A parallel computing approach to fast geostatistical areal interpolation

<div><p>Areal interpolation is the procedure of using known attribute values at a set of (source) areal units to predict unknown attribute values at another set of (target) units. Geostatistical areal interpolation employs spatial prediction algorithms, that is, variants of Kriging, which explicitly incorporate spatial autocorrelation and scale differences between source and target units in the interpolation endeavor. When all the available source measurements are used for interpolation, that is, when a global search neighborhood is adopted, geostatistical areal interpolation is extremely computationally intensive. Interpolation in this case requires huge memory space and massive computing power, even with the dramatic improvement introduced by the spectral algorithms developed by Kyriakidis <i>et al</i>. (<a href="#CIT0027" target="_blank">2005</a>. Improving spatial data interoperability using geostatistical support-to-support interpolation. <i>In</i>: <i>Proceedings of geoComputation.</i> Ann Arbor, MI: University of Michigan) and Liu <i>et al</i>. (<a href="#CIT0028" target="_blank">2006</a>. <i>Calculation of average covariance using fast Fourier transform</i> (<i>FFT</i>). Menlo Park, CA: Stanford Center for Reservoir Forecasting, Petroleum Engineering Department, Stanford University) based on the fast Fourier transform (FFT). In this study, a parallel FFT-based geostatistical areal interpolation algorithm was developed to tackle the computational challenge of such problems. The algorithm includes three parallel processes: (1) the computation of source-to-source and source-to-target covariance matrices by means of FFT; (2) the QR factorization of the source-to-source covariance matrix; and (3) the computation of source-to-target weights via Kriging, and the subsequent computation of predicted attribute values for the target supports. Experiments with real-world datasets (i.e., predicting population densities of watersheds from population densities of counties in the Eastern Time Zone and in the continental United States) showed that the parallel algorithm drastically reduced the computing time to a practical length that is feasible for actual spatial analysis applications, and achieved fairly high speed-ups and efficiencies. Experiments also showed the algorithm scaled reasonably well as the number of processors increased and as the problem size increased.</p> </div

A time series of urban extent in China using DSMP/OLS nighttime light data

<div><p>Urban extent data play an important role in urban management and urban studies, such as monitoring the process of urbanization and changes in the spatial configuration of urban areas. Traditional methods of extracting urban-extent information are primarily based on manual investigations and classifications using remote sensing images, and these methods have such problems as large costs in labor and time and low precision. This study proposes an improved, simplified and flexible method for extracting urban extents over multiple scales and the construction of spatiotemporal models using DMSP/OLS nighttime light (NTL) for practical situations. This method eliminates the regional temporal and spatial inconsistency of thresholding NTL in large-scale and multi-temporal scenes. Using this method, we have extracted the urban extents and calculated the corresponding areas on the county, municipal and provincial scales in China from 2000 to 2012. In addition, validation with the data of reference data shows that the overall accuracy (OA), Kappa and F1 Scores were 0.996, 0.793, and 0.782, respectively. We increased the spatial resolution of the urban extent to 500 m (approximately four times finer than the results of previous studies). Based on the urban extent dataset proposed above, we analyzed changes in urban extents over time and observed that urban sprawl has grown in all of the counties of China. We also identified three patterns of urban sprawl: Early Urban Growth, Constant Urban Growth and Recent Urban Growth. In addition, these trends of urban sprawl are consistent with the western, eastern and central cities of China, respectively, in terms of their spatial distribution, socioeconomic characteristics and historical background. Additionally, the urban extents display the spatial configurations of urban areas intuitively. The proposed urban extent dataset is available for download and can provide reference data and support for future studies of urbanization and urban planning.</p></div

Spatial distribution of analysis on the urban sprawl pattern in China.

<p>Spatial distribution of analysis on the urban sprawl pattern in China.</p

Urban extents of cities in China extracted in this study.

<p>(a) the Beijing-Tianjin-Tangshan district, (b) the Pearl River Delta, (c) the Yangtze River Delta, (d) Urumqi in the Xinjiang Uygur Autonomous Region, (e) Zhengzhou in Henan Province.</p

Accuracy assessment of the extracted urban extents using different P values in 2000.

Accuracy assessment of the extracted urban extents using different P values in 2000.</p

Analysis of urban sprawl in Guangzhou.

<p>(a) The urban extents in Guangzhou; (b) Curves displaying the area and ratio of urban sprawl in Guangzhou, (c) Curves displaying the rate and acceleration of urban sprawl in Guangzhou.</p

TR2RM: an urban road network generation model based on multisource big data

Road networks are an important part of transportation infrastructure through which people experience a city. The existing methods of vector map data generation mainly depend on a single data source, e.g. images, trajectories, or existing raster maps, which are limited by information fragmentation due to incomplete data. This study proposes an urban road network extraction framework named trajectory and remote-sensing image to RoadMap (TR2RM) based on deep learning technology by combining high-resolution remote sensing images with big trajectory data; this framework is composed of three components. The first component focuses on feature map generation by fusing remote sensing images with trajectories. The second component is composed of a novel neural network architecture denoted as AD-LinkNet, which is used to identify roads from the fused dataset of the first component. The last component is a postprocessing step that aims to generate the vector map accurately. Taking Rome, Beijing, and Wuhan as examples, we conduct extensive experiments to verify the effectiveness of the TR2RM. The results showed that the correctness of both the topology and geometry of the generated road network based on the TR2RM in Rome, Beijing, and Wuhan was 83.86% and 88.27%, 74.72% and 80.36%, and 73.83% and 77.7%, respectively.</p

Trend of the urban area series in China.

<p><b>(</b>A) The urban area using the raw NTL dataset and the proposed temporal and spatial normalization; (B) The urban area using the intercalibrated NTL dataset and the proposed temporal and spatial normalization.</p

Flowchart of the proposed urban extents extraction model.

<p>Flowchart of the proposed urban extents extraction model.</p

Accuracy assessment of the extracted urban extents at the county, municipal, and provincial scales in 2000.

<p>Accuracy assessment of the extracted urban extents at the county, municipal, and provincial scales in 2000.</p