scholarly journals Does fluvial channel-belt clustering predict net sand to gross rock volume? Architectural metrics and point-pattern analysis of a digital outcrop model

2019 ◽  
Vol 89 (11) ◽  
pp. 1109-1126
Author(s):  
Alexander R. Koch ◽  
Cari L. Johnson ◽  
Lisa Stright
Keyword(s):  
Large Scale ◽  
Small Scale ◽  
Point Pattern ◽  
Point Patterns ◽  
Spatial Point ◽  
Channel Belt ◽  
John Henry Member ◽  
Scale Point ◽  
Belt Width ◽  
Digital Outcrop

ABSTRACT Spatial point-pattern analyses (PPAs) are used to quantify clustering, randomness, and uniformity of the distribution of channel belts in fluvial strata. Point patterns may reflect end-member fluvial architecture, e.g., uniform compensational stacking and avulsion-generated clustering, which may change laterally, especially at greater scales. To investigate spatial and temporal changes in fluvial systems, we performed PPA and architectural analyses on extensive outcrops of the Cretaceous John Henry Member of the Straight Cliffs Formation in southern Utah, USA. Digital outcrop models (DOMs) produced using unmanned aircraft system-based stereophotogrammetry form the basis of detailed interpretations of a 250-m-thick fluvial succession over a total outcrop length of 4.5 km. The outcrops are oriented roughly perpendicular to fluvial transport direction. This transverse cross-sectional exposure of the fluvial system allows a study of the system's variation along depositional strike. We developed a workflow that examines spatial point patterns using the quadrat method, and architectural metrics such as net sand to gross rock volume (NTG), amalgamation index, and channel-belt width and thickness within moving windows. Quadrat cell sizes that are ∼ 50% of the average channel-belt width-to-thickness ratio (16:1 aspect ratio) provide an optimized scale to investigate laterally elongate distributions of fluvial-channel-belt centroids. Large-scale quadrat point patterns were recognized using an array of four quadrat cells, each with 237× greater area than the median channel belt. Large-scale point patterns and NTG correlate negatively, which is a result of using centroid-based PPA on a dataset with disparately sized channel belts. Small-scale quadrat point patterns were recognized using an array of 16 quadrat cells, each with 21× greater area than the median channel belt. Small-scale point patterns and NTG correlate positively, and match previously observed stratigraphic trends in the fluvial John Henry Member, suggesting that these are regional trends. There are deviations from these trends in architectural statistics over small distances (hundreds of meters) which are interpreted to reflect autogenic avulsion processes. Small-scale autogenic processes result in architecture that is difficult to correlate between 1D datasets, for example when characterizing a reservoir using well logs. We show that 1D NTG provides the most accurate prediction for surrounding 2D architecture.

scholarly journals Spatial micro-distribution of shoots in Posidonia oceanica (L.) Delile meadows

2015 ◽  
Author(s):  
Tiziano Bacci ◽  
Sante Francesco Rende ◽  
Domenico Rocca ◽  
Michele Scardi
Keyword(s):  
Spatial Distribution ◽  
Posidonia Oceanica ◽  
Small Scale ◽  
Nearest Neighbour ◽  
Data Set ◽  
Spatial Indices ◽  
Neighbour Distance ◽  
Point Patterns ◽  
Spatial Point ◽  
Spatial Point Patterns

Posidonia oceanica meadows contain huge numbers of shoots and their dynamics are strictly related to spatial distribution patterns of those shoots. In order to investigate the structure of P. oceanica meadows at very small spatial scale (i.e. in the 1 cm 2 -1 m 2 range), patterns in shoot distribution were analyzed. Spatial distribution of shoots was recorded by cutting all the leaves and by digitizing shoot location from images of 10 square frames (1 m 2 ), sampled in seemingly uniformly dense meadows at two sites in Southern Italy. Spatial point patterns have been explored testing the sensitivity and robustness through different spatial indices, based on i) nearest neighbour analysis, ii) quadrat counts analysis, iii) fractal dimension. Clark & Evans nearest neighbour distance index has been proved to be the most suitable for aim of the work and it has been selected for the further analysis. Data analysis of the 10 square frames (1 m 2 ) highlighted regular spatial point patterns (R>1; p<0.0001) in most cases (8 frames), while aggregated (R<1; p<0.01) and random (R=1) spatial point patterns were rare. In addition, mean value of nearest neighbour distance of shoots in each square frame analyzed has been shown to be always close to 2 cm (min: 1.73 cm; max: 2.21 cm). The potential implications of this type of data set were highlighted. Both nearest neighbour distance of shoots and spatial point pattern typology (aggregated, random or regular) could provide useful and integrative information for the study of P. oceanica macrostructure (e.g. implementation of shoot growth models, development of new descriptors). The raw data, provided by the authors as supplementary material, are currently the first and the only information available about shoot spatial micro-distribution. In this regard, although our data set cannot represent the whole spectrum of variability in P. oceanica meadows, it certainly shed some light on the small scale patterns of P. oceanica meadows and it prompts us many questions, some of which are still unanswered.

Testing the Stability of Crime Patterns: Implications for Theory and Policy

2010 ◽  
Vol 48 (1) ◽  
pp. 58-82 ◽  
Author(s):  
Martin A. Andresen ◽  
Nicolas Malleson
Keyword(s):  
Spatial Scales ◽  
Theoretical Development ◽  
Point Pattern ◽  
Crime Patterns ◽  
Point Patterns ◽  
Spatial Point ◽  
Spatial Point Patterns ◽  
Spatial Criminology ◽  
The Stability ◽  
Street Segments

Recent research in the ‘‘crime at places’’ literature is concerned with smaller units of analysis than conventional spatial criminology. An important issue is whether the spatial patterns observed in conventional spatial criminology focused on neighborhoods remain when the analysis shifts to street segments. In this article, the authors use a new spatial point pattern test that identifies the similarity in spatial point patterns. This test is local in nature such that the output can be mapped showing where differences are present. Using this test, the authors investigate the stability of crime patterns moving from census tracts to dissemination areas to street segments. The authors find that general crime patterns are somewhat similar at all spatial scales, but finer scales of analysis reveal significant variations within larger units. This result demonstrates the importance of analyzing crime patterns at small scales and has important implications for further theoretical development and policy implementation.

scholarly journals EXPLORATORY SPECTRAL ANALYSIS IN THREE-DIMENSIONAL SPATIAL POINT PATTERNS

2021 ◽  
Vol 39 (1) ◽  
pp. 177
Author(s):  
Edmary Silveira Barreto ARAÚJO ◽  
João Domingos SCALON ◽  
Lurimar Smera BATISTA
Keyword(s):  
Spectral Analysis ◽  
Point Processes ◽  
Three Dimensional ◽  
Dependence Structure ◽  
Point Pattern ◽  
Spatial Point Pattern ◽  
Space Domain ◽  
Point Patterns ◽  
Spatial Point ◽  
Spatial Point Patterns

A spatial point pattern is a collection of points irregularly located within a bounded area (2D) or space (3D) that have been generated by some form of stochastic mechanism. Examples of point patterns include locations of trees in a forest, of cases of a disease in a region, or of particles in a microscopic section of a composite material. Spatial Point pattern analysis is used mostly to determine the absence (completely spatial randomness) or presence (regularity and clustering) of spatial dependence structure of the locations. Methods based on the space domain are widely used for this purpose, while methods conducted in the frequency domain (spectral analysis) are still unknown to most researchers. Spectral analysis is a powerful tool to investigate spatial point patterns, since it does not assume any structural characteristics of the data (ex. isotropy), and uses only the autocovariance function, and its Fourier transform. There are some methods based on the spectral frameworks for analyzing 2D spatial point patterns. There is no such methods available for the 3D situation and, therefore, the aim of this work is to develop new methods based on spectral framework for the analysis of three-dimensional point patterns. The emphasis is on relating periodogram structure to the type of stochastic process which could have generated a 3D observed pattern. The results show that the methods based on spectral analysis developed in this work are able to identify patterns of three typical three-dimensional point processes, and can be used, concurrently, with analyzes in the space domain for a better characterization of spatial point patterns.

Exploratory Point Pattern Analysis for Modeling Biological Data

2013 ◽  
Vol 2 (1) ◽  
pp. 1-13
Author(s):  
Stelios Zimeras
Keyword(s):  
Biological Process ◽  
Biological Data ◽  
Point Pattern ◽  
Data Sets ◽  
Similar Data ◽  
Point Patterns ◽  
Spatial Point ◽  
Spatial Point Patterns ◽  
Statistical Measures ◽  
Physical Scale

Data in the form of sets of points, irregular distributed in a region of space could be identified in varies biological applications for examples the cell nuclei in a microscope section of tissue. These kinds of data sets are defined as spatial point patterns and the presentation of the positions in the space are defined as points. The spatial pattern generated by a biological process, can be affected by the physical scale on which the process is observed. With these spatial maps, the biologists will usually want a detailed description of the observed patterns. One way to achieve this is by forming a parametric stochastic model and fitting it to the data. The estimated values of the parameters could be used to compare similar data sets providing statistical measures for fitting models. Also a fitted model can provide an explanation of the biological processes. Model fitting especially for large data sets is difficult. For that reason, statistical methods can apply with main purpose to formulate a hypothesis for the implementation of biological process. Spatial statistics could be implemented using advance statistical techniques that explicitly analyses and simulates point structures data sets. Typically spatial point patterns are data that explain the location of point events. The author’s interest is the investigation of the significance of these patterns. In this work, an investigation of biological spatial data is analyzed, using advance statistical modeling techniques like kriging.

scholarly journals Spatial micro-distribution of shoots in Posidonia oceanica (L.) Delile meadows

2015 ◽  
Author(s):  
Tiziano Bacci ◽  
Sante Francesco Rende ◽  
Domenico Rocca ◽  
Michele Scardi
Keyword(s):  
Spatial Distribution ◽  
Posidonia Oceanica ◽  
Small Scale ◽  
Nearest Neighbour ◽  
Data Set ◽  
Spatial Indices ◽  
Neighbour Distance ◽  
Point Patterns ◽  
Spatial Point ◽  
Spatial Point Patterns

Posidonia oceanica meadows contain huge numbers of shoots and their dynamics are strictly related to spatial distribution patterns of those shoots. In order to investigate the structure of P. oceanica meadows at very small spatial scale (i.e. in the 1 cm 2 -1 m 2 range), patterns in shoot distribution were analyzed. Spatial distribution of shoots was recorded by cutting all the leaves and by digitizing shoot location from images of 10 square frames (1 m 2 ), sampled in seemingly uniformly dense meadows at two sites in Southern Italy. Spatial point patterns have been explored testing the sensitivity and robustness through different spatial indices, based on i) nearest neighbour analysis, ii) quadrat counts analysis, iii) fractal dimension. Clark & Evans nearest neighbour distance index has been proved to be the most suitable for aim of the work and it has been selected for the further analysis. Data analysis of the 10 square frames (1 m 2 ) highlighted regular spatial point patterns (R>1; p<0.0001) in most cases (8 frames), while aggregated (R<1; p<0.01) and random (R=1) spatial point patterns were rare. In addition, mean value of nearest neighbour distance of shoots in each square frame analyzed has been shown to be always close to 2 cm (min: 1.73 cm; max: 2.21 cm). The potential implications of this type of data set were highlighted. Both nearest neighbour distance of shoots and spatial point pattern typology (aggregated, random or regular) could provide useful and integrative information for the study of P. oceanica macrostructure (e.g. implementation of shoot growth models, development of new descriptors). The raw data, provided by the authors as supplementary material, are currently the first and the only information available about shoot spatial micro-distribution. In this regard, although our data set cannot represent the whole spectrum of variability in P. oceanica meadows, it certainly shed some light on the small scale patterns of P. oceanica meadows and it prompts us many questions, some of which are still unanswered.

Small-Scale Science to Large-Scale Profession

2000 ◽  
Vol 45 (4) ◽  
pp. 396-398
Author(s):  
Roger Smith
Keyword(s):  
Large Scale ◽  
Small Scale

Development of Physics Learning Module Based on Guided Inquiry with Light Wave Material in Class XI of Senior High School

2020 ◽  
Vol 1 (1) ◽  
pp. 1-10
Author(s):  
Evi Rahmawati ◽  
Irnin Agustina Dwi Astuti ◽  
N Nurhayati
Keyword(s):  
Large Scale ◽  
Learning Strategy ◽  
Student Response ◽  
Average Score ◽  
Small Scale ◽  
Design Development ◽  
Teacher Response ◽  
Student Responses ◽  
Analysis Design ◽  
Scientific Skills

IPA Integrated is a place for students to study themselves and the surrounding environment applied in daily life. Integrated IPA Learning provides a direct experience to students through the use and development of scientific skills and attitudes. The importance of integrated IPA requires to pack learning well, integrated IPA integration with the preparation of modules combined with learning strategy can maximize the learning process in school. In SMP 209 Jakarta, the value of the integrated IPA is obtained from 34 students there are 10 students completed and 24 students are not complete because they get the value below the KKM of 68. This research is a development study with the development model of ADDIE (Analysis, Design, Development, Implementation, and Evaluation). The use of KPS-based integrated IPA modules (Science Process sSkills) on the theme of rainbow phenomenon obtained by media expert validation results with an average score of 84.38%, average material expert 82.18%, average linguist 75.37%. So the average of all aspects obtained by 80.55% is worth using and tested to students. The results of the teacher response obtained 88.69% value with excellent criteria. Student responses on a small scale acquired an average score of 85.19% with highly agreed criteria and on the large-scale student response gained a yield of 86.44% with very agreed criteria. So the module can be concluded receiving a good response by the teacher and students.

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