Coupling Relationship between Commercial Spatial Structure and Population Based on the Point Pattern Analysis and Coupling Model: A Case Study in Beijing

Abstract There is some evidence to suggest that the etiology of Hodgkin's disease includes an infectious component One approach to investigating this possibility is a formal assessment of the geographic and temporal variation in the incidence of the disease. The present study was designed to better evaluate space-time patterns of residence prior to diagnosis of persons with Hodgkln's disease. Lifetime residential histories were obtained from 279 incident cases of Hodgkin's disease diagnosed between 1974 and 1982 in a defined population in northwestern Washington State, and a similar number of population controls matched to the cases by age, sex, and socioeconomic status. A method of point pattern analysis was modified for use in this context to compare the spatial proximity of case residences to that expected under the null assumption of no difference between the spatial pattern of case and control residences. Analyses were applied within temporal groupings of residences specified a priori assuming 1) no specified latency, 2) four different 5-year latent intervals, and 3) three age at exposure intervals. Assuming no latency, there is no evidence that case residences are more or less clustered than would be expected by chance. Assuming latent intervals of up to 15 years prior to diagnosis, case residences are less clustered than expected, particularly among those who developed Hodgkin's disease after the age of 40 years. In contrast, there is suggestive evidence that persons diagnosed with Hodgkin's disease after age 40 years lived closer together than expected as young children and teenagers. These results illustrate the need to focus such analyses on specific time intervals defined a priori to most likely represent periods of greatest etiologic relevance. To the extent that these findings are not the result of some unknown artifact of the method itself, they may serve to focus additional attention on childhood environment as a particularly important period in the etiology of this disease. Furthermore, the analytic method employed may be useful in identifying space-time clustering using population-based data in other etiologic settings

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Investigating spatial structure in specific tree species in ancient semi-natural woodland using remote sensing and marked point pattern analysis

Ecography ◽

10.1111/j.0906-7590.2007.04726.x ◽

2007 ◽

Vol 30 (1) ◽

pp. 88-104 ◽

Cited By ~ 14

Author(s):

Peter M. Atkinson ◽

Giles M. Foody ◽

Peter W. Gething ◽

Ajay Mathur ◽

Colleen K. Kelly

Keyword(s):

Remote Sensing ◽

Spatial Structure ◽

Tree Species ◽

Pattern Analysis ◽

Marked Point ◽

Point Pattern Analysis ◽

Point Pattern

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Point Pattern Analysis as Tool for Digital Geoarchaeology: A Case Study of Megalithic Graves in Schleswig-Holstein, Germany

Natural Science in Archaeology - Digital Geoarchaeology ◽

10.1007/978-3-319-25316-9_4 ◽

2017 ◽

pp. 45-64 ◽

Cited By ~ 3

Author(s):

Daniel Knitter ◽

Oliver Nakoinz

Keyword(s):

Pattern Analysis ◽

Point Pattern Analysis ◽

Point Pattern

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Point Pattern Analysis as Tool for Digital Geoarchaeology – A Case Study of Megalithic Graves in Schleswig-Holstein, Germany

10.31235/osf.io/vh5g2 ◽

2018 ◽

Author(s):

Daniel Knitter ◽

Oliver Nakoinz

Keyword(s):

Open Source Software ◽

Pattern Analysis ◽

Nearest Neighbor ◽

Specific Pattern ◽

Point Pattern Analysis ◽

Point Pattern ◽

Order Effects ◽

First Order ◽

Digital Elevation

In this contribution we apply different methods of spatial and geomorphometric analysis in order to present a general approach of data exploration in areas where detailed local information is absent. Our data are based on locations of megalithic graves from Funnel Beaker societies (3700-2800 BCE) in the area of Schleswig-Holstein, Germany. Using these locations, we apply methods of point pattern analysis in order to reconstruct the spatial processes that created the sample: We use density based measures to show the influence of first order effects on the dataset. While first order effects are related to the underlying areal characteristics of the point locations and hence are determinant of their intensity, second-order effects are the result of interactions between points. We conduct distance related approaches, e.g.\ focusing on nearest neighbor characteristics, in order to investigate the interaction between the points. The point pattern analyses is complemented by integrating geomorphometric measures that are indirectly indicative for some general environmental conditions, even in prehistoric times. This helps (a) to relate first-order effects to societal or environmental features and (b) to understand the specific pattern of interactions between the points. The necessary raw data in form of digital elevation models are freely available for large parts of the globe. All analyses are conducted using free and open source software in order to provide their limitless application.

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Spatial Point Pattern Analysis of Human Settlements and Geographical Associations in Eastern Coastal China — A Case Study

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph110302818 ◽

2014 ◽

Vol 11 (3) ◽

pp. 2818-2833 ◽

Cited By ~ 18

Author(s):

Zhonghao Zhang ◽

Rui Xiao ◽

Ashton Shortridge ◽

Jiaping Wu

Keyword(s):

Pattern Analysis ◽

Point Pattern Analysis ◽

Point Pattern ◽

Spatial Point Pattern ◽

Human Settlements ◽

Spatial Point ◽

Spatial Point Pattern Analysis ◽

Coastal China

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Spatial point-pattern analysis as a powerful tool in identifying pattern-process relationships in plant ecology: an updated review

Ecological Processes ◽

10.1186/s13717-021-00314-4 ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Mariem Ben-Said

Keyword(s):

Spatial Patterns ◽

Pattern Analysis ◽

Biotic Interactions ◽

Plant Ecology ◽

Point Pattern Analysis ◽

Point Pattern ◽

Summary Statistics ◽

Spatial Point Pattern ◽

Spatial Point ◽

Spatial Point Pattern Analysis

Abstract Background Ecological processes such as seedling establishment, biotic interactions, and mortality can leave footprints on species spatial structure that can be detectable through spatial point-pattern analysis (SPPA). Being widely used in plant ecology, SPPA is increasingly carried out to describe biotic interactions and interpret pattern-process relationships. However, some aspects are still subjected to a non-negligible debate such as required sample size (in terms of the number of points and plot area), the link between the low number of points and frequently observed random (or independent) patterns, and relating patterns to processes. In this paper, an overview of SPPA is given based on rich and updated literature providing guidance for ecologists (especially beginners) on summary statistics, uni-/bi-/multivariate analysis, unmarked/marked analysis, types of marks, etc. Some ambiguities in SPPA are also discussed. Results SPPA has a long history in plant ecology and is based on a large set of summary statistics aiming to describe species spatial patterns. Several mechanisms known to be responsible for species spatial patterns are actually investigated in different biomes and for different species. Natural processes, plant environmental conditions, and human intervention are interrelated and are key drivers of plant spatial distribution. In spite of being not recommended, small sample sizes are more common in SPPA. In some areas, periodic forest inventories and permanent plots are scarce although they are key tools for spatial data availability and plant dynamic monitoring. Conclusion The spatial position of plants is an interesting source of information that helps to make hypotheses about processes responsible for plant spatial structures. Despite the continuous progress of SPPA, some ambiguities require further clarifications.

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