scholarly journals “Seeing” or “Being Seen”: Research on the Sight Line Design in the Lion Grove Based on Visitor Temporal–Spatial Distribution and Space Syntax

2019 ◽  
Vol 11 (16) ◽  
pp. 4348 ◽  
Author(s):  
Tiantian Zhang ◽  
Weicheng Hua ◽  
Yannan Xu
Keyword(s):  
Spatial Distribution ◽  
Spatial Data ◽  
Visual Control ◽  
Average Speed ◽  
Data Loggers ◽  
One Step ◽  
Gps Data Loggers ◽  
Large Water ◽  
Visual Characteristic ◽  
Line Design

Research on the sight line design of the Classical Chinese Garden (CCG) is an important issue of CCGs’ sustainable development. Taking the Lion Grove as a case, GPS data loggers and questionnaires were employed to collect visitor temporal–spatial data and visiting motivations. We then calculated the “Revisiting Proportion” and “Average Speed” values. Furthermore, we selected the “Visual Control” values analyzed by Depthmap as an indicator of visibility. The statistical analysis of the relationship among “Revisiting Proportion”, “Average Speed”, and “Visual Control” values of each space showed that the spatial visual characteristic affected the visitor temporal–spatial distribution. Scenery spots in and around the large water pool, within one-step visual depth of each other, occupying the visual advantage of both “seeing” and “being seen”, can facilitate the transformation of sight lines and form the visual effect of “one step, one scene”. This research also proved that the sight line design of the Lion Grove was more intentional than random.

scholarly journals Spatial Agglomeration of China’s Forest Products Manufacturing Industry: Measurement, Characteristics and Determinants

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1006
Author(s):  
Zhenhuan Chen ◽  
Hongge Zhu ◽  
Wencheng Zhao ◽  
Menghan Zhao ◽  
Yutong Zhang
Keyword(s):  
Spatial Distribution ◽  
Spatial Data ◽  
Large Scale ◽  
Manufacturing Industry ◽  
Economies Of Scale ◽  
Forest Products ◽  
Spatial Data Analysis ◽  
Forest Protection ◽  
Spatial Agglomeration ◽  
Continuous Space

China’s forest products manufacturing industry is experiencing the dual pressure of forest protection policies and wood scarcity and, therefore, it is of great significance to reveal the spatial agglomeration characteristics and evolution drivers of this industry to enhance its sustainable development. Based on the perspective of large-scale agglomeration in a continuous space, in this study, we used the spatial Gini coefficient and standard deviation ellipse method to investigate the spatial agglomeration degree and location distribution characteristics of China’s forest products manufacturing industry, and we used exploratory spatial data analysis to investigate its spatial agglomeration pattern. The results show that: (1) From 1988 to 2018, the degree of spatial agglomeration of China’s forest products manufacturing industry was relatively low, and the industry was characterized by a very pronounced imbalance in its spatial distribution. (2) The industry has a very clear core–periphery structure, the spatial distribution exhibits a “northeast-southwest” pattern, and the barycenter of the industrial distribution has tended to move south. (3) The industry mainly has a high–high and low–low spatial agglomeration pattern. The provinces with high–high agglomeration are few and concentrated in the southeast coastal area. (4) The spatial agglomeration and evolution characteristics of China’s forest products manufacturing industry may be simultaneously affected by forest protection policies, sources of raw materials, international trade and the degree of marketization. In the future, China’s forest products manufacturing industry should further increase the level of spatial agglomeration to fully realize the economies of scale.

Mapping the Spatial Distribution Patterns of Personal Time Spent Based on Trip Purpose

2018 ◽  
Vol 9 (2) ◽  
pp. 1-13 ◽  
Author(s):  
Ko Ko Lwin ◽  
Yoshihide Sekimoto
Keyword(s):  
Spatial Distribution ◽  
Survey Data ◽  
Spatial Data ◽  
Distribution Patterns ◽  
Spatial Data Mining ◽  
Urban Transport ◽  
Social Characteristics ◽  
Public Facility ◽  
Spatial Distribution Patterns ◽  
Trip Purpose

Understanding the spatial distribution patterns of the time spent by people based on their trip purpose and other social characteristics is important for sustainable urban transport planning, public facility management, socio-economic development, and other types of policy planning. Although personal trip survey data includes travel behavior and other social characteristics, many are lacking in detail regarding the spatial distribution patterns of individual movements based on time spent, typically due to privacy issues and difficulties in converting non-spatial survey data into a spatial format. In this article, geospatially-enabled personal trip data (Geospatial Big Data), converted from traditional paper-based survey data, are subjected to a spatial data mining process in order to examine the detailed spatial distribution patterns of time spent by the public based on various trip purposes and other social characteristics, using the Tokyo metropolitan area as a case study.

scholarly journals Approach to Urban Environmental Justice Using Exploratory Spatial Data Analysis. The Case of Valencia’s Monumental Trees

2020 ◽  
Vol 12 (18) ◽  
pp. 7760
Author(s):  
Alfonso Gallego-Valadés ◽  
Francisco Ródenas-Rigla ◽  
Jorge Garcés-Ferrer
Keyword(s):  
Spatial Distribution ◽  
Data Analysis ◽  
Environmental Justice ◽  
Spatial Data ◽  
Urban Trees ◽  
Spatial Data Analysis ◽  
Exploratory Spatial Data Analysis ◽  
The Public ◽  
Object Of Study ◽  
The City

Environmental justice has been a relevant object of analysis in recent decades. The generation of patterns in the spatial distribution of urban trees has been a widely addressed issue in the literature. However, the spatial distribution of monumental trees still constitutes an unknown object of study. The aim of this paper was to analyse the spatial distribution of the monumental-tree heritage in the city of Valencia, using Exploratory Spatial Data Analysis (ESDA) methods, in relation to different population groups and to discuss some implications in terms of environmental justice, from the public-policy perspective. The results show that monumental trees are spatially concentrated in high-income neighbourhoods, and this fact represents an indicator of environmental inequality. This diagnosis can provide support for decision-making on this matter.

scholarly journals Usefulness of commercially available GPS data-loggers for tracking human movement and exposure to dengue virus

2009 ◽  
Vol 8 (1) ◽  
pp. 68 ◽  
Author(s):  
Gonzalo M Vazquez-Prokopec ◽  
Steven T Stoddard ◽  
Valerie Paz-Soldan ◽  
Amy C Morrison ◽  
John P Elder ◽  
...  
Keyword(s):  
Dengue Virus ◽  
Human Movement ◽  
Gps Data ◽  
Data Loggers ◽  
Gps Data Loggers

scholarly journals Spatial distribution of pedestrian-motor vehicle collisions before and after pedestrian countdown signal installation in Toronto, Canada

2017 ◽  
Vol 25 (2) ◽  
pp. 110-115 ◽  
Author(s):  
Linda Rothman ◽  
Marie-Soleil Cloutier ◽  
Alison K Macpherson ◽  
Sarah A Richmond ◽  
Andrew William Howard
Keyword(s):  
Spatial Distribution ◽  
Spatial Patterns ◽  
Spatial Data ◽  
Motor Vehicle ◽  
Spatial Data Analysis ◽  
Motor Vehicle Collisions ◽  
Vehicle Collisions ◽  
Point Patterns ◽  
Kernel Density Estimates ◽  
Before And After

BackgroundPedestrian countdown signals (PCS) have been installed in many cities over the last 15 years. Few studies have evaluated the effectiveness of PCS on pedestrian motor vehicle collisions (PMVC). This exploratory study compared the spatial patterns of collisions pre and post PCS installation at PCS intersections and intersections or roadways without PCS in Toronto, and examined differences by age.MethodsPCS were installed at the majority of Toronto intersections from 2007 to 2009. Spatial patterns were compared between 4 years of police-reported PMVC prior to PCS installation to 4 years post installation at 1864 intersections. The spatial distribution of PMVC was estimated using kernel density estimates and simple point patterns examined changes in spatial patterns overall and stratified by age. Areas of higher or lower point density pre to post installation were identified.ResultsThere were 14 911 PMVC included in the analysis. There was an overall reduction in PMVC post PCS installation at both PCS locations and non-PCS locations, with a greater reduction at non-PCS locations (22% vs 1%). There was an increase in PMVC involving adults (5%) and older adults (9%) at PCS locations after installation, with increased adult PMVC concentrated downtown, and older adult increases occurring throughout the city following no spatial pattern. There was a reduction in children’s PMVC at both PCS and non-PCS locations, with greater reductions at non-PCS locations (35% vs 48%).ConclusionsResults suggest that the effects of PCS on PMVC may vary by age and location, illustrating the usefulness of exploratory spatial data analysis approaches in road safety. The age and location effects need to be understood in order to consistently improve pedestrian mobility and safety using PCS.

scholarly journals A SVR Learning Based Sensor Placement Approach for Nonlinear Spatially Distributed Systems

2016 ◽  
Vol 2016 ◽  
pp. 1-12
Author(s):  
Xian-xia Zhang ◽  
Zhi-qiang Fu ◽  
Wei-lu Shan ◽  
Bing Wang ◽  
Tao Zou
Keyword(s):  
Spatial Distribution ◽  
Distributed Systems ◽  
Spatial Data ◽  
Sensor Placement ◽  
Spatiotemporal Data ◽  
Industrial Processes ◽  
Crucial Issue ◽  
Data Set ◽  
Spatially Distributed ◽  
Optimal Sensor Location

Many industrial processes are inherently distributed in space and time and are called spatially distributed dynamical systems (SDDSs). Sensor placement affects capturing the spatial distribution and then becomes crucial issue to model or control an SDDS. In this study, a new data-driven based sensor placement method is developed. SVR algorithm is innovatively used to extract the characteristics of spatial distribution from a spatiotemporal data set. The support vectors learned by SVR represent the crucial spatial data structure in the spatiotemporal data set, which can be employed to determine optimal sensor location and sensor number. A systematic sensor placement design scheme in three steps (data collection, SVR learning, and sensor locating) is developed for an easy implementation. Finally, effectiveness of the proposed sensor placement scheme is validated on two spatiotemporal 3D fuzzy controlled spatially distributed systems.

scholarly journals Spatial distribution of Infectious Disease Epidemic in The Soppeng Regency, 2016-2018

2021 ◽  
Vol 940 (1) ◽  
pp. 012017
Author(s):  
Basri ◽  
Tasrifin Tahara ◽  
Dinna Dayana La Ode Malim ◽  
La Ode Abdul Munafi
Keyword(s):  
Infectious Disease ◽  
Spatial Distribution ◽  
Infectious Diseases ◽  
Typhoid Fever ◽  
Dengue Hemorrhagic Fever ◽  
Spatial Data ◽  
Health Centre ◽  
Hemorrhagic Fever ◽  
Development Planning ◽  
Public Health Centre

Abstract Diarrhea, typhoid fever and dengue hemorrhagic fever (DHF) are environmentalbased infectious diseases that contribute to the mortality rate of humans. This paper investigates the spatial distribution and the infectious disease epidemic that occurs based on environmental factors. The three primary diseases analyzed were diarrhea, typhoid fever, and dengue hemorrhagic fever. We abstracted data from several sources, including administrative maps, Regional Spatial Planning, BAPPEDA Soppeng Regency, the Central Statistics Agency (BPS), Public Health Centre, RBI Maps, and National DEM. The tool used in this research is a computer equipped with ArcGIS. The analysis documented that the trend of the three primary diseases did not represent a consistent decline in three consecutive years and even increased in certain subdistricts. Spatial data shows that the spread of infectious diseases based on the incidence rate is still dominated at low levels, although medium and high IR categories are also found in several areas in The Soppeng district. This paper proposes information for local government to implement health development planning and programs, particularly preventing and treating infectious diseases in Soppeng District.

scholarly journals SPATIAL DATA INTERPOLATION

2018 ◽  
Vol 2 (4) ◽  
Author(s):  
Prof.RAE ZH Aliyev
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Spatial Distribution ◽  
Spatial Data ◽  
Movement Speed ◽  
Data Interpolation ◽  
Heavy Metal Concentrations ◽  
Groundwater Movement ◽  
Record Data ◽  
Inverse Distance

During the study and adjustment, techniques revealed our analysis of spatial data in vector format. The latter is best suited for the spatial analysis of discrete objects. However, when the spatial variable is represented as a field of scalar or vector greatness (for example, the spatial distribution of concentrations of heavy metal concentrations in soils or groundwater movement speed field). Convenient ways to record data is bitmap format. This approach is most often used for phenomena of processes that are characterized by considerable anisotropy. However, the characteristic feature of the method of inverse distance is the fact that the interpolated value in measured point is equal to the measured value. Key words: erosion, soil; heavy metals, extremum, spatial data, raster data anti-erosion measures

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