scholarly journals Spatial-Temporal Analysis of the Economic and Environmental Coordination Development Degree in Liaoning Province

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Hui Wang ◽  
Fan Liu ◽  
Ying Yuan ◽  
Liang Wang
Keyword(s):  
Spatial Data ◽  
Temporal Analysis ◽  
Spatial Dimension ◽  
Spatial Data Analysis ◽  
Liaoning Province ◽  
Time Dimension ◽  
Temporal Dimension ◽  
Northwestern Region ◽  
Data Points ◽  
Coordination Development

This study selects 20 indices of economic and environmental conditions over 15 years (1996–2010) for 14 cities in Liaoning province, China. We calculate the economic score and environmental score of each city by processing 4200 data points through SPSS 16.0 and establish synthesis functions between the economy and the environment. For the time dimension, we study the temporal evolution of the economic and environmental coordination development degree . Based on Exploratory Spatial Data Analysis (ESDA) techniques and using GeoDa, we calculate Moran's index of local spatial autocorrelation and explore the spatial distribution character of in Liaoning province through a LISA cluster map. As we found in the temporal dimension, the results show that of the 14 cities has been rising for 15 years and that increases year by year, which indicates that the economic and environmental coordination development condition has been improving from disorder to highly coordinated. A smaller gap between economic strength and environmental carrying capacity in Liaoning province exists, which means that economic development and environmental protection remain synchronized. In the spatial dimension, the highly coordinated cities have changed from a scattering to a concentration in the middle-south region of Liaoning province. Poorly coordinated cities are scattered in the northwestern region of Liaoning province.

Cluster identification

2019 ◽  
Vol 29 (4) ◽  
pp. 401-415
Author(s):  
Reinaldo Belickas Manzini ◽  
Di Serio Carlos Luiz
Keyword(s):  
Spatial Data ◽  
Regional Analysis ◽  
Spatial Association ◽  
Spatial Dimension ◽  
Spatial Data Analysis ◽  
Industry Concentration ◽  
Spatial Effects ◽  
Temporal Dimension ◽  
Content Type ◽  
Concentration Statistics

Purpose This paper aims to contribute to the approaches based on traditional industry concentration statistics for identifying clusters by complementing them with the techniques of exploratory spatial data analysis (ESDA). Design/methodology/approach Using a sample with 34,500 observations retrieved from the social information annual report released by Brazil Ministry of Labor and Employment, the methodology was designed to make a comparison between the application of industry concentration statistics and ESDA statistics. Findings As the results show, the geographic distribution measures proved to be fundamental for longitudinal studies on regional dynamics and industrial agglomerations, and the local indicator of spatial association statistic tends to overcome the limitation of the industry concentration approach. Research limitations/implications In the period considered, due to economic, structural and circumstantial questions, activities linked to the transformation industry have been losing ground in the value creation process in Brazil. In this sense, the study of other industries may generate other types of insights that should be considered in the process of regional development. Originality/value This paper offers a critical analysis of empirical approaches and methodological advances with an emphasis on the treatment of special effects: spatial dependence, spatial heterogeneity and spatial scale. However, the regional dynamic presents a temporal dimension and a spatial dimension. The role of space has increasingly attracted attention in the analysis of economic changes. This work has identified opportunities for incorporating spatial effects in regional analysis over time.

Isotropic correlation functions on d-dimensional balls

1999 ◽  
Vol 31 (03) ◽  
pp. 625-631
Author(s):  
Tilmann Gneiting
Keyword(s):  
Data Analysis ◽  
Spatial Data ◽  
Correlation Functions ◽  
Dimensional Space ◽  
Extension Theorem ◽  
Spatial Dimension ◽  
Positive Definite ◽  
Spatial Data Analysis ◽  
Spatial Lag ◽  
Image Position

A popular procedure in spatial data analysis is to fit a line segment of the formc(x) = 1 - α ||x||, ||x|| < 1, to observed correlations at (appropriately scaled) spatial lagxind-dimensional space. We show that such an approach is permissible if and only ifthe upper bound depending on the spatial dimensiond. The proof relies on Matheron's turning bands operator and an extension theorem for positive definite functions due to Rudin. Side results and examples include a general discussion of isotropic correlation functions defined ond-dimensional balls.

scholarly journals Spatio-temporal analysis of the development of the COVID-19 epidemic (pandemic) in Poland: First phase of development

2021 ◽  
Vol 94 (3) ◽  
pp. 325-354
Author(s):  
Jerzy Parysek ◽  
Lidia Mierzejewska
Keyword(s):  
Development Process ◽  
Temporal Analysis ◽  
Spatial Dimension ◽  
Regional Differentiation ◽  
Retirement Age ◽  
Material Base ◽  
Time Dimension ◽  
Spatio Temporal ◽  
Degree Of Similarity

The purpose of this study is to present a description of the course of the COVID-19 epidemic in Poland in the space-time dimension in the period from March 15th to August 8th 2020. The result of the conducted research is a presentation of the regional differentiation of the course of the epidemic in Poland, the comparison of the intensity of SARS-CoV-2 infections in particular voivodeships, the determination of the degree of similarity in the course of the pandemic development process in individual regions (voivodeships) of the country, and also the indication of the factors which could be taken into account when attempting to explain the interregional differences in the course of the epidemic. The conducted research shows, among other things, that: (1) in terms of time, the development of the epidemic was generally monotonic, however the increase in new infections was rather cyclical, (2) in the spatial dimension, the development of the epidemic was rather random, although the greatest number of infections was characteristic of the most populated regions of the country, (3) the level of infections in Poland was mainly positively influenced by: population density, working in industry, people beyond retirement, age as well as a poorly developed material base of inpatient care.

scholarly journals Research on Urban and Rural Coordination Development and Its Driving Force Based on the Space-time Evolvement Taking Guangdong Province as an Example

Land ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 253
Author(s):  
Daizhong Tang ◽  
Baorui Li ◽  
Yuan Qiu ◽  
Linlin Zhao
Keyword(s):  
Spatial Data ◽  
Driving Forces ◽  
River Estuary ◽  
Principal Component ◽  
Guangdong Province ◽  
Pearl River Estuary ◽  
Space Time ◽  
Spatial Data Analysis ◽  
New Countryside ◽  
Coordination Development

Based on the background of the change in the urban–rural relationship in Guangdong Province, this paper constructs an analysis framework of urban and rural coordination development. Using the data of 19 administrative units above prefecture level in Guangdong Province, this paper studies the space–time evolution of urban and rural coordination development during 2000–2015 through Principal Component Analysis (PCA) and Exploratory Spatial Data Analysis (ESDA) and explores the influencing factors and driving forces behind it. It is found that there is club convergence in the urban and rural coordination development in Guangdong Province. This kind of convergence is reflected in the findings that the east bank of the Pearl River estuary is the best area for the urban and rural coordination development where Guangzhou, Dongguan, Shenzhen is the core and the level of urban and rural coordination development in the east, west and north of Guangdong Province is relatively low, which also reflects a geographical polarization feature. Based on the analysis of the factors that promote the urban and rural coordination development in the main years of 2000–2015, it can be concluded that location, economic development and urbanization level are the most important driving forces, followed by industrial structure. This research can be used as a decision-making reference for urban and rural coordination development and new countryside construction in China in the New Era.

Isotropic correlation functions on d-dimensional balls

1999 ◽  
Vol 31 (3) ◽  
pp. 625-631 ◽  
Author(s):  
Tilmann Gneiting
Keyword(s):  
Data Analysis ◽  
Spatial Data ◽  
Correlation Functions ◽  
Dimensional Space ◽  
Extension Theorem ◽  
Spatial Dimension ◽  
Positive Definite ◽  
Spatial Data Analysis ◽  
Spatial Lag ◽  
Image Position

A popular procedure in spatial data analysis is to fit a line segment of the form c(x) = 1 - α ||x||, ||x|| < 1, to observed correlations at (appropriately scaled) spatial lag x in d-dimensional space. We show that such an approach is permissible if and only if the upper bound depending on the spatial dimension d. The proof relies on Matheron's turning bands operator and an extension theorem for positive definite functions due to Rudin. Side results and examples include a general discussion of isotropic correlation functions defined on d-dimensional balls.

scholarly journals Effects of Different Spatial Precipitation Input Data on Crop Model Outputs under a Central European Climate

Atmosphere ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 290 ◽  
Author(s):  
Sabina Thaler ◽  
Luca Brocca ◽  
Luca Ciabatta ◽  
Josef Eitzinger ◽  
Sebastian Hahn ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Spatial Data ◽  
Crop Production ◽  
Input Data ◽  
Spring Barley ◽  
Regional Scale ◽  
Temporal Analysis ◽  
Spatial Data Analysis ◽  
Distributed Model ◽  
Precipitation Data

Crop simulation models, which are mainly being utilized as tools to assess the consequences of a changing climate and different management strategies on crop production at the field scale, are increasingly being used in a distributed model at the regional scale. Spatial data analysis and modelling in combination with geographic information systems (GIS) integrates information from soil, climate, and topography data into a larger area, providing a basis for spatial and temporal analysis. In the current study, the crop growth model Decision Support System for Agrotechnology Transfer (DSSAT) was used to evaluate five gridded precipitation input data at three locations in Austria. The precipitation data sets consist of the INtegrated Calibration and Application Tool (INCA) from the Meteorological Service Austria, two satellite precipitation data sources—Multisatellite Precipitation Analysis (TMPA) and Climate Prediction Center MORPHing (CMORPH)—and two rainfall estimates based on satellite soil moisture data. The latter were obtained through the application of the SM2RAIN algorithm (SM2RASC) and a regression analysis (RAASC) applied to the Metop-A/B Advanced SCATtermonter (ASCAT) soil moisture product during a 9-year period from 2007–2015. For the evaluation, the effect on winter wheat and spring barley yield, caused by different precipitation inputs, at a spatial resolution of around 25 km was used. The highest variance was obtained for the driest area with light-textured soils; TMPA and two soil moisture-based products show very good results in the more humid areas. The poorest performances at all three locations and for both crops were found with the CMORPH input data.

An Interdisciplinary Narrative Oncology, Capital, and Solidarity

2018 ◽  
Vol 12 (1) ◽  
Author(s):  
Benjamin Wilson
Keyword(s):  
Genetic Mapping ◽  
Spatial Data ◽  
Spatial Dimension ◽  
Spatial Data Analysis ◽  
Patient Specific ◽  
Gis Technology ◽  
Solidarity Economy ◽  
Oncology Research ◽  
Cancer Mutations ◽  
Story Maps

This paper utilizes oncology as the thematic core of an interdisciplinary narrative describing spatial tensions between capitalism and developments in the countervailing social and solidarity economy (SSE).  This exercise explores oncology as metaphor, as a methodological model, and for intervention and policy change.  A Marxian framework supports this narrative construction.   Vampires, the fundamental class process, and the circuit of money capital are concepts that link the genetics of cancer and recent advances in oncology research to political economy.  The application of geographic information system (GIS) technology contributes a spatial dimension to this story.  Maps and exploratory spatial data analysis of the metamorphoses generated across the stages of the circuit of money capital present GIS’s capacity to replicate the genetic mapping of cancer mutations used in oncology.  In addition to diagnostics, genetic mapping enables targeted patient-specific hormonal treatments.  Similarly, it is argued that the SSE is an effective locational treatment strategy for the adverse effects of capitalism’s metabolic processes.

scholarly journals Event Detection and Spatio-temporal Analysis of Low-Altitude Unstable Approach

2020 ◽  
Vol 10 (14) ◽  
pp. 4934
Author(s):  
Huabo Sun ◽  
Jiayi Xie ◽  
Yang Jiao ◽  
Rongshun Huang ◽  
Binbin Lu
Keyword(s):  
Spatial Distribution ◽  
Correlation Coefficient ◽  
Spatial Data ◽  
Mainland China ◽  
Temporal Analysis ◽  
Spatial Data Analysis ◽  
Technical Program ◽  
Low Altitude ◽  
Spatio Temporal ◽  
Bad Weather

Low-altitude unstable approach (UA) is one of the crucial risks that threaten flight safety. In this study, we proposed a technical program for detecting low-altitude UA events. The detection logic was to optimize the step-wise regression model with iterative surveys with more than 20 experienced pilots. Accordingly, the frequencies of UA events occurring around each airport in January 2018 were calculated for all the airports within mainland China. Finally, the spatial distribution characteristics of UA events were analyzed via exploratory spatial data analysis. In addition, Pearson’s correlation coefficient and the geographically weighted correlation coefficient were used to explore the correlations between UA frequency and the altitude elevation, wind level, and bad weather. The experimental results revealed that the proposed method can accurately detect the occurrence of low-altitude UA and quantitatively characterize risks. It was found that UA exhibits obvious differences in spatial distribution. Moreover, significantly strong correlations were found between UA and altitude elevation, wind level, and bad weather, and correlation differences were also reflected in different regions in China.

scholarly journals Social Disorganization and Urban Homicide Rates: A Spatial-Temporal Analysis in São Paulo, Brazil 2000 to 2015

2021 ◽  
pp. 108876792110108
Author(s):  
Amy Nivette ◽  
Maria Fernanda Tourinho Peres
Keyword(s):  
Spatial Data ◽  
Social Disorganization ◽  
Temporal Patterns ◽  
Temporal Analysis ◽  
Sao Paulo ◽  
Spatial Data Analysis ◽  
São Paulo ◽  
Spatial And Temporal Patterns ◽  
Homicide Rates ◽  
Regression Techniques

This study aims to contribute to understanding urban spatial and temporal patterns of social disorganization and homicide rates in São Paulo, Brazil (2000–2015). Using exploratory spatial data analysis and spatial panel regression techniques, we describe spatial-temporal patterns of homicide rates and assess to what extent social disorganization can explain between-district variation in homicide trajectories. The results showed some variation in the pattern of homicide decline across districts, and less disorganized communities experienced earlier, more linear declines. However, we found no evidence to suggest that changes in social disorganization are associated with differences in the decline in homicide rates.

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