the present study, eight permanent study areas were established according to the substituting space for time method in Changbai Mountains of north-eastern China. The optimal division method was used to quantify the successional series into different stand development stages, and the point pattern analysis method (L(r) function) was used to analyse the dynamic changes in spatial patterns and interspecific associations. Our results suggested that: (1) The stand development process was divided into five stages: the first three stages were poplar-birch secondary forests in different stages of recovery, the fourth stage was the spruce-fir mixed forest, and the last stage was the primary broadleaf-Korean pine forest; (2) The spatial pattern showed an aggregated distribution at a small scale and changed to a random distribution as the scale increased in poplar-birch secondary forests, but the spatial pattern appeared to be randomly distributed in spruce-fir mixed forest and broadleaf-Korean pine forest; (3) The interspecific associations between pioneer species and climax species changed from negative to positive among the different stand stages, and environmental resources were shared among these species. Interspecific differences in shade tolerance among the tree species were key determinants of forest dynamics and structure. Our study is vital to the understanding of the forest development; thus, the spatial change features should receive greater attention when forest management is being planned and restoration strategies are being developed for the Changbai Mountains.

Spatial pattern and regeneration characteristics of main woody species in natural secondary forest in Maoershan, Northeast China.

Based on the survey data from a 1 hm2 plot of the second forest in Maoershan, we divided the stand into four size degrees, including seedlings (tree height H<30 cm), saplings (H 30-130 cm or H>130 cm meanwhile DBH< 5 cm), medium trees (DBH 5-10 cm) and big trees (DBH≥10 cm). The spatial pattern and spatial association of all woody plants and three dominant tree species in four size degrees, as well as the basic forest regeneration, were chosen as study objects, to explore the renewal trend and provide theoretical basis for the sustainable forest management. The O-ring point pattern analysis method was used to analyze the spatial distribution and spatial association. The dominant trees were planted Pinus koraiensis and three common broadleaved tree species (Fraxinus mandshurica, Juglans mandshurica, Phellodendron amurense), and Ulmus pumila. The results showed that the regeneration of the stand was quite common, with 2894 seedlings·hm-2 and 3337 saplings·hm-2 . For the whole forest, the seedlings in the stand were positively associated with large trees and medium trees and saplings respectively at the scales of 0-25 m, 0-15 m and 0-40 m, and negatively associated at the subsequent scales. The saplings were positively associated with large trees and medium trees at 0-15 m, 0-25 m respectively, and had no significant association at the subsequent scales. Medium trees showed no significant association with big trees at all the scales. For the dominant trees, the saplings and seedlings of Pinus koraiensis showed negative association at the scale of 0-40 m, while the saplings and seedlings of the three common broadleaved tree species or Ulmus pumila were mainly non-associated. There was a negative association between the saplings of Pinus koraiensis and each size degree of the three common broadleaved tree species or Ulmus pumila at most scale, whereas saplings of the three common broadleaved tree species and Ulmus pumila showed a negative association with non-conspecies only at 0-20 m scale. In the medium and large size degrees, there was no significant association between each two of the three dominant tree species at the scale above 20 m. The secondary forest in Maoershan is still under the process of succession. During the process of forest resource management, measures should be taken to promote the renewal, and the unreasonable spatial pattern should be adjusted in time.

Analysis of Spatial Characteristics of Digital Signage in Beijing with Multi-Source Data

Digital signage is an important medium for urban outdoor advertising. Understanding the spatial distribution characteristics and factors that influence the site of digital signage are conducive to the efficient, standardized, and sustainable development of digital signage. The outdoor commercial digital signage within the Sixth Ring Road in Beijing is taken as the research object, and social network check-ins, housing prices, traffic network centrality and the mount of commercial facilities are considered factors that influence digital signage. The spatial distribution characteristics of digital signage are studied by using point pattern analysis methods. Moreover, we use three spatial clustering algorithms to study the hierarchical spatial characteristics of digital signage and test the effectiveness of the results. In addition, the factors that influence the distribution of digital signage are analyzed by Spearman correlation analysis. The results indicate that (1) the digital signage in Beijing generally presents a relatively concentrated distribution with centrality and forms an obvious gathering area and the agglomeration centers are mainly concentrated in the core parts of the central business district (CBD). (2) Digital signage is categorized into three groups, the traffic-oriented, the population-oriented, and the market-oriented. In addition, the spatial distribution of digital signage is consistent with the historical urban development of Beijing. (3) The social network check-ins with dynamic population characteristics had the highest correlation with the operation cost of digital signage. The spatial characteristics of digital signage evaluated in this study can effectively enhance the sustainable management of digital signage and provide a reference for research of the sustainable allocation of digital signage resources.

Community structure characteristics and spatial distribution of dominant species of secondary Quercus mongolica forest in Changbai Mountains, China.

Understanding forest community structure is the basis for revealing community maintenance mechanism and succession dynamics, and the premise forest management activities. Taking two permanent 1-hm2 plots of Quercus mongolica broadleaved mixed forest located in Wangqing Fore-st Bureau in Jilin Province as objects, we analyzed the community structure characteristics of secondary Q. mongolica forest and spatial distribution of dominant species with the point pattern analysis method (the O-ring statistics). The results showed that both plots were dominated by Q. mongo-lica with distinct hierarchy character. The co-dominated trees in plot I were Populus ussuriensis, Be-tula platyphylla, and Pinus koraiensis, which were different from plot II (Tilia amurensis, Acer mono, and Pinus koraiensis). The richness and Shannon index of plot I were higher than that of plot II. The DBH class distribution of trees in both plots were reverse-J-shaped. Individuals of Q. mongolica exhibited a normal distribution and P. koraiensis showed a reverse-J-shaped. There were differences in the diameter structure of other co-dominant tree species. The spatial distribution of Q. mongolica in two plots was aggregation distribution at small scale and random distribution in medium and large scales. P. koraiensis showed aggregation-random distribution at 0-50 m scale, while its aggregation degree in plot I were higher than that of plot II. B. platyphylla and P. ussuriensis in plot I were aggregated at the scale of 0-17 m, and the aggregation intensity was significantly higher than other tree species, and showed random distribution and uniform-random distribution at the scale of 18-50 m, respectively. Random or uniform distribution at the medium-large scale, and aggregate distribution at small scale of T. amurensis were observed in plot II. These results demonstrated that both plots were at the primary stage of succession with different growth stages. The succession stage of plot II was more progressed than that of plot I and the community of plot II was relatively more stable. Our results provide references for the precise management of Q. mongolica secondary forests at different developmental stages.

The effect of user-defined parameters on DTM accuracy—development of a hybrid model

User-defined parameters (point density, interpolation, and pixel size) have a great influence on the accuracy of digital terrain model (DTM). Therefore, the optimal interpolation method (IM) and appropriate pixel size should be used to create a continuous surface. Pixel size or spatial resolution tends to be a compromise between the number of samples and the size of the study area, whereby input dataset is often devaluated. In this paper, the authors propose a new methodological approach to DTM production (hybrid DTM—HDTM) from an aero-photogrammetry dataset. Two different approaches to DTM production are presented: the usual (UDTM) and the hybrid (HDTM). HDTM is based on restructuring and refining the input dataset by generating contour lines, determining the optimal interpolation method and selecting the appropriate spatial resolution. The goal is to develop a qualitative DTM while minimalizing the devaluation of input data and error propagation. The accuracy of different IMs for UDTM was examined in a comparative analysis of six statistical parameters by applying cross-validation, with the focus on the root mean square error (RMSE) parameter. Four different methods of selecting optimal spatial resolution were tested for the same model. The UDTM and HDTM generated were compared by interior, exterior, and visual accuracy assessment and by the performance success of specific hydrological parameters: (1) flow accumulation—by applying a developed DNA concept and (2) watershed—by calculating volume and 3D surface. The reference value for exterior accuracy assessment was high-resolution airborne LiDAR DTM (LDTM). It was found that ordinary kriging (OK) was the best IM (RMSE, 1.9893 m). The spatial resolution of UDTM was calculated by combining two variants of the point pattern analysis method and was found to be 19 m. On the other hand, the complexity of terrain method was used to define the spatial resolution of HDTM (3 m). HDTM achieved better results than UDTM in all aspects of accuracy assessment. The exterior accuracy of HDTM was better by 1.483 m (RMSE). Finally, the results of the applied DNA concept showed that a stream generated from HDTM had a 2.587 m lower horizontal root mean square error (HRMSE) than the stream generated from UDTM.

Density-dependent and distance-dependent effects in a 60-ha tropical mountain rain forest in the Jianfengling mountains, Hainan Island, China: Spatial pattern analysis

To understand the density-dependent and distance-dependent effects on the spatial patterns of trees in tropical mountain rain forest, we used the recent developed O-ring statistic, which is a point pattern analysis method, to explore spatial distribution patterns and spatial associations of trees of different size-classes in a 60-ha tropical mountain rain forest plot in the Jianfengling Nature Reserve, Hainan Island, China. We describe four main findings. (1) As the size-classes of live trees increased, the live trees became more regular. (2) Densities of saplings, small trees, and dead trees tended to decrease with increasing distances, while densities of medium trees, big trees, and old-growth trees initially increased and then decreased at greater distances, showing a skewed inverted V-shaped pattern. (3) Densities of saplings, small trees, and medium trees first increased rapidly from 0 m to 5 m to 7 m radius (distance from old-growth trees) and then decreased continuously away from old-growth trees, showing consistent patterns expect from the distance-dependent effect. (4) Mortality was non-random, and dead trees were significantly aggregated with live trees at scales <2 m, showing clustering of dead (or surviving) trees and clear density dependence where stems with more neighbors had a higher risk of mortality. We concluded that size and spacing effects play important roles in determining spatial distribution patterns of the trees in the Jianfengling tropical mountain rain forest plot.

Anomaly behaviour detection based on the meta-Morisita index for large scale spatio-temporal data set

In this paper, we propose a framework for processing and analysing large-scale spatio-temporal data that uses a battery of machine learning methods based on a meta-data representation of point patterns. Existing spatio-temporal analysis methods do not include a specific mechanism for analysing meta-data (point pattern information). In this work, we extend a spatial point pattern analysis method (the Morisita index) with meta-data analysis, which includes anomaly behaviour detection and unsupervised learning to support spatio-temporal data analysis and demonstrate its practical use. The resulting framework is robust and has the capability to detect anomalies among large-scale spatio-temporal data using meta-data based on point pattern analysis. It returns visualized reports to end users.

Cluster Detection of Non‐Metallic Inclusions in 42CrMo4 Steel

Recent progress in steel casting shows significant reduction of non‐metallic inclusions (NMIs). Unfortunately, total NMI volume reduction due to refining can surprisingly lead to NMI size enlargement or cluster formation. This effect decreases the fatigue endurance limit of steel, which is sensitive not only to the presence of large NMIs, but also to clusters of small inclusions, which are usually overlooked in standard metallography inspection. In the present paper, the authors aim to investigate clustering properties of NMIs in a dataset of 42CrMo4 steel, which are observed on fracture surfaces after fatigue tests and from inclusion patterns in polished cross‐sections after metallographic inspection. The authors use both data types for estimating critical distances as main cluster size parameters and find excellent agreement between the results of both methods. The clusters are determined by a classical statistical method of cluster analysis, called “agglomerative hierarchical clustering”. The authors show by means of methods of spatial point pattern analysis that their method yields clusters that are more plausible than clusters resulting from the well‐known Murakami “thumbnail rule”.

Incorporating Temporal and Spatial Variations of Groundwater into the Construction of a Water-Based Ecological Network: A Case Study in Denko County

It is of great practical significance to construct a water-based ecological network in arid and semi-arid areas. The spatial distribution of water resources is one of the most important factors in determining the ecological stability of such areas. In this study, groundwater level trends were analyzed with a model called Empirical Mode Decomposition (EMD). The temporal and spatial evolution of groundwater depth data from 1990 to 2016 were analyzed. The surface water bodies were analyzed using a point pattern analysis method. Based on this, a water-based ecological network was constructed with a minimum cumulative resistance surface model. The study indicated that the trend lines for the groundwater tables of 17 wells could be divided into five types in Denko County. The landscape types that changed from a desert landscape to an oasis landscape had a positive impact on groundwater. Precipitation trend was related to the spatial distribution of the groundwater depth, and the spatial pattern of the water nodes was characterized by a small-scale highly aggregated distribution and a large-scale uniform distribution in Denko County. These results suggest that for the stability of arid and semi-arid ecological environments, the appropriate human intervention (such as construction of an artificial oasis) is of great significance. Based on the analysis of groundwater and surface water bodies, a water-based ecological network in Denko County, which consisted of 391 ecological sources and 7360 ecological corridors, was constructed in 2016. The water-based ecological network constructed in this study was more sustainable and stable, and also suitable for arid and semi-arid areas, which is of great practical significance and application value.

Spatial distribution patterns of Symplocos congeners in a subtropical evergreen broad-leaf forest of southern China

Studies on spatial distribution of congeneric species can supplement our understanding of species ecological processes. We analyzed population structure, spatial distribution, intra- and interspecific associations among six Symplocos tree species on a large (50 ha) plot in a subtropical, evergreen broad-leaf forest in southern China using spatial point pattern analysis methods. Our results suggested that the six Symplocos tree species were all clustered at small scales. The aggregation intensity of S. wikstroemiifolia with low abundance but relatively numerous large-diameter trees was much higher than that of the other five Symplocos species that occurred at high abundance but included few large-diameter trees. Spatial associations among the six congeners showed that 12 of 30 pairs were associated positively at small scales, and 13 of 30 pairs were unrelated. For species among different size classes, 79 of 120 pairs were not correlated, and 17 of 120 pairs were associated positively. These results showed insufficient evidence for interspecific competition and congeneric Symplocos species commonly coexist within subtropical plant communities. The spatial patterns of Symplocos species and their correlations changed with size (DBH) class and were simultaneously affected by spatial scales; the intensity of their aggregation and association decreased with increasing area of the sample plot.

Software Application Development for Data Spatial Analysis on Network Spaces

Although the point patterns analysis is not a new area of study, it maintains its validity by allowing the analysis of different kinds of phenomena from a spatial perspective through various methods. Among the existing point pattern analysis methods there are some focused on the analysis on networks spaces, defined as linear subspaces contained within the two-dimensional Euclidean spaces. An example of network space is the one composed by the road network within a city or region. Over these network spaces there can be made analysis of phenomena such as traffic accidents and crimes. The present work focuses on the development of a software solution that implements two of these methods, the network K function and NetKDE, and its application on traffic accidents occurring during 2014 in the cities of Bucaramanga (Colombia) and Barcelona (Spain).