Background
Spatial data refers to data that includes a spatial attribute which denotes a position, shape, or size in a specific space such as physical or digital environments. This data is intrinsic to the analysis of spatial patterns and processes in various fields such as economics, geography, urban planning, and environmental studies.
Historical Context
The study of spatial data has evolved from traditional cartography and geography to more sophisticated forms with the advent of Geographic Information Systems (GIS) and spatial econometrics. Early analysis focused on mapping attributes to specific locations, while modern approaches leverage advanced statistical methods to identify spatial patterns and relationships.
Definitions and Concepts
Spatial data differs from typical datasets in its inclusion of spatial dependency and heterogeneity:
- Spatial Dependence: Refers to the relationship where the value of a variable at one location depends on values of the same variable at nearby locations.
- Spatial Heterogeneity: Describes the variation of a variable across different locations encapsulating non-uniformity and often different statistical properties.
Major Analytical Frameworks
Classical Economics
Spatial data used in classical economics mainly involved the study of location theories (Weber’s least-cost theory) and exploration of transportation costs in manufacturing location.
Neoclassical Economics
Neoclassical economics involves spatial data in understanding market mechanics and phenomena such as the regional disparity of incomes and asset prices, applying much-advanced econometric analysis.
Keynesian Economics
The emphasis is more on spatial differences in aggregate demand and their influence on regional investment flows.
Marxian Economics
Focuses on how spatial relations reflect broader socio-economic inequalities and uses spatial data to analyze the spatial dimensions of capital accumulation and geographic distributions of labor.
Institutional Economics
Studies the spatial distributions of institutional efficacy and interactions in economics by accounting for spatial factors in the examination of institutional impacts and development trajectories.
Behavioral Economics
Uses spatial data to study patterns and deviations in human behaviors influenced by geographic and spatial attributes.
Post-Keynesian Economics
Considers spatial dependency in analyzing the effects of public policies and the regional propagation of economic shocks.
Austrian Economics
Smaller focus but includes spatial analysis of market processes and entrepreneurial research considering location implications.
Development Economics
Utilizes spatial data extensively to investigate the spatial aspects of poverty, development levels, natural resource distributions, and the effectiveness of regional policies.
Monetarism
Examines spatial variations through the effect of central bank policies on different regions, with interest in spatial data’s depiction of differentiated regional inflation/deflation dynamically.
Comparative Analysis
Spatial data integration highlights discrepancies and unique patterns across different spatial clusters, crucial for micro and macroeconomic policy formulations. Classical and neoclassical models tend to be more abstract and general, whereas spatial econometrics provide a more refined, nuanced analysis indispensable to contemporary economic challenges.
Case Studies
Investigations examining real estate markets, regional economic growth differences, urbanization impacts, and environmental hazards often employ spatial datasets and methods to present precise, contextually rich insights.
Suggested Books for Further Studies
- “Spatial Data Analysis in the Social and Environmental Sciences” by Robert Haining
- “Applied Spatial Data Analysis with R” by Roger S. Bivand, Edzer Pebesma, and Virgilio Gómez-Rubio
- “Fundamentals of Spatial Data Quality” by Rodolphe Devillers and Robert Jeansoulin
Related Terms with Definitions
- Spatial Autocorrelation: It quantifies the degree to which a set of spatial data points is correlated to itself over a given spatial range.
- Heteroscedasticity: The presence of non-constant variability in the errors or residuals of a model.
- Spatial Structural Breaks: Instances where spatial relationships or dependencies undergo substantial changes across space.
