scholarly journals Spatial Dimension of Unemployment: Space-Time Analysis Using Real-Time Accessibility in Czechia

2020 ◽  
Vol 9 (6) ◽  
pp. 401 ◽  
Author(s):  
Pavlína Netrdová ◽  
Vojtěch Nosek
Keyword(s):  
Spatial Autocorrelation ◽  
Real Time ◽  
Spatial Dimension ◽  
Space Time ◽  
Spatial Weights ◽  
Spatial Weight ◽  
Unemployment Data ◽  
Global And Local ◽  
Municipal Level ◽  
Local Spatial Autocorrelation

This paper focuses on the analysis of unemployment data in Czechia on a very detailed spatial structure and yearly, extended time series (2002–2019). The main goal of the study was to examine the spatial dimension of disparities in regional unemployment and its evolutionary tendencies on a municipal level. To achieve this goal, global and local spatial autocorrelation methods were used. Besides spatial and space-time analyses, special attention was given to spatial weight matrix selection. The spatial weights were created according to real-time accessibilities between the municipalities based on the Czech road network. The results of spatial autocorrelation analyses based on network spatial weights were compared to the traditional distance-based spatial weights. Despite significant methodological differences between applied spatial weights, the resulting spatial pattern of unemployment proved to be very similar. Empirically, relative stability of spatial patterns of unemployment with only slow shift of differentiation from macro- to microlevels could be observed.

scholarly journals La evolución espacial de los subcentros de empleo en Ciudad Juárez, Chihuahua (1994-2004): un análisis con indicadores de autocorrelación espacial global y local / The Spatial Evolution of Employment Subcenters in Ciudad Juárez, Chihuahua (1994-2004): An Analysis Using Global and Local Spatial Autocorrelation Indicators

2015 ◽  
Vol 30 (2) ◽  
pp. 433 ◽  
Author(s):  
César M. Fuentes ◽  
Vladimir Hernández
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Association ◽  
Central Business District ◽  
High Density ◽  
Ciudad Juarez ◽  
Urban Structure ◽  
Threshold Method ◽  
Total Employment ◽  
Global And Local ◽  
Local Spatial Autocorrelation

El objetivo del artículo es identificar los subcentros de empleo total mediante el uso de indicadores de autocorrelación espacial global y local en Ciudad Juárez, Chihuahua, en el periodo 1994-2004. Esta metodología usa matrices de pesos espaciales e incorpora la noción de unidades vecinas y no está limitada al criterio de contigüidad del método de doble umbral. La variable usada fue la densidad bruta de empleo total (manufactura, comercio y servicio) en los años 1994 y 2004 a nivel de AGEB, obtenida de los Censos Económicos (INEGI, 1994 y 2004). Mediante el uso de dos indicadores de autocorrelación espacial, en específico el I de Moran y los indicadores locales de asociación espacial (LISA por sus siglas en inglés), fue posible identificar varios centros y subcentros de empleo total. Los resultados muestran la presencia de dependencia y heterogeneidad espacial que se manifiestan en la forma de agrupamientos de alta densidad de empleo (alto-alto) tanto en el distrito central de negocios (DCN) como en el subcentro de empleo mixto localizado en el corredor industrial de la avenida Rafael Pérez Serna. Asimismo, existen varios subcentros de empleo manufacturero aislados de alta densidad (alto-alto) localizados sobre las principales vialidades dirigidas a puertos internacionales. En este contexto, se puede concluir que la distribución del empleo fuera del DCN, producto de economías de aglomeración, implica la presencia de una estructura urbana policéntrica.Abstract:The objective of this article is to identify total employment subcenters through the use of global and local spatial autocorrelation indicators in Ciudad Juárez, Chihuahua, during the period from 1994-2004. This methodology uses spatial weights matrices, includes the notion of neighboring units and is not restricted to the contiguity criterion of the double threshold method. The variable used was the gross density of total employment (manufacturing, trade and service) in 1994 and 2004 at the ageb level, obtained from the Economic Census (INEGI, 1994 and 2004). Two spatial autocorrelation indicators, specifically Moran’s I and local indicators of spatial association (LISA) were used to identify several centers and sub-centers of total employment. The results show the presence of dependence and spatial heterogeneity expressed in the form of groups of high density employment (high-high) in both the central business district (CBD) and the mixed employment subcenter located in the industrial corridor of Avenida Rafael Pérez Serna. Likewise, there are several isolated high density manufacturing employment subcenters (high-high) located on the main roads leading to international ports. In this context, one can conclude that employment distribution outside the CBD, resulting from agglomeration economies, implies the presence of a polycentric urban structure.

Global and Local Spatial Autocorrelation in Predictive Clustering Trees

2011 ◽  
pp. 307-322 ◽  
Author(s):  
Daniela Stojanova ◽  
Michelangelo Ceci ◽  
Annalisa Appice ◽  
Donato Malerba ◽  
Sašo Džeroski
Keyword(s):  
Spatial Autocorrelation ◽  
Global And Local ◽  
Predictive Clustering Trees ◽  
Local Spatial Autocorrelation

scholarly journals Phenylketonuria incidence in China between 2013 and 2017 based on data from the Chinese newborn screening information system: a descriptive study

BMJ Open ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. e031474 ◽  
Author(s):  
Liangcheng Xiang ◽  
Jing Tao ◽  
Kui Deng ◽  
Xiaohong Li ◽  
Qi Li ◽  
...  
Keyword(s):  
Information System ◽  
Newborn Screening ◽  
Spatial Autocorrelation ◽  
Spatial Clustering ◽  
Mild Disease ◽  
System A ◽  
High Incidence ◽  
Global And Local ◽  
Global Moran’S I ◽  
Local Spatial Autocorrelation

ObjectiveThis study examines the incidence and spatial clustering of phenylketonuria (PKU) in China between 2013 and 2017.MethodsData from the Chinese Newborn Screening Information System were analysed to assess PKU incidence with 95% CIs by province, region and disease severity. Spatial clustering of PKU cases was analysed using global and local spatial autocorrelation analysis in the geographic information system.ResultsThe database contained 4925 neonates with confirmed PKU during the study period, corresponding to an incidence of 6.28 (95% CI: 6.11 to 6.46) per 100 000 neonates screened. Incidence was highest in the provinces of Gansu, Ningxia and Qinghai, where it ranged from 19.00 to 28.63 per 100 000 neonates screened. Overall incidence was higher in the northern part of the country, where classical disease predominated, than in the southern part, where mild disease predominated. PKU cases clustered spatially (global Moran’s I=0.3603,Z=5.3097, p<0.001), and local spatial autocorrelation identified four northern provinces as high–high clusters (Gansu, Qinghai, Ningxia and Shanxi).ConclusionsChina shows an intermediate PKU incidence among countries, and incidence differs substantially among Chinese provinces and between northern and southern regions. Our results suggest the need to focus efforts on screening, diagnosing and treating PKU in high-incidence provinces.

GIS-Aided Evolvement Analysis of Spatial-Temporal Pattern of Regional Tourism Industry Environment

2013 ◽  
Vol 726-731 ◽  
pp. 4690-4693
Author(s):  
Hong Yang ◽  
Xiao Ya Dong ◽  
Min Wang ◽  
Yu Guo
Keyword(s):  
Spatial Autocorrelation ◽  
Temporal Pattern ◽  
Spatial Cluster ◽  
Tourism Industry ◽  
Regional Tourism ◽  
Industry Environment ◽  
Spatial Weight ◽  
Tourism Economy ◽  
Global Spatial Autocorrelation ◽  
Local Spatial Autocorrelation

Regional tourism is a part of China's economic income. It is of great significance for tourism economy development to study spatial-temporal evolvement. This study analyzed time-based characteristics and spatial cluster characteristics through methods including Spatial Weight Matrix, global spatial autocorrelation (Morans I) statistic, spatial Statistics (Getis-Ord Gi*) and local spatial autocorrelation calculations. Results show that the overall spatial autocorrelation model changed slowly from negative (-0.05) to positive (0.08) while eastern part of study area clustered as hotspot and western part clustered as coldspot. It can be concluded that the spatial distribution pattern of the tourism economy in study area from 2002 to 2007 was increasingly clustered and the tourism development of each units in study area will be much more spatially inter-dependent.

scholarly journals Recognizing the Aggregation Characteristics of Extreme Precipitation Events Using Spatio-Temporal Scanning and the Local Spatial Autocorrelation Model

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 218
Author(s):  
Changjun Wan ◽  
Changxiu Cheng ◽  
Sijing Ye ◽  
Shi Shen ◽  
Ting Zhang
Keyword(s):  
Spatial Autocorrelation ◽  
Extreme Precipitation ◽  
Eastern China ◽  
Temporal Aggregation ◽  
Global Changes ◽  
Percentile Method ◽  
Extreme Precipitation Events ◽  
Spatio Temporal ◽  
Precipitation Events ◽  
Local Spatial Autocorrelation

Precipitation is an essential climate variable in the hydrologic cycle. Its abnormal change would have a serious impact on the social economy, ecological development and life safety. In recent decades, many studies about extreme precipitation have been performed on spatio-temporal variation patterns under global changes; little research has been conducted on the regionality and persistence, which tend to be more destructive. This study defines extreme precipitation events by percentile method, then applies the spatio-temporal scanning model (STSM) and the local spatial autocorrelation model (LSAM) to explore the spatio-temporal aggregation characteristics of extreme precipitation, taking China in July as a case. The study result showed that the STSM with the LSAM can effectively detect the spatio-temporal accumulation areas. The extreme precipitation events of China in July 2016 have a significant spatio-temporal aggregation characteristic. From the spatial perspective, China’s summer extreme precipitation spatio-temporal clusters are mainly distributed in eastern China and northern China, such as Dongting Lake plain, the Circum-Bohai Sea region, Gansu, and Xinjiang. From the temporal perspective, the spatio-temporal clusters of extreme precipitation are mainly distributed in July, and its occurrence was delayed with an increase in latitude, except for in Xinjiang, where extreme precipitation events often take place earlier and persist longer.

scholarly journals A longitudinal study of e-commerce diversity in Europe

2021 ◽  
Author(s):  
Adam Sadowski ◽  
Karolina Lewandowska-Gwarda ◽  
Renata Pisarek-Bartoszewska ◽  
Per Engelseth
Keyword(s):  
Spatial Data ◽  
Online Shopping ◽  
Statistical Data ◽  
Spatial Diversity ◽  
Regional Level ◽  
Spatial Data Analysis ◽  
Household Preferences ◽  
Global And Local ◽  
Data Analysis Methods ◽  
Local Spatial Autocorrelation

AbstractOwing to increased access to the Internet and the development of electronic commerce, e-commerce has become a common method of shopping in all countries. The purpose of this study is more precisely to research e-commerce diversity in Europe at the regional level and develop the conception of “E-commerce Supply Chain Management”. Statistical data derived from the European Statistical Office were applied to analyse the spatial diversity of e-retailing. Assessments of the regional diversity of e-retailing applied geographic information systems and exploratory spatial data analysis methods such us global and local spatial autocorrelation statistics. Clusters of regions with similar household preferences related to online shopping were identified. A spatial visualisation of the e-retailing diversity phenomenon may be utilised for the reconfiguration of supply chains and to adapt them to actual household preferences related to shopping methods.

The local spatial autocorrelation and the kernel method for identifying black zones

2003 ◽  
Vol 35 (6) ◽  
pp. 991-1004 ◽  
Author(s):  
Benoı̂t Flahaut ◽  
Michel Mouchart ◽  
Ernesto San Martin ◽  
Isabelle Thomas
Keyword(s):  
Spatial Autocorrelation ◽  
Kernel Method ◽  
Local Spatial Autocorrelation

scholarly journals The CoRisk-Index: Measuring economic risks related to COVID-19 in real time

2021 ◽  
Author(s):  
Fabian Braesemann ◽  
Fabian Stephany ◽  
Leonie Neuhäuser ◽  
Niklas Stoehr ◽  
Philipp Darius ◽  
...  
Keyword(s):  
Stock Market ◽  
Real Time ◽  
Language Processing ◽  
Economic Indicator ◽  
Economic Consequences ◽  
Economic Downturn ◽  
Related Risk ◽  
Unemployment Data ◽  
Using Data ◽  
Processing Techniques

Abstract The global spread of Covid-19 has caused major economic disruptions. Governments around the world provide considerable financial support to mitigate the economic downturn. However, effective policy responses require reliable data on the economic consequences of the corona pandemic. We propose the CoRisk-Index: a real-time economic indicator of Covid-19 related risk assessments by industry. Using data mining, we analyse all reports from US companies filed since January 2020, representing more than a third of all US employees. We construct two measures - the number of 'corona' words in each report and the average text negativity of the sentences mentioning corona in each industry - that are aggregated in the CoRisk-Index. The index correlates with U.S. unemployment data and preempts stock market losses of February 2020. Moreover, thanks to topic modelling and natural language processing techniques, the CoRisk data provides unique granularity with regards to the particular contexts of the crisis and the concerns of individual industries about them. The data presented here help researchers and decision makers to measure, the previously unobserved, risk awareness of industries with regard to Covid-19, bridging the quantification gap between highly volatile stock market dynamics and long-term macro-economic figures. For immediate access to the data, we provide all findings and raw data on an interactive online dashboard in real time.

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