Analysis of land subsidence changes on the Beijing Plain from 2004 to 2015

Land subsidence, as a surface response to the development, utilization and evolution of underground space, has become a global and multidisciplinary complex geological environment problem. Since the 1960s, land subsidence has been developing rapidly in the Beijing Plain area. Against the backdrop of the integration of Beijing, Tianjin and Hebei in addition to “southern water” (South-to-North Water Diversion Project, SNWDP) entering Beijing, the systematic study of the evolution mechanism of land subsidence is of great significance for the sustainable development of the regional economy. Firstly, this study used ENVISAT ASAR and RADARSAT-2 data to obtain surface deformation information for the Beijing Plain area from 2004 to 2015 and then verified the results. Secondly, the study area was divided into units using a 960 m×960 m grid, and the ground settlement rate of each grid unit from 2004 to 2015 was obtained. Finally, the Mann–Kendall test was performed on the grid to obtain the mutation information for each grid unit. Combined with hydrogeology and basic geological conditions, we have attempted to analyze the causes of the mutations in the grid. The results show that 2347 grid cells were mutated in a single year, with most of these distributed across the Yongding River alluvial fan and the middle and lower parts of the Chaobai River alluvial fan. A total of 1128 grid cells were mutated in multiple years, with the majority of these cells mainly distributed across the upper-middle area of the alluvial fan, near the emergency water source and at the edge of the groundwater funnel. This study aims to provide favorable technical support and a scientific basis for urban construction in Beijing.

Spatial Proximity-Based Geographically Weighted Regression Model for Landslide Susceptibility Assessment: A Case Study of Qingchuan Area, China

Landslides pose a serious threat to the safety of human life and property in mountainous regions. Susceptibility assessment for landslides is critical in landslide management strategy. Recent studies indicate that the traditional assessment models in many previous studies commonly assume a fixed relationship between influencing factors and landslide occurrence within an area, resulting in an inadequate evaluation for the local landslides susceptibility. To address this issue, in this paper we propose a spatial proximity-based geographically weighted regression (S-GWR) model considering spatial non-stationarity of landslide data for assessing the landslide susceptibility. Spatial proximity is the basic input condition for the proposed S-GWR model. The challenge lies in defining the spatial proximity expression that shows the geographical features of landslides and therefore affects the model ability of S-GWR. Our solution chooses the slope unit as spatial adjacency, rather than the grid unit in DTM. The multicollinearity between landslide influencing factors is then eliminated through variance inflation factor (VIF) method and principal component analysis (PCA). The proposed model is subsequently validated by using data in Qingchuan County, southwestern China. Spatial non-stationary is identified for landslide data. A comparison with grid unit and four traditional evaluation models is conducted. Validation results using the area under the ROC (receiver operating characteristic) curve and success rate curve indicate that the spatial proximity-based GWR model with slope unit has the highest predictive accuracy (0.859 and 0.850 respectively).

Viticultural landscapes: Localised transformations over the past 150 years through an analysis of three case studies in Slovakia

The transformation of vineyard landscapes is evaluated in this article by assessing the changes in land cover and landscape diversity in selected study areas in two time periods – from 1867 to 1949, and from then to 2016. The study areas are characterised by a long history of viticulture and with important occurrences of old and new agrarian relief forms. Fine-scale land cover and landscape diversity analysis, as well as the study of historical and strategic documents, enabled an accurate interpretation of the viticultural landscape trajectories and their drivers. Landscape diversity was computed using the Shannon diversity index for each 625 square metre grid unit, and applying other metrics for the entire study area. Our research established that the study areas oscillated during this period between extensification and agricultural intensification, and the general trend confirmed the disappearance of traditional vineyards and a decline in modernised vineyard areas after socialism. Although extensification and intensification are seemingly contradictory processes, it is established that these both increase landscape diversity. In addition, landscape diversity changes in the second period are influenced more by changes in quantitative landscape pattern characteristics via edge density than qualitative patterns, e.g. patch richness, which reflect land use diversity.

Design of Energy Management System using Autonomous Hybrid Micro-Grid under IOT Environment

In this paper, the automated power consumption unit is implemented by smart system technology. The interfacing could be done between the user and power sensing unit in which the collection of data is segregated and processed by the common interface bridge unit. Then, the data is transmitted to the power grid unit where the the processing and sensing power consumption is executed for the consumer in aspects of bill generation. All this kind of environment is controlled by the Internet of Things (IoT) substances and regulated by the configured power supplies. The entire setup wire diagram is simulated using Lab VIEW. The another advantage of the proposed system is equipped with highly cyber security point

Run length coding and efficient compression of hexagonal raster data based on Gosper curve

<p><strong>Abstract.</strong> Compared with regular quadrilateral grid, regular hexagonal grid is isotropy and has higher cell compactness and sampling density. This gives regular hexagonal grid advantages in visual display, spatial analysis, and many other aspects. However, the studies of raster data mainly focus on regular quadrilateral grid, and various encoding methods are also focused on it. The researches on hexagonal raster data are relatively insufficient.</p><p>In this paper, encoding and compression for regular hexagonal grid are studied. By introducing Gosper curve which has good spatial aggregation and takes into account the morphological structure of regular hexagonal grid, the bidirectional correlation between Gosper curve and regular hexagonal grid is established. Then, a new encoding framework is built to determine the Gosper coding of each grid unit. The lossless compression is completed by performing run-length coding on adjacent coding sets in the target region.</p>

Spatiotemporal Influence of Urban Environment on Taxi Ridership Using Geographically and Temporally Weighted Regression

Taxicabs play an important role in urban transit systems, and their ridership is significantly influenced by the urban built environment. The intricate relationship between taxi ridership and the urban environment has been explored using either conventional ordinary least squares (OLS) regression or geographically weighted regression (GWR). However, time constitutes a significant dimension, particularly when analyzing spatiotemporal hourly taxi ridership, which is not effectively incorporated into conventional models. In this study, the geographically and temporally weighted regression (GTWR) model was applied to model the spatiotemporal heterogeneity of hourly taxi ridership, and visualize the spatial and temporal coefficient variations. To test the performance of the GTWR model, an empirical study was implemented for Xiamen city in China using a set of weekday taxi pickup point data. Using point-of-interest (POI) data, hourly taxi ridership was analyzed by incorporating it to various spatially urban environment variables based on a 500 × 500 m grid unit. Compared to the OLS and GWR, the GTWR model obtained the best performance, both in terms of model fit and explanatory accuracy. Moreover, the urban environment was revealed to have a significant impact on taxi ridership. Road density was found to decrease the number of taxi trips in particular places, and the density of bus stops competed with taxi ridership over time. The GTWR modelling provides valuable insights for investigating taxi ridership variation as a function of spatiotemporal urban environment variables, thereby facilitating an optimal allocation of taxi resources and transportation planning.