Spatial Autocorrelation in Spatial Interaction

2009 ◽  
pp. 221-237 ◽  
Author(s):  
Daniel A. Griffith
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Interaction

scholarly journals Spatial Interaction Model for Healthcare Accessibility: What Scale Has to Do with It

2020 ◽  
Vol 12 (10) ◽  
pp. 4324
Author(s):  
Felipa de Mello-Sampayo
Keyword(s):  
Spatial Autocorrelation ◽  
Multilevel Modeling ◽  
Regression Models ◽  
Spatial Interaction ◽  
Healthcare Providers ◽  
Interaction Model ◽  
Local Variation ◽  
Spatial Interaction Model ◽  
Healthcare Accessibility ◽  
Spatial Autoregressive

This manuscript develops a theoretical spatial interaction model using the entropy approach to relax the assumption of the deterministic utility function. The spatial healthcare accessibility improves as the demand for healthcare increases or the opportunity cost of traveling to and from healthcare providers decreases. The empirical application used different spatial econometric techniques and multilevel modeling to evaluate the spatial distribution of existing hospitals in Texas and their social and economic correlates. To control for spatial autocorrelation, spatial autoregressive regression models were estimated, and geographically weighted regression models examined potential spatial non-stationarity. The multilevel modeling controlled for spatial autocorrelation and also allowed local variation and spatial non-stationarity. The empirical analysis showed that healthcare accessibility was not stationary in Texas in 2015, with areas of poor accessibility in rural and peripheral areas in Texas, when using hospitals’ location and county data. The model of spatial interaction applied to healthcare accessibility can be used to evaluate policies aiming at the provision of health services, such as closures of hospitals and capacity increases.

Spatial Pattern Evolution of Coal Consumption at Provincial Level in China

2013 ◽  
Vol 734-737 ◽  
pp. 1752-1756
Author(s):  
Kai Yong She ◽  
Wen Jun Chen
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Data ◽  
Hot Spots ◽  
Spatial Interaction ◽  
Interaction Mechanism ◽  
Spatial Data Analysis ◽  
Management Policy ◽  
Coal Consumption ◽  
Cold Spots ◽  
Consumption Activity

Exploratory Spatial Data Analysis was used to analyze the evolvement of spatia1 pattern on coal consumption in China since 2002. General spatial autocorrelation of coal consumption in 31 provinces of China was analyzed by Morans I and Getis-Ord General G. Getis-0rd Gi* was used to test the local spatial dependence, identifying the spatial distribution of hot spots and cold spots. The results show that coal consumption per capita of 31 provinces in China exhibits an enhanced trend of spatial autocorrelation. The areas with similar level of coal consumption are clustered in space. The coal consumption activity can be affected by the neighborhoods and their own regions. Hotspot areas are mainly concentrated in North and Northeast China and continuously increase with time, coldspot areas are mainly concentrated in South China and constantly decrease by time. So government needs to consider the spatial interaction mechanism of coal consumption when establishing the energy management policy.

Spatial Autocorrelation

Geography ◽  
2016 ◽  
Author(s):  
Dan Griffith
Keyword(s):  
School Districts ◽  
Spatial Autocorrelation ◽  
Spatial Econometrics ◽  
Spatial Interaction ◽  
Relevant Literature ◽  
Common Factor ◽  
Barometric Pressure ◽  
Entire Family ◽  
Geographic Space ◽  
Eigenvector Spatial Filtering

Spatial autocorrelation (SA)—the correlation among georeferenced observations arising from their relative locations in geographic space—has a history dating to the mid-1900s, although conceptual awareness of it dates back to the early 1900s. But SA is everywhere. It manifests itself in one- and two-dimensional synchronizations, exemplified by the experiment involving multiple metronomes sitting on a board that rests on two soda cans (illustrating an indirect, common factor SA source), or the aggregate flashing of fireflies created by their emission into the air of chemicals that stimulate nearby fireflies to light (illustrating a direct spatial interaction SA source). This latter outcome also can arise from mimicking behavior, as occurs with bandit bumble bees in a single meadow (i.e., when they rob a yellow rattler’s flower of nectar, their entry holes side in a given field tend to be unambiguously on only its left or right hand). The degree of organization in the geographic patterns that emerge signifies the level of positive SA. Such SA resulted in Heckscher discovering a new species of firefly in 2013. This type of SA is the basis of Nobel winner Schelling’s models of segregation, and The Economist (20 April 2013, p. 16) stating that regardless of class in Britain, geographical clusters of voters act like “political opinions derive from the air people breathe.” Moderate positive SA characterizes slider puzzles, magnetic sculpture toys, and television pictures. Meanwhile, negative SA relates to spatial patterns of competition. Although this nature of SA is rarely encountered in practice, it is illustrated by the Grand Prairie Independent School District’s (GPISD) attempts to increase the amount of money it receives from the state of Texas by holding annual events to attract students from surrounding school districts to attend its schools (Dallas Morning News, 9 January 2014); GPISD attempts to increase its enrollments by decreasing enrollments in its neighboring school districts. A timeline for the evolution of the SA concept helps establish its historically relevant literature. In the early 1800s, Laplace recognized autocorrelation—albeit serial for time series—by acknowledging that between day variations in barometric pressure readings tend to be much greater than within day readings. From 1914 to 1935, spatial series observational correlations were recognized by Student, then Yule, then both Stephan and Neprash, and then Fisher. This recognition set the stage for establishing the concept of SA. Moran and Geary did so in the early 1950s. In parallel, writing in French, Matheron and Krige also did so within the context of geostatistics. Next, more formal models of SA were formulated, first by Whittle, then by Mead, and finally by Cliff and Ord, whose numerous publications popularized the concept in the 1970s. One outcome of Cliff and Ord’s work was the coining of the phrase spatial econometrics in 1979 by Paelinck and Klaassen. Finally, as the century drew to a close, Griffith established the foundation of eigenvector spatial filtering, which extends SA analysis to the entire family of non-normal random variables.

Dynamic Models of Spatial Autocorrelation

1977 ◽  
Vol 9 (5) ◽  
pp. 505-519 ◽  
Author(s):  
L Hordijk ◽  
P Nijkamp
Keyword(s):  
Spatial Autocorrelation ◽  
Dynamic Models ◽  
Spatial Dispersion ◽  
Spatial Interaction ◽  
Growth Theory ◽  
Foreign Workers ◽  
Spatial Interaction Models ◽  
Statistical Measures ◽  
Diffusion Analysis ◽  
And Diffusion

Dynamic models have been studied intensively during the last decade, particularly in the field of growth theory and diffusion analysis. Consequently, problems like temporal autocorrelation have received much attention. Recently the attention of econometricians has also concentrated on spatial autocorrelation, particularly in the field of spatial interaction models. The existence of spatial autocorrelation among spatially dispersed phenomena appears to lead to significant differences in the treatment of multiregional models. In this paper, attention will be focused on the formal aspects of dynamic spatial diffusion models. Some statistical measures for temporal-spatial autocorrelation will be constructed. In addition, a formal derivation of necessary extensions in estimation procedures for dynamic multiregional econometric models will be presented, particularly in the field of seemingly unrelated regressions and instrumental variables. The analysis will be illustrated by means of a dynamic explanatory model for the spatial dispersion of foreign workers in The Netherlands.

scholarly journals Does Economic Geography Matter for Pakistan? A Spatial Exploratory Analysis of Income and Education Inequalities.

2011 ◽  
Vol 50 (4II) ◽  
pp. 929-953 ◽  
Author(s):  
Sofia Ahmed
Keyword(s):  
Income Inequality ◽  
Spatial Autocorrelation ◽  
Human Development ◽  
Spatial Clustering ◽  
Spatial Interaction ◽  
Exploratory Analysis ◽  
Spatial Effects ◽  
Spatial Disparities ◽  
Development Levels ◽  
Economic Inequalities

Generally, econometric studies on socio-economic inequalities consider regions as independent entities, ignoring the likely possibility of spatial interaction between them. This interaction may cause spatial dependency or clustering, which is referred to as spatial autocorrelation. This paper analyses for the first time, the spatial clustering of income, income inequality, education, human development, and growth by employing spatial exploratory data analysis (ESDA) techniques to data on 98 Pakistani districts. By detecting outliers and clusters, ESDA allows policy makers to focus on the geography of socio-economic regional characteristics. Global and local measures of spatial autocorrelation have been computed using the Moran‘s I and the Geary‘s C index to obtain estimates of the spatial autocorrelation of spatial disparities across districts. The overall finding is that the distribution of district wise income inequality, income, education attainment, growth, and development levels, exhibits a significant tendency for socio-economic inequalities and human development levels to cluster in Pakistan (i.e. the presence of spatial autocorrelation is confirmed). Keywords: Pakistan, Spatial Effects, Spatial Exploratory Analysis, Spatial Disparities, Income Inequality, Education Inequality, Spatial Autocorrelation

scholarly journals FORECASTING URBAN POPULATION DISTRIBUTION OF ILOILO CITY USING GIS AND SPATIAL AUTOCORRELATION MODELS

2021 ◽  
Vol XLVI-4/W6-2021 ◽  
pp. 185-192
Author(s):  
L. Hilario ◽  
J. A. Duka ◽  
M. I. Mabalot ◽  
J. Domingo ◽  
K. A. Vergara ◽  
...  
Keyword(s):  
Spatial Autocorrelation ◽  
Population Distribution ◽  
Smart Cities ◽  
Spatial Interaction ◽  
Rapid Urbanization ◽  
Urban And Regional Planning ◽  
Factors Affecting ◽  
Explanatory Variables ◽  
Goods And Services ◽  
Population Forecasts

Abstract. Rapid urbanization in localities offers a lot of opportunities but also imposes a lot of challenges due to its direct relationship to population growth. This leads to an increase in the demand for essential goods and services such as food, energy, water among others. Hence, small-area population forecasts have long been an important element in urban and regional planning to aid in the decision-making processes in a locality. The promise of smart cities, through the use of advanced technologies, is to make cities livable and sustainable, preparing more opportunities and addressing challenges on urbanization. This study aims to forecast population distribution in Iloilo city by incorporating GIS techniques and highlighting the use of spatial autocorrelation models. The spatial interaction effects between neighboring barangays are taken into consideration to identify a set of factors affecting the population. The results identified a set of significant explanatory variables and whether it will result in an increase or decrease in population. The study also illustrates the resulting population forecast comparing it to the actual total population of the city.

Sign in / Sign up

Export Citation Format

Share Document