Abstract
Estimates of gross domestic product (GDP) play a significant role in evaluating the economic performance of a country or region. Understanding the spatiotemporal process of GDP growth is important for estimating or monitoring the economic state of a region. Various GDP studies have been reported, and several studies have focused on spatiotemporal GDP variations. This study presents a map spectrum-based clustering approach to analyze the spatiotemporal variation patterns of GDP growth. First, a sequence of nighttime light images (from the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS)) is used to support the spatial distribution of statistical GDP data. Subsequently, the time spectrum of each spatial unit is generated using a time series of dasymetric GDP maps, and then the spatial units with similar time spectra are clustered into one class. Each category has a similar spatiotemporal GDP variation pattern. Finally, the proposed approach is applied to analyze the spatiotemporal patterns of GDP growth in the Wuhan urban agglomeration. The experimental results illustrated regional discrepancies of GDP growth existed in the study area.
Highlights
The spatiotemporal patterns of gross domestic product (GDP) are considered significant indicators when evaluating the economic status of a region
This case suggests that a dasymetric map of GDP can be obtained based on the digital numbers (DNs) of calibrated night light images because these DNs reflect the levels of human activity, commercial activity, and industrial activity, which are related to GDP
Accuracy Assessment of the Dasymetric GDP Map based on County-Level GDP Statistics 5.1
Summary
The spatiotemporal patterns of gross domestic product (GDP) are considered significant indicators when evaluating the economic status of a region. Obtaining the precise spatial distributions of GDP in a specific region at different times and analyzing the associated spatiotemporal processes are crucial for assessing the economic status and growth of a region. Damiani et al [14] proposed a recent time-aware, density-based clustering technique and used it to study partial migrations These spatiotemporal analysis approaches commonly fail to effectively model spatiotemporally integrated processes. To investigate the characteristics of internal variations in GDP growth in the Wuhan urban agglomeration, GDP dasymetric maps of the study area from 1992 to 2012 were created using GDP statistical data and saturation-corrected nighttime light images obtained by the Defense Meteorological Satellite Program (DMSP)-Operational Linescan System (OLS).
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