Micro-Spatial Analysis of Tenants within Large-Scale Retail Properties in Taiwan: A GIS Approach

2007 ◽  
Keyword(s):  
Spatial Analysis ◽  
Large Scale

scholarly journals Fast Playback Framework for Analysis of Ground-Based Doppler Radar Observations Using MapReduce Technology

2016 ◽  
Vol 33 (4) ◽  
pp. 621-634 ◽  
Author(s):  
Jingyin Tang ◽  
Corene J. Matyas
Keyword(s):  
Spatial Analysis ◽  
High Performance ◽  
Large Scale ◽  
Doppler Radar ◽  
Weather Events ◽  
Data Architecture ◽  
Research Grade ◽  
High Performance Computing Cluster ◽  
Time Systems ◽  
Performance Computing

AbstractThe creation of a 3D mosaic is often the first step when using the high-spatial- and temporal-resolution data produced by ground-based radars. Efficient yet accurate methods are needed to mosaic data from dozens of radar to better understand the precipitation processes in synoptic-scale systems such as tropical cyclones. Research-grade radar mosaic methods of analyzing historical weather events should utilize data from both sides of a moving temporal window and process them in a flexible data architecture that is not available in most stand-alone software tools or real-time systems. Thus, these historical analyses require a different strategy for optimizing flexibility and scalability by removing time constraints from the design. This paper presents a MapReduce-based playback framework using Apache Spark’s computational engine to interpolate large volumes of radar reflectivity and velocity data onto 3D grids. Designed as being friendly to use on a high-performance computing cluster, these methods may also be executed on a low-end configured machine. A protocol is designed to enable interoperability with GIS and spatial analysis functions in this framework. Open-source software is utilized to enhance radar usability in the nonspecialist community. Case studies during a tropical cyclone landfall shows this framework’s capability of efficiently creating a large-scale high-resolution 3D radar mosaic with the integration of GIS functions for spatial analysis.

scholarly journals A Spatial Analysis Approach to Understanding Caddoan Mounds in the Arkansas River Drainage

2004 ◽  
Author(s):  
Gregory Vogel
Keyword(s):  
Spatial Analysis ◽  
Spatial Data ◽  
Large Scale ◽  
Theoretical Background ◽  
Gis Analysis ◽  
Arkansas River ◽  
Gis Modeling ◽  
River Drainage ◽  
The People ◽  
Computationally Intensive

In this article I present a theoretical framework for understanding Caddoan mounds in the central Arkansas River drainage and the implications they may hold for the social structure and environmental adaptations of the people who made them. The power and efficiency of Geographic Information Systems (GIS) modeling now allows for large-scale, computationally intensive spatial analysis simply not possible before. Questions of landscape organization or spatial relationships that previously would have taken months or even years to answer can now be solved in a matter of minutes with GIS and related technologies, given the appropriate datasets. Quite importantly, though, such analyses must first be placed in context and theory if they are to be meaningful additions to our understanding of the past. While it is conventional to refer to “GIS analysis” (and I use the term in this article), it is important to keep in mind that data manipulations alone are not analysis. GIS, along with statistical software and related computer technologies, are tools of spatial analysis just as shovels and trowels are tools of excavation. Such tools can organize and reveal information if they are employed carefully, but the tools themselves have no agency and cannot interpret anything on their own. The terms “GIS analysis” or “GIS interpretation” are therefore somewhat misnomers, just as “trowel analysis” or “trowel interpretation” would be. It is not the GIS, or any component of it, that does the analysis or interpretation; it simply manipulates spatial data. We interpret these manipulations based upon theoretical background, previous research, and the questions we wish to answer.

scholarly journals Crossbows and imperial craft organisation: the bronze triggers of China's Terracotta Army

Antiquity ◽  
2014 ◽  
Vol 88 (339) ◽  
pp. 126-140 ◽  
Author(s):  
Xiuzhen Janice Li ◽  
Andrew Bevan ◽  
Marcos Martinón-Torres ◽  
Thilo Rehren ◽  
Wei Cao ◽  
...  
Keyword(s):  
Spatial Analysis ◽  
Large Scale ◽  
Craft Production ◽  
Third Century ◽  
Administrative Control ◽  
The Third ◽  
Trigger Mechanisms ◽  
Qin Shihuang

The Terracotta Army that protected the tomb of the Chinese emperor Qin Shihuang offers an evocative image of the power and organisation of the Qin armies who unified China through conquest in the third century BC. It also provides evidence for the craft production and administrative control that underpinned the Qin state. Bronze trigger mechanisms are all that remain of crossbows that once equipped certain kinds of warrior in the Terracotta Army. A metrical and spatial analysis of these triggers reveals that they were produced in batches and that these separate batches were thereafter possibly stored in an arsenal, but eventually were transported to the mausoleum to equip groups of terracotta crossbowmen in individual sectors of Pit 1. The trigger evidence for large-scale and highly organised production parallels that also documented for the manufacture of the bronze-tipped arrows and proposed for the terracotta figures themselves.

Application of UAV Technology to Planning Study on Chinese Villages in Guanzhong

2019 ◽  
pp. 180-195
Author(s):  
Yuan Zhong Cai ◽  
Feng Wu ◽  
Jing Li ◽  
Jin Wang ◽  
Mei Huang
Keyword(s):  
Magnetic Field ◽  
Spatial Analysis ◽  
Data Collection ◽  
Work Environment ◽  
Rural Areas ◽  
Large Scale ◽  
Industrial Facilities ◽  
Scale Factors ◽  
Rural Revitalization ◽  
The Magnetic Field

Driven by the state strategy of rural revitalization, Chinese rural areas receive unprecedented opportunities for development. However, China's Guanzhong region faces numerous problems in its rural planning research, such as 1) lack of terrain maps of most villages, 2) satellite maps collected from open platforms are inaccurate and fail to support a more detailed spatial analysis, 3) data and information are 2-dimensional, 4) data collection is inefficient. And, most villages consist of several village groups that are usually 400~500 m apart. Areas of Guanzhong are located on the plain, with low architectural height and an excellent environment of net clearance. In addition, there are no large-scale factors, mineral areas, and industrial facilities, which means low interference from the magnetic field. Compared with urban regions, such rural areas have a better work environment for UAV and better conditions of collecting needed data.

Parallelization and optimization of spatial analysis for large scale environmental model data assembly

2012 ◽  
Vol 89 ◽  
pp. 94-99 ◽  
Author(s):  
Gang Zhao ◽  
Brett A. Bryan ◽  
Darran King ◽  
Xiaodong Song ◽  
Qiang Yu
Keyword(s):  
Spatial Analysis ◽  
Large Scale ◽  
Model Data ◽  
Environmental Model

scholarly journals Temperature‐mediated changes in zooplankton body size: large scale temporal and spatial analysis

Ecography ◽  
2020 ◽  
Vol 43 (4) ◽  
pp. 581-590 ◽  
Author(s):  
Lowri E. Evans ◽  
Andrew G. Hirst ◽  
Pavel Kratina ◽  
Grégory Beaugrand
Keyword(s):  
Body Size ◽  
Spatial Analysis ◽  
Large Scale ◽  
Temporal And Spatial

scholarly journals A MAPPING METHOD OF INTEGRATING MULTI-SCALE RIVER THEMATIC MAPS

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 573-577
Author(s):  
W. Zhang ◽  
C. Yue ◽  
C. Cui ◽  
L. Meng
Keyword(s):  
Spatial Analysis ◽  
Large Scale ◽  
Mapping Method ◽  
Experimental Results ◽  
Small Scale ◽  
Thematic Maps ◽  
Fast Calculation ◽  
Thematic Map ◽  
Multi Scale ◽  
Scale Line

Small-scale maps are generally used in spatial analysis for fast calculation, but part of important features are missing due to its generalization level, which makes the analysis results less accurate. Therefore, it is necessary to improve feature completeness of smallscale maps. The goal of this paper is to put forward a mapping method of integrating the existing multi-scale river thematic maps. In order to achieve this goal, this paper proposed an algorithm for multi-scale line features matching by calculating the distance from node to polyline and an integrating algorithm by simplifying, shortening and merging the features from the original multi-scale thematic maps. The experimental results proved that the new map produced by the method proposed in this paper keeps the same scale as the original small-scale map and it is consistent with the original large-scale map in terms of feature completeness. The strategy proposed in this paper can be used to produce a new river thematic map concluding all the features that users need; moreover, the new map not only expresses features completely but also takes up less storage.

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