scholarly journals Estimating the Spatial Heterogeneity and Seasonal Differences of the Contribution of Tourism Industry Activities to Night Light Index by POI

2022 ◽  
Vol 14 (2) ◽  
pp. 692
Author(s):  
Juan Wei ◽  
Yongde Zhong ◽  
Jingling Fan
Keyword(s):  
Spatial Distribution ◽  
Spatial Heterogeneity ◽  
Spatial Data ◽  
Ordinary Least Squares ◽  
Tourism Industry ◽  
Hunan Province ◽  
Observation Data ◽  
Seasonal Differences ◽  
Night Lights ◽  
Luminous Radiation

The spatial distribution of tourism has a profound impact on its operational efficiency and geographical relevance. Point of interest (POI), as a kind of spatial data shared by subject and object, can reflect the spatial distribution form and function of tourism geographical objects under the all-for-one tourism policy. Continuous satellite observation and in-depth study of night lights pave the way to clarify human activities and socio-economic dynamics. The purpose of this paper is to investigate the seasonal changes of night light images and their correlation with tourism in 122 counties (cities, districts) of Hunan Province. We obtained night earth observation data (seasonality) and POI in 2019 and processed them by Geographic Information System and statistical analysis (ordinary least squares (OLS) and geographically weighted regression (GWR)). The results show that the luminous radiation intensity is highly correlated with the POI of tourism activities. The POI of different tourism activities in different regions shows obvious spatial heterogeneity and seasonal differences, which is the result of the comprehensive effect of tourism resource distribution and social environment in Hunan Province. GWR has proved to be a more effective tool. It provides a new method and perspective for tourism research and especially reveals the geographical spatial differences of tourism activities, which is helpful to study the spatial distribution and seasonality of tourism at the county level. In addition, the spatial evaluation of the contribution of tourism and luminous radiation can provide reference and suggestions for relevant departments to formulate tourism night protection measures.

Uma análise espacial do emprego formal dos municípios alagoanos: evidências para setor agrícola

2020 ◽  
Vol 10 (23) ◽  
pp. 1-12
Author(s):  
Samoel Santos Da Silva ◽  
Keuler Hissa Teixeira
Keyword(s):  
Spatial Distribution ◽  
Spatial Heterogeneity ◽  
Distribution Pattern ◽  
Spatial Data ◽  
Spatial Dependence ◽  
Agricultural Sector ◽  
Descriptive Analysis ◽  
The State ◽  
The Other ◽  
Formal Employment

ResumoO objetivo principal deste estudo é avaliar a presença de dependência espacial no estoque de emprego formal no setor agrícola do Estado de Alagoas nos anos 2002 e 2011. Neste sentido, esse trabalho identifica através da Análise Exploratória de Dados Espaciais aglomerações geográficas de valores significativamente similares (clusters), localidades atípicas (outliers) e padrões de heterogeneidade espacial no emprego formal no setor agrícola dos municípios no Estado de Alagoas. Desta forma, busca-se apresentar por meio de mapas de coeficiente de correlação espacial e de uma análise descritiva evidências empíricas que possibilitem delinear um padrão de distribuição espacial do emprego formal do setor acima citado. Contudo, pretende-se, também, identificar seus transbordamentos espaciais entre os municípios e desta forma perceber as contribuições do outro setor para reverter as desigualdades entre as regiões de Alagoas. AbstractThe main objective of this study is to evaluate the presence of spatial dependence in the formal employment stock in the agricultural sector in the state of Alagoas in the years 2002 and 2011. In this sense, this work identifies, through the Exploratory Analysis of Spatial Data, geographic agglomerations of significantly similar values (Clusters), outliers and patterns of spatial heterogeneity in formal employment in the agricultural sector of the municipalities of the state of Alagoas. In this way, we seek to present, through maps, the spatial correlation coefficient and a descriptive analysis, empirical evidence that allows us to delineate a spatial distribution pattern of the formal employment of the aforementioned sector. However, it is also intended to identify their spatial floods between the municipalities and in this way perceive the contributions of the other sector to reverse the inequalities between the Alagoas regions. 

scholarly journals Modeling spatial determinates of teenage pregnancy in Ethiopia; geographically weighted regression

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Seblewongel Tigabu ◽  
Alemneh Mekuriaw Liyew ◽  
Bisrat Misganaw Geremew
Keyword(s):  
Spatial Distribution ◽  
Geographically Weighted Regression ◽  
Teenage Pregnancy ◽  
Wealth Quintile ◽  
Ordinary Least Squares ◽  
Weighted Regression ◽  
Contraceptive Methods ◽  
Hotspot Analysis ◽  
Increased Risk ◽  
Risk Areas

Abstract Background In developing countries, 20,000 under 18 children give birth every day. In Ethiopia, teenage pregnancy is high with Afar and Somalia regions having the largest share. Even though teenage pregnancy has bad maternal and child health consequences, to date there is limited evidence on its spatial distribution and driving factors. Therefore, this study is aimed to assess the spatial distribution and spatial determinates of teenage pregnancy in Ethiopia. Methods A secondary data analysis was conducted using 2016 EDHS data. A total weighted sample of 3381 teenagers was included. The spatial clustering of teenage pregnancy was priorly explored by using hotspot analysis and spatial scanning statistics to indicate geographical risk areas of teenage pregnancy. Besides spatial modeling was conducted by applying Ordinary least squares regression and geographically weighted regression to determine factors explaining the geographic variation of teenage pregnancy. Result Based on the findings of exploratory analysis the high-risk areas of teenage pregnancy were observed in the Somali, Afar, Oromia, and Hareri regions. Women with primary education, being in the household with a poorer wealth quintile using none of the contraceptive methods and using traditional contraceptive methods were significant spatial determinates of the spatial variation of teenage pregnancy in Ethiopia. Conclusion geographic areas where a high proportion of women didn’t use any type of contraceptive methods, use traditional contraceptive methods, and from households with poor wealth quintile had increased risk of teenage pregnancy. Whereas, those areas with a higher proportion of women with secondary education had a decreased risk of teenage pregnancy. The detailed maps of hotspots of teenage pregnancy and its predictors had supreme importance to policymakers for the design and implementation of adolescent targeted programs.

Application of PCA with georeferenced data in the tourism industry: A case study in the province of Córdoba, Argentina

2021 ◽  
pp. 135481662098768
Author(s):  
Laura I Luna
Keyword(s):  
Decision Making ◽  
Principal Components ◽  
Spatial Data ◽  
Local Development ◽  
Value Added ◽  
Tourism Industry ◽  
Methodological Innovation ◽  
Spatial Principal Components ◽  
Components Analysis

The spatial analysis of tourism industries provides information about their structure, which is necessary for decision-making. In this work, tourism industries in the departments of Córdoba province, Argentina, for the 2001–2014 period were mapped. Multivariate methods with and without spatial restrictions (spatial principal components (sPCs) analysis, MULTISPATI-PCA, and principal components analysis (PCA), respectively) were applied and their performance was compared. MULTISPATI-PCA yielded a higher degree of spatial structuring of the components that summarize tourism activities than PCA. The methodological innovation lies in the generation of statistics for multidimensional spatial data. The departments were classified according to the participation of tourism activities in the value added of tourism using the sPCs obtained as input of the cluster fuzzy k-means analysis. This information provides elements necessary for appropriately defining local development strategies and, therefore, is useful to improve decision-making.

scholarly journals Classification of Recreation Opportunity Spectrum Using Night Lights for Evidence of Humans and POI Data for Social Setting

2021 ◽  
Vol 13 (14) ◽  
pp. 7782
Author(s):  
Wenjing Zeng ◽  
Yongde Zhong ◽  
Dali Li ◽  
Jinyang Deng
Keyword(s):  
Large Scale ◽  
Regional Scale ◽  
Hunan Province ◽  
Social Settings ◽  
Recreation Opportunity Spectrum ◽  
City District ◽  
Night Lights ◽  
Recreational Resources ◽  
Metropolitan Counties ◽  
Land Types

The recreation opportunity spectrum (ROS) has been widely recognized as an effective tool for the inventory and planning of outdoor recreational resources. However, its applications have been primarily focused on forest-dominated settings with few studies being conducted on all land types at a regional scale. The creation of a ROS is based on physical, social, and managerial settings, with the physical setting being measured by three criteria: remoteness, size, and evidence of humans. One challenge to extending the ROS to all land types on a large scale is the difficulty of quantifying the evidence of humans and social settings. Thus, this study, for the first time, developed an innovative approach that used night lights as a proxy for evidence of humans and points of interest (POI) for social settings to generate an automatic ROS for Hunan Province using Geographic Information System (GIS) spatial analysis. The whole province was classified as primitive (2.51%), semi-primitive non-motorized (21.33%), semi-primitive motorized (38.60%), semi-developed natural (30.99%), developed natural (5.61%), and highly developed (0.96%), which was further divided into three subclasses: large-natural (0.63%), small natural (0.27%), and facilities (0.06%). In order to implement the management and utilization of natural recreational resources in Hunan Province at the county (city, district) level, the province’s 122 counties (cities, districts) were categorized into five levels based on the ROS factor dominance calculated at the county and provincial levels. These five levels include key natural recreational counties (cities, districts), general natural recreational counties (cities, districts), rural counties (cities, districts), general metropolitan counties (cities, districts), and key metropolitan counties (cities, districts), with the corresponding numbers being 8, 21, 50, 24, and 19, respectively.

scholarly journals Research on Spatiotemporal Differentiation and Influence Mechanism of Urban Resilience in China Based on MGWR Model

2021 ◽  
Vol 18 (3) ◽  
pp. 1056
Author(s):  
Yu Chen ◽  
Mengke Zhu ◽  
Qian Zhou ◽  
Yurong Qiao
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Correlation ◽  
Spatial Data ◽  
Central Area ◽  
Urban System ◽  
Entropy Method ◽  
Driving Factors ◽  
Spatial Data Analysis ◽  
Eastern Region ◽  
Urban Resilience

Urban resilience in the context of COVID-19 epidemic refers to the ability of an urban system to resist, absorb, adapt and recover from danger in time to hedge its impact when confronted with external shocks such as epidemic, which is also a capability that must be strengthened for urban development in the context of normal epidemic. Based on the multi-dimensional perspective, entropy method and exploratory spatial data analysis (ESDA) are used to analyze the spatiotemporal evolution characteristics of urban resilience of 281 cities of China from 2011 to 2018, and MGWR model is used to discuss the driving factors affecting the development of urban resilience. It is found that: (1) The urban resilience and sub-resilience show a continuous decline in time, with no obvious sign of convergence, while the spatial agglomeration effect shows an increasing trend year by year. (2) The spatial heterogeneity of urban resilience is significant, with obvious distribution characteristics of “high in east and low in west”. Urban resilience in the east, the central and the west are quite different in terms of development structure and spatial correlation. The eastern region is dominated by the “three-core driving mode”, and the urban resilience shows a significant positive spatial correlation; the central area is a “rectangular structure”, which is also spatially positively correlated; The western region is a “pyramid structure” with significant negative spatial correlation. (3) The spatial heterogeneity of the driving factors is significant, and they have different impact scales on the urban resilience development. The market capacity is the largest impact intensity, while the infrastructure investment is the least impact intensity. On this basis, this paper explores the ways to improve urban resilience in China from different aspects, such as market, technology, finance and government.

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.

Spatial Distribution of Local Permeabilities in Fibrous Networks

1992 ◽  
Vol 62 (3) ◽  
pp. 151-161 ◽  
Author(s):  
Susan M. Montgomery ◽  
Bernard Miller ◽  
Ludwig Rebenfeld
Keyword(s):  
Spatial Distribution ◽  
Spatial Heterogeneity ◽  
Heterogeneous Networks ◽  
Flow Direction ◽  
Time Dependent ◽  
Dependent Development ◽  
Fabric Structure ◽  
Elliptical Flow ◽  
Fluid Boundary ◽  
Liquid Flows

The shape of a developing radial fluid boundary in the plane of a fabric is a reflection of the structure of the fabric. Homogeneous fabrics, with permeabilities independent of position, yield circular or elliptical flow fronts, depending on the existence of a universally preferred flow direction. Heterogeneous networks yield flow fronts that deviate from this elliptical shape due to spatial variations in permeability. The time-dependent development of the fluid front that occurs when liquid flows radially in the plane of a fabric may be analyzed using Darcy's law to calculate local fabric permeabilities. The resulting spatial distribution of permeabilities is representative of the spatial heterogeneity of the fabric structure. Sample permeability distributions of geotextile fabrics are discussed.

scholarly journals A Spatial Quantitative Systems Pharmacology Platform spQSP-IO for Simulations of Tumor—Immune Interactions and Effects of Checkpoint Inhibitor Immunotherapy

Cancers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 3751
Author(s):  
Chang Gong ◽  
Alvaro Ruiz-Martinez ◽  
Holly Kimko ◽  
Aleksander S. Popel
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Data ◽  
Immune Cell ◽  
Imaging Techniques ◽  
Tumor Development ◽  
Systems Pharmacology ◽  
Quantitative Systems Pharmacology ◽  
Spatially Resolved ◽  
Biomarker Analysis ◽  
Model Platform

Quantitative systems pharmacology (QSP) models have become increasingly common in fundamental mechanistic studies and drug discovery in both academic and industrial environments. With imaging techniques widely adopted and other spatial quantification of tumor such as spatial transcriptomics gaining traction, it is crucial that these data reflecting tumor spatial heterogeneity be utilized to inform the QSP models to enhance their predictive power. We developed a hybrid computational model platform, spQSP-IO, to extend QSP models of immuno-oncology with spatially resolved agent-based models (ABM), combining their powers to track whole patient-scale dynamics and recapitulate the emergent spatial heterogeneity in the tumor. Using a model of non-small-cell lung cancer developed based on this platform, we studied the role of the tumor microenvironment and cancer–immune cell interactions in tumor development and applied anti-PD-1 treatment to virtual patients and studied how the spatial distribution of cells changes during tumor growth in response to the immune checkpoint inhibition treatment. Using parameter sensitivity analysis and biomarker analysis, we are able to identify mechanisms and pretreatment measurements correlated with treatment efficacy. By incorporating spatial data that highlight both heterogeneity in tumors and variability among individual patients, spQSP-IO models can extend the QSP framework and further advance virtual clinical trials.

A spatial heterogeneity-based rough set extension for spatial data

2018 ◽  
Vol 33 (2) ◽  
pp. 240-268 ◽  
Author(s):  
Hexiang Bai ◽  
Deyu Li ◽  
Yong Ge ◽  
Jinfeng Wang
Keyword(s):  
Spatial Heterogeneity ◽  
Rough Set ◽  
Spatial Data

scholarly journals How spatial heterogeneity shapes multiscale biochemical reaction network dynamics

2015 ◽  
Vol 12 (104) ◽  
pp. 20141106 ◽  
Author(s):  
Peter Pfaffelhuber ◽  
Lea Popovic
Keyword(s):  
Spatial Distribution ◽  
Spatial Heterogeneity ◽  
Reaction Network ◽  
Stochastic Averaging ◽  
Multiscale Model ◽  
Point Of View ◽  
Rigorous Results ◽  
Biochemical Reaction Network ◽  
Molecular Movement ◽  
Heterogeneous Multiscale

Spatial heterogeneity in cells can be modelled using distinct compartments connected by molecular movement between them. In addition to movement, changes in the amount of molecules are due to biochemical reactions within compartments, often such that some molecular types fluctuate on a slower timescale than others. It is natural to ask the following questions: how sensitive is the dynamics of molecular types to their own spatial distribution, and how sensitive are they to the distribution of others? What conditions lead to effective homogeneity in biochemical dynamics despite heterogeneity in molecular distribution? What kind of spatial distribution is optimal from the point of view of some downstream product? Within a spatially heterogeneous multiscale model, we consider two notions of dynamical homogeneity (full homogeneity and homogeneity for the fast subsystem), and consider their implications under different timescales for the motility of molecules between compartments. We derive rigorous results for their dynamics and long-term behaviour, and illustrate them with examples of a shared pathway, Michaelis–Menten enzymatic kinetics and autoregulating feedbacks. Using stochastic averaging of fast fluctuations to their quasi-steady-state distribution, we obtain simple analytic results that significantly reduce the complexity and expedite simulation of stochastic compartment models of chemical reactions.

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