EFRS:Enabling Efficient and Fine-Grained Range Search on Encrypted Spatial Data

2018 ◽  
Author(s):  
Guowen Xu ◽  
Hongwei Li ◽  
Yuanshun Dai ◽  
Jian Bai ◽  
Xiaodong Lin
Keyword(s):  
Spatial Data ◽  
Range Search ◽  
Fine Grained

Geo-DRS: Geometric Dynamic Range Search on Spatial Data with Backward and Content Privacy

2021 ◽  
pp. 24-43
Author(s):  
Shabnam Kasra Kermanshahi ◽  
Rafael Dowsley ◽  
Ron Steinfeld ◽  
Amin Sakzad ◽  
Joseph K. Liu ◽  
...  
Keyword(s):  
Spatial Data ◽  
Dynamic Range ◽  
Range Search

scholarly journals Graph-Based Spatial Data Processing and Analysis for More Efficient Road Lighting Design

2018 ◽  
Vol 10 (11) ◽  
pp. 3850 ◽  
Author(s):  
Sebastian Ernst ◽  
Marek Łabuz ◽  
Kamila Środa ◽  
Leszek Kotulski
Keyword(s):  
Spatial Data ◽  
Traditional Approach ◽  
Lighting Design ◽  
Prototype System ◽  
Real World Data ◽  
Fine Grained ◽  
Road Lighting ◽  
Calculation Software ◽  
Spatial Data Processing

The efficiency and affordability of modern street lighting equipment are improving quickly, but systems used to manage and design lighting installations seem to lag behind. One of their problems is the lack of consistent methods to integrate all relevant data. Tools used to manage lighting infrastructure are not aware of the geographic characteristics of the lit areas, and photometric calculation software requires a lot of manual editing by the designer, who needs to assess the characteristics of roads, define the segments, and assign the lighting classes according to standards. In this paper, we propose a graph-based method to integrate geospatial data from various sources to support the process of data preparation for photometric calculations. The method uses graph transformations to define segments and assign lighting classes. A prototype system was developed to conduct experiments using real-world data. The proposed approach is compared to results obtained by professional designers in a case study; the method was also applied to several European cities to assess its efficiency. The obtained results are much more fine-grained than those yielded by the traditional approach; as a result, the lighting is more adequate, especially when used in conjunction with automated optimisation tools.

scholarly journals Refining Coarse-Grained Spatial Data Using Auxiliary Spatial Data Sets with Various Granularities

2019 ◽  
Vol 33 ◽  
pp. 5091-5099 ◽  
Author(s):  
Yusuke Tanaka ◽  
Tomoharu Iwata ◽  
Toshiyuki Tanaka ◽  
Takeshi Kurashima ◽  
Maya Okawa ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Spatial Data ◽  
Coarse Grained ◽  
Model Parameters ◽  
Data Sets ◽  
Auxiliary Data ◽  
Fine Grained ◽  
Proposed Model ◽  
Spatial Data Sets ◽  
Target Data

We propose a probabilistic model for refining coarse-grained spatial data by utilizing auxiliary spatial data sets. Existing methods require that the spatial granularities of the auxiliary data sets are the same as the desired granularity of target data. The proposed model can effectively make use of auxiliary data sets with various granularities by hierarchically incorporating Gaussian processes. With the proposed model, a distribution for each auxiliary data set on the continuous space is modeled using a Gaussian process, where the representation of uncertainty considers the levels of granularity. The finegrained target data are modeled by another Gaussian process that considers both the spatial correlation and the auxiliary data sets with their uncertainty. We integrate the Gaussian process with a spatial aggregation process that transforms the fine-grained target data into the coarse-grained target data, by which we can infer the fine-grained target Gaussian process from the coarse-grained data. Our model is designed such that the inference of model parameters based on the exact marginal likelihood is possible, in which the variables of finegrained target and auxiliary data are analytically integrated out. Our experiments on real-world spatial data sets demonstrate the effectiveness of the proposed model.

scholarly journals Socio-spatial Disparities in Brussels and its Hinterland

2021 ◽  
pp. 271-291
Author(s):  
Rafael Costa ◽  
Helga A. G. de Valk
Keyword(s):  
Spatial Data ◽  
Local Level ◽  
Regional Scale ◽  
National Income ◽  
Socioeconomic Groups ◽  
Fine Grained ◽  
Spatial Disparities ◽  
Socioeconomic Segregation ◽  
Migration Flows ◽  
The City

AbstractBrussels’ urban and suburban landscape has changed considerably since the 1980s. The consolidation of socioeconomic fractures inside the city, a reinforcement of long-lasting disparities between the city and its prosperous hinterland, as well as the increasing diversification of migration flows—both high- and low-skilled—contributed to these disparities. Recent evolutions of these patterns, however, have not been investigated yet and therefore remain unknown. Besides, the extent to which segregation is primarily related to economic inequalities and to migration flows—or a combination/interaction between the two—so far has not been studied. This chapter offers a detailed overview of the socio-spatial disparities in the Brussels Functional Urban Area. Our analyses relied on fine-grained spatial data, at the level of statistical sections and of individualised neighbourhoods built around 100 m x 100 m grids. We analysed socioeconomic segregation measures and patterns, as well as their evolution between 2001 and 2011. Socioeconomic groups were defined based on individuals’ position with respect to national income deciles. In line with previous research, our results show very marked patterns of socioeconomic segregation in and around Brussels operating both at a larger regional scale and at the local level.

scholarly journals Fine Grained Food Image Segmentation through EADCNNs

2019 ◽  
Vol 9 (1) ◽  
pp. 212-218
Keyword(s):  
Spatial Data ◽  
Visual Recognition ◽  
Ground Truth ◽  
Training Model ◽  
Ground Truth Data ◽  
Fine Grained ◽  
Proposed Model ◽  
Validation Parameters ◽  
Food Image ◽  
Indian Food

-The recognition of Indian food can be considered as a fine-grained visual recognition due to the same class photos may provide considerable amount of variability. Thus, an effective segmentation and classification method is needed to provide refined analysis. While only consideration of CNN may cause limitation through the absence of constraints such as shape and edge that causes output of segmentation to be rough on their edges. In order overcome this difficulty, a post-processing step is required; in this paper we proposed an EA based DCNNs model for effective segmentation. The EA is directly formulated with the DCNNs approach, which allows training step to get beneficial from both the approaches for spatial data relationship. The EA will help to get better-refined output after receiving the features from powerful DCNNs. The EA-DCNN training model contains convolution, rectified linear unit and pooling that is much relevant and practical to get optimize segmentation of food image. In order to evaluate the performance of our proposed model we will compare with the ground-truth data at several validation parameters

A Territorial Dimension Can Be Useful for Managing Long-Term Regional Road Safety

2019 ◽  
pp. 248-267
Author(s):  
Alessandro Sciullo ◽  
Sylvie Occelli
Keyword(s):  
Spatial Data ◽  
Population Distribution ◽  
Local Level ◽  
Crash Data ◽  
Road Crashes ◽  
Fine Grained ◽  
Urban Settlements ◽  
Road Crash ◽  
Main Observation ◽  
Gis Methods

Analysis of road crashes at the local level is necessary for targeting and implementing effective countermeasures. This chapter presents a contribution to this task. It describes the research carried out in Piedmont, Italy, where an exploratory approach has been used to link road crash data with information about the spatial characteristics of urban settlements. The analytic strategy is developed in three steps. First, fine-grained spatial data for road crashes, land use, traffic counts, and population distribution are linked by GIS methods. Second, a selection of the data is implemented at the municipality level and processed through a cluster analysis to identify territorial accident profiles. Finally, to show their analytic potential, one case study is discussed that considers road segments as main observation units.

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