Spatial Data Transformation Research Articles

Spatial Data Transformation and Vision Learning For Elevating Intrusion Detection in IoT Networks

Spatial Data Transformation and Vision Learning For Elevating Intrusion Detection in IoT Networks

Mapping and identification of potential groundwater development zones of an alluvial aquifer in parts of Ghaggar and Upper Yamuna basins in India.

Using an integrated analytical hierarchy process, remote sensing and geographic information system techniques, the current study aims to map and identify the potential groundwater zones of Kurukshetra District of Haryana, which is located in the Ghaggar and Upper Yamuna Basins in India. This is done in the context of a significant change in the use of groundwater pattern, with respect to its continuously increasing demand due to the growing population, expansion of area under irrigation and related economic factors. The amount and quality of groundwater are anticipated to be impacted by anthropogenic activities as well as natural factors such as geomorphology, soil type, lithology and rainfall variance owing to a changing climatic scenario. The potential index of groundwater for this study was calculated by using nine important factors, including geomorphology, rainfall, soil type, depth to groundwater level, lithology, land use land cover, normalized difference vegetation index, cumulative sand thickness and elevation. The integration of multiple thematic layers was accomplished using the overlay weighted method to generate a potential groundwater zonation map and the accuracy of the resulting map was validated against a groundwater resource potential map. Statistical measures demonstrate an 82% agreement between the two maps, indicating a high level of concurrence. Accordingly, three groundwater zones of good, average and bad potential have been identified in the study area. In the current study, a process that combines weighted ranking with spatial data transformation and harmonization has been developed to obtain information for accurate decision-making. The results accruing from this research have significant ramifications for creating regional sustainable groundwater management plans.

An Approach for Efficient and Secure Data Encryption Scheme for Spatial Data

This article represents a dynamic grid system (DGS), a privacy grid system defined by the user. This is the primary all-encompassing secure and spatial data satisfying basic essential necessities for confidentiality-securing snapshot and location-based services (LBSs). First, secure and spatial data are responsible for achieving simple matching operation using a semi-trusted third party. The semi-trusted third party has no information about the location of the user. Second, under the defined adversary model, we can provide a secured snapshot and uninterrupted location-based services. Third, not beyond the proximity of the user???s area, the communication cost does not rely on others ideal confidentiality location; it depends on the number of pertinent salient activities. Fourth, despite these things, it has only been targeted on the range and our system that can effectively support different spatial queries without altering the algorithms that are kept running by the semi-reliable third parties and the database servers, given that the spatial query is abstracted into spatial regions within the desired search area. The experimental assessment shows a more efficient approach towards the dynamic grid structure than the progressive confidentiality technique for uninterrupted location-based services. We offer a dual spatial data transformation and encryption scheme in which encrypted requests are executed completely on the encrypted database by the provider, and the user gets encrypted results. To attain services found on their location, location-based services want users to consistently report their locations to a potentially unreliable server which may open them to security risks. Lamentably, there were many restrictions in the existing confidentiality-securing methods for location-based services, such as the trustworthy third party requirement, confidentiality restrictions and high overhead communication.

Spatial Differencing: Estimation and Inference

Spatial differencing (SD) is a spatial data transformation pioneered by Holmes (1998) increasingly used to estimate causal effects with non-experimental data. Recently, this transformation has been ...

Spectral–Spatial Rotation Forest for Hyperspectral Image Classification

Rotation Forest (RoF) is a recent powerful decision tree (DT) ensemble classifier of hyperspectral images. RoF exploits random feature selection and data transformation techniques to improve both the diversity and accuracy of DT classifiers. Conventional RoF only considers data transformation on spectral information. To overcome this limitation, we propose a spectral and spatial RoF (SSRoF), to further improve the performance. In SSRoF, pixels are first smoothed by the multiscale (MS) spatial weight mean filtering. Then, spectral–spatial data transformation, which is based on a joint spectral and spatial rotation matrix, is introduced into the RoF. Finally, classification results obtained from each scale are integrated by a majority voting rule. Experimental results on two datasets indicate the competitive performance of the proposed method when compared to other state-of-the-art methods.

아웃소싱 데이터베이스에서 데이터 프라이버시 및 질의 무결성을 지원하는 공간 변환 기법

Due to the popularity of location-based services, the amount of generated spatial data in daily life has been dramatically increasing. Therefore, spatial database outsourcing has become popular for data owners to reduce the spatial database management cost. The most important consideration in database outsourcing is meeting the privacy requirements and guarantying the integrity of the query result. However, most of existing database transformation techniques do not support both of the data privacy and integrity of the query result. To solve this problem, we propose a spatial data transformation scheme that utilizes the shearing transformation with rotation shifting. In addition, we described the attack models to measure the data privacy of database transformation schemes. Finally, we demonstrated through the experimental evaluations that our scheme provides high level of data protection against different kinds of attack models, compared to the existing schemes, while guaranteeing the integrity of the query result sets.

AN EFFICIENT ALGORITHM FOR MINING SPATIALLY CO-LOCATED MOVING OBJECTS

Mining co-location patterns from spatial databases may disclose the types of spatial features which ar e likely located as neighbors’ in space. Accordingly, we present an algorithm previously for mining spat ially co-located moving objects using spatial data mining techniques and Prim’s Algorithm. In the previous technique, the scanning of database to mine the spa tial co-location patterns took much computational c ost. In order to reduce the computation time, in this st udy, we make use of R-tree that is spatial data str ucture to mine the spatial co-location patterns. The importan t step presented in the approach is that the transf ormation of spatial data into the compact format that is wel l-suitable to mine the patterns. Here, we have adap ted the R-tree structure that converts the spatial data wit h the feature into the transactional data format. T hen, the prominent pattern mining algorithm, FP growth is us ed to mine the spatial co-location patterns from th e converted format of data. Finally, the performance of the proposed technique is compared with the prev ious technique in terms of time and memory usage. From the results, we can ensure that the proposed techniq ue outperformed of about more than 50% of previous algorithm in time and memory usage.

Evolution of coordination degree of eco-economic system and early-warning in the Yangtze River Delta

On the basis of Landsat TM data of the Yangtze River Delta (YRD) Economic Zone in 1991, 2001 and 2008, this article, taking 90 counties in this region as study units, built spatial data transformation models, ecosystem service value (ESV) and coordination degree of eco-economic system (CDES) models. With the aid of ArcGIS9.3, mass grid and vector data has been processed for spatial analyses. ESV and CDES indexes have demonstrated the relationship between economic development and eco-environment system and its evolution characteristics in the researched areas. Furthermore, the indexes have also been used for functional zoning and pattern recognition. Some results can be shown as follows. Firstly, since 1991, land use in the YRD has greatly changed: urban land area has increased primarily from original paddy land, dry land, grassland, garden plot and other land. Secondly, the ESV model has proved the deterioration trend of the YRD ecological system from 1991 to 2001 and slower degradation trend during 2001–2008. Also, it is illustrated that land-use conversion from water area and paddy field to urban area and dry land could cause great damage to ecosystem stabilization. Thirdly, GDP in the central and southern parts of the YRD is higher than that in the northern part since 1991. GDP growth rate in the central part is higher than that in the northern part during 1991–2001. This growth rate in the central part is also higher than that in the southern and northern parts of the YRD from 2001 to 2008. Fourthly, the YRD could be categorized into 12 types of subregions in terms of CDES index. According to its spatial characteristic of CDES index value in the study area, eco-economic conflict area with low CDES value which is located in the central part is surrounded by eco-coordinated areas with high CDES values. This illustrates a core-periphery spatial structure exists in the YRD. During 1991–2001, the CDES value implied the convergent deterioration trend of eco-economic system in the study area; while it gradually stepped into coexistence of divergent deterioration and coordination during 2001–2008. Finally, this paper analyzed five subregions in the YRD, including initially degrading zone, initially coordinative zone, continuously degrading zone, coordination-declined zone and coordination-promoted zone, based on eco-economic coordination and evolution patterns. And these subregions can be recognized and categorized by spatial transformation model.

Transformation of Spatial Data to a New Zone System: A Survey of US Metropolitan Planning Organizations

This study reports on a survey of Metropolitan Planning Organizations (MPOs) in the USA to collect information about spatial data transformation, a geographic procedure used to convert data from one set of zones (system A) to a different set of zones (system B), where systems A and B have incompatible zone boundaries. The survey reveals that 60% of MPOs perform spatial data transformation more than once per year, usually using the simplest, most error-prone methods. Given a lag in application of new GIS techniques among planners, it is perhaps not surprising that practising planners tend to favor simpler methods of spatial data transformation, despite the widely documented shortcomings of such techniques in the literature. These findings suggest that the gap between research and application in the utilization of GIS in planning practice will require both more advanced training of planners using GIS, and, perhaps more importantly, greater sophistication among the ‘consumers' of GIS planning analyses—non-GIS planners, public officials, and the general public.

Feature detection using spikes: The greedy approach

A goal of low-level neural processes is to build an efficient code extracting the relevant information from the sensory input. It is believed that this is implemented in cortical areas by elementary inferential computations dynamically extracting the most likely parameters corresponding to the sensory signal. We explore here a neuro-mimetic feed-forward model of the primary visual area (VI) solving this problem in the case where the signal may be described by a robust linear generative model. This model uses an over-complete dictionary of primitives which provides a distributed probabilistic representation of input features. Relying on an efficiency criterion, we derive an algorithm as an approximate solution which uses incremental greedy inference processes. This algorithm is similar to ‘Matching Pursuit’ and mimics the parallel architecture of neural computations. We propose here a simple implementation using a network of spiking integrate-and-fire neurons which communicate using lateral interactions. Numerical simulations show that this Sparse Spike Coding strategy provides an efficient model for representing visual data from a set of natural images. Even though it is simplistic, this transformation of spatial data into a spatio-temporal pattern of binary events provides an accurate description of some complex neural patterns observed in the spiking activity of biological neural networks.

Signal detection with superdirective circular arrays operating in isotropic noise

The Fourier‐Bessel transformation of spatial data from a circular array is used as a basis for the study of the characteristics of super‐directive circular arrays operating in isotropic noise. The resulting transform variables, known as kth order circle processes, are used to calculate the array gain of such an array. For the case of planewave signals with wavelength much larger than the radius of the array, the detection performance of nondirectional parametric and nonparametric detectors can be determined analytically.