Spatio-temporal association analysis of county potential in the Pearl River Delta during 1990–2009

Abstract

According to the highway data and some socioeconomic data of 1990, 1994, 2000, 2005 and 2009 of county units in the Pearl River Delta, this paper measured urban integrated power of different counties in different years by factor analysis, and estimated each county's potential in each year by means of expanded potential model. Based on that, the spatio-temporal association patterns and evolution of county potential were analyzed using spatio-temporal autocorrelation methods, and the validity of spatio-temporal association patterns was verified by comparing with spatial association patterns and cross-correlation function. The main results are shown as follows: (1) The global spatio-temporal association of county potential showed a positive effect during the study period. But this positive effect was not strong, and it had been slowly strengthened during 1994-2005 and decayed during 2005-2009. The local spatio-temporal association characteristics of most counties' potential kept relatively stable and focused on a positive autocorrelation, however, there were obvious transformations in some counties among four types of local spatio-temporal association (i.e., HH, LL, HL and LH).(2) The distribution difference and its change of local spatio-temporal association types of county potential were obvious. Spatio-temporal HH type units were located in the central zone and Shenzhen-Dongguan region of the eastern zone, but the central spatio-temporal HH area shrunk to the Guangzhou-Foshan core metropolitan region only after 2000; the spatio-temporal LL area in the western zone kept relatively stable with a surface-shaped continuous distribution pattern, new LL type units emerged in the south-central zone since 2005, the eastern LL area expanded during 1994-2000, but then gradually shrunk and scattered at the eastern edge in 2009; the spatio-temporal HL and LH areas varied significantly. (3) The local spatio-temporal association patterns of county potential among the three zones presented significant disparity, and obvious difference between the eastern and central zones tended to decrease, whereas that between the western zone and the central and eastern zones further expanded.(4) Spatio-temporal autocorrelation methods can efficiently mine the spatio-temporal association patterns of county potential, and can better reveal the complicated spatio-temporal interaction between counties than ESDA methods.

Data Association at the Level of Narrative Plots to Support Analysis of Spatiotemporal Evolvement of Conflict: A Case Study in Nigeria

“…Compared with our approach, some investigations depend on geo-spatial or spatiotemporal association analysis to study, such as the relationship between adverse birth outcomes and arsenic in the groundwater [32] and the county potential [35]. These parametric modeling approaches [40,44,46] focus on a few data members, whereas our framework can associate more extensive data types that include more than spatial and temporal data.…”

“…A timeline-based data organization has been used for the analysis of successive impacts [34]. A cross-correlation function provides a perspective regarding studying the relationships between cities in a regional urban system [35]. However, these approaches are unsuitable for modeling unclear objects, and they must mine notable items in a preprocessing step through the use of data mining techniques [36], e.g., the Apriori association rule mining [37].…”

Bayesian Space–Time Analysis of Brain Cancer Incidence in Southern Ontario, Canada: 2010–2013

“…NT . We note that w ijt−1 = w ij due to the temporal invariance amongst adjacent regions of the LHIN areal units [48].…”

The Spatiotemporal Pattern of the Aerosol Optical Depth (AOD) on the Canopies of Various Forest Types in the Exurban National Park: A Case in Ningbo City, Eastern China

“…e second step was inversion of the AOD by using the dark pixel method because of the high vegetation cover of the study area. e detailed processes include four substeps [39,40]: (1) the dark pixels, which are usually in the dense vegetation covered areas with short wavelength and dark surface, were identified by the NDVI; (2) since the apparent reflectance of 2.1-2.2 μm wavelength is almost independent of aerosol [41][42][43], the short infrared band was chosen to calculate the surface reflectance; (3) the optimal AOD was determined by matching the 6 S LookUp Table (LUT) and apparent reflectance and the mean AOD mean values of red and blue band were obtained; (4) the Kriging interpolation was applied to the preliminary inversion results which were resampled to 30 m [44]; and (5)the AOD inversion results in this study were compared with MOD04_3K data to verify the inversion accuracy. In our study, For this substep, we firstly registered Landsat AOD and MOD04_3K data which were taken at the same time (two sets of data at the same time), then calculated the average value of the corresponding mesh of Landsat AOD according to the mesh size of MOD04_3K, counted the number of effective pixels, and finally counted the linear regression equation and R 2 .…”