scholarly journals A Grid-Based Approach for Measuring Similarities of Taxi Trajectories

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3118
Author(s):  
Wei Jiao ◽  
Hongchao Fan ◽  
Terje Midtbø
Keyword(s):  
Spatial Data ◽  
Behavior Pattern ◽  
Spatial Data Mining ◽  
Spatial Data Analysis ◽  
Exact Matching ◽  
The Road ◽  
Data Volume ◽  
Grid Codes ◽  
Position Prediction ◽  
Pass Through

Similarity measurement is one of the key tasks in spatial data analysis. It has a great impact on applications i.e., position prediction, mining and analysis of social behavior pattern. Existing methods mainly focus on the exact matching of polylines which result in the trajectories. However, for the applications like travel/drive behavior analysis, even for objects passing by the same route the trajectories are not the same due to the accuracy of positioning and the fact that objects may move on different lanes of the road. Further, in most cases of spatial data mining, locations and sometimes sequences of locations on trajectories are most important, while how objects move from location to location (the exact geometries of trajectories) is of less interest. For the abovementioned situations, the existing approaches cannot work anymore. In this paper, we propose a grid aware approach to convert trajectories into sequences of codes, so that shape details of trajectories are neglected while emphasizing locations where trajectories pass through. Experiments with Shanghai Float Car Data (FCD) show that the proposed method can calculate trajectories with high similarity if these pass through the same locations. In addition, the proposed methods are very efficient since the data volume is considerably reduced when trajectories are converted into grid-codes.

DATA ANALYTICS IN SPATIAL EPIDEMIOLOGY: A SURVEY

2016 ◽  
Vol 78 (10) ◽  
Author(s):  
Sharmila Banu Kather ◽  
BK Tripathy
Keyword(s):  
Soft Computing ◽  
Spatial Data ◽  
Data Science ◽  
Rough Set Theory ◽  
Spatial Epidemiology ◽  
Spatial Data Mining ◽  
Computing Methods ◽  
Spatial Data Analysis ◽  
Survey Paper ◽  
Soft Computing Techniques

Spatial data analysis is being used efficiently and the governments have realized that georeferenced data yields more insight with time and locations. Epidemiology is about the study of origin and distribution of diseases and dates back to the 1600s with the instance of cholera in London. Data Science has been evolving and when analyzed with Soft Computing techniques like Rough Set Theory (RST), Fuzzy Sets, Granulation Computing which encompasses the data in its original nature, results can be obtained with accrued accuracy. This survey paper highlights Spatial Data Mining methods used in the field of Epidemiology, identifies crucial challenges and discusses of the use of Soft Computing methods. 

scholarly journals Data Dissemination Techniques using DBSCAN and DD-Rtree for Spatial Data Mining

2020 ◽  
Vol 8 (5) ◽  
pp. 4465-4470
Keyword(s):  
Data Processing ◽  
Spatial Data ◽  
Data Dissemination ◽  
Clustering Algorithms ◽  
Research Work ◽  
Three Dimensional ◽  
Spatial Data Mining ◽  
X Ray Crystallography ◽  
Dynamic Level ◽  
Data Volume

In today’s scenario where data volumes are growing on enormous speed over cloud or internet, we want to limit this growing data size. This can be achieved by data processing methods where data processing can be done in parallel. To make the data processing done in parallel, various clustering sampling methodologies are in use such as Slink, DBSCAN, and Optics and so on. The power accomplished by various methodologies which already exist will be focusing to the preservation of three-dimensional surroundings such as grid tree, grid files, quad tree and tree like k-d-tree, etc. This all compartmentalization constructions are generally done in static way which is a fix way. Since this data volume size is very big, this results in a high cost of information sharing and clustering. Hence through this research work we want to analyze various clustering algorithms both on static level and at dynamic level. For doing this we are majorly comparing the dynamic distribution using DBSCAN and DD-Rtree algorithm by proposing a DD-Rtree will help us to preserver the spatial vicinity. In addition, DD-Rtree is not static but more than that it is dynamic, i.e. it will create build the data as we progress with clustering. DD-Rtree methodologies are based on R-Tree concepts which analyses the data at dynamic random way. We tend to compare DD-RTree’s information distribution norm with one of the clustering system recently published, DBSCAN. On the side of the potential of DBSCAN formula, we tend to distinguish the potential of queries managed by these compartmentalization structures. Numerous applications requires such kind of implementation at dynamic level of spatial database system such as satellite images, X-Ray crystallography, metrological department or other such atomic equipment’s spatial datasets. Our research work will help to implements spatial data dynamically using DDR-tree mechanism.

scholarly journals A Comparative Analysis of Environmental Justice Between Urban Neighbourhoods

2021 ◽  
Vol 1 (2) ◽  
pp. 27-40
Author(s):  
Maikudi Mohammed ◽  
Keyword(s):  
Environmental Justice ◽  
Spatial Data ◽  
Open Data ◽  
Sampling Technique ◽  
Spatial Data Analysis ◽  
Digital Tool ◽  
The Road ◽  
Urban Neighbourhoods ◽  
High Level ◽  
Open Data Kit

Inequality and spatial disparities in the provision of amenities and services is a serious problem that often expressed itself in the realms of social and environmental justice. This study investigated the level of environmental justice or otherwise in the provision of amenities and services in selected Northern and Southern neighbourhoods of Minna. Qualitative and quantitative data were acquired through physical surveys and direct observations of amenities and facilities to aid identification of salient features while a checklist was used to guide the research and converting field data to a digital tool using the Open Data Kit (ODK) tool. With purposive sampling technique, descriptive statistic, spatial data analysis and the Gini coefficient; it was shown that injustice in the provision of environmental amenities is both a cause and effect of distributive and procedural inequities across space. Although there is a moderate level of environmental justice (GC=0.33) on the aggregate in the study area, this is not even across sampled amenities, as there is a low level of equality in the provision of schools (GC=0.51). However, the provision of facilities like water boreholes (GC=0.37) and waste dumpsites (GC=0.28) performed fairly with moderate environmental justice; it is, worrisome to note that access roads recorded a high level of inequality (GC=0.19) – particularly as Bosso town with 40% of the residents of the metropolis only has 20% of access roads, while, Tudun Wada South is having 22% of the road for just 7% of the population. The study, therefore, recommended equitable construction of more access roads across the neighbourhoods; imbibing equity and justice in the provision of more water boreholes and waste dumpsites to help institute more sustainable development and management in Minna and similar cities.

Spatial Data Mining for Highlighting Hotspots in Personal Navigation Routes

2012 ◽  
Vol 8 (3) ◽  
pp. 45-61 ◽  
Author(s):  
Gabriella Schoier ◽  
Giuseppe Borruso
Keyword(s):  
Network Structure ◽  
Spatial Data ◽  
Road Network ◽  
Spatial Clustering ◽  
Clustering Algorithms ◽  
Analytical Techniques ◽  
Spatial Data Mining ◽  
The Road ◽  
Gps Traces ◽  
Insight Into

Rapid developments in the availability and access to spatially referenced information in a variety of areas have induced the need for better analytical techniques to understand the various phenomena. In particular, the authors’ analysis is an insight into a wealth of geographical data collected by individuals as activity dairy data. The attention is drawn on point datasets corresponding to GPS traces driven along a same route in different days. In this paper, the authors explore the presence of clusters along the route, trying to understand the origins and motivations behind that to better understand the road network structure in terms of ’dense’ spaces along the network. Therefore, the attention is focused on methods to highlight such clusters and see their impact on the network structure. Spatial clustering algorithms are examined (DBSCAN) and a comparison with other non-parametric density based algorithm (Kernel Density Estimation) is performed. Different tests are performed over the urban area of Trieste (Italy), considering both multiple users and different origin/destination journeys.

A method for efficient clustering of spatial data in network space

2021 ◽  
pp. 1-18
Author(s):  
Trang T.D. Nguyen ◽  
Loan T.T. Nguyen ◽  
Anh Nguyen ◽  
Unil Yun ◽  
Bay Vo
Keyword(s):  
Spatial Data ◽  
Euclidean Distance ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Spatial Data Mining ◽  
Spatial Data Analysis ◽  
Clustering Methods ◽  
Dbscan Algorithm ◽  
Density Based Clustering ◽  
Network Space

Spatial clustering is one of the main techniques for spatial data mining and spatial data analysis. However, existing spatial clustering methods primarily focus on points distributed in planar space with the Euclidean distance measurement. Recently, NS-DBSCAN has been developed to perform clustering of spatial point events in Network Space based on a well-known clustering algorithm, named Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The NS-DBSCAN algorithm has efficiently solved the problem of clustering network constrained spatial points. When compared to the NC_DT (Network-Constraint Delaunay Triangulation) clustering algorithm, the NS-DBSCAN algorithm efficiently solves the problem of clustering network constrained spatial points by visualizing the intrinsic clustering structure of spatial data by constructing density ordering charts. However, the main drawback of this algorithm is when the data are processed, objects that are not specifically categorized into types of clusters cannot be removed, which is undeniably a waste of time, particularly when the dataset is large. In an attempt to have this algorithm work with great efficiency, we thus recommend removing edges that are longer than the threshold and eliminating low-density points from the density ordering table when forming clusters and also take other effective techniques into consideration. In this paper, we develop a theorem to determine the maximum length of an edge in a road segment. Based on this theorem, an algorithm is proposed to greatly improve the performance of the density-based clustering algorithm in network space (NS-DBSCAN). Experiments using our proposed algorithm carried out in collaboration with Ho Chi Minh City, Vietnam yield the same results but shows an advantage of it over NS-DBSCAN in execution time.

scholarly journals Development of Multilayer-Based Map Matching to Enhance Performance in Large Truck Fleet Dispatching

2021 ◽  
Vol 10 (2) ◽  
pp. 79
Author(s):  
Ching-Yun Mu ◽  
Tien-Yin Chou ◽  
Thanh Van Hoang ◽  
Pin Kung ◽  
Yao-Min Fang ◽  
...  
Keyword(s):  
Data Structures ◽  
Spatial Data ◽  
Spatial Information ◽  
Data Retrieval ◽  
Spatial Data Mining ◽  
Map Matching ◽  
Matching Algorithm ◽  
Table Method ◽  
Look Up Table ◽  
The Look

Spatial information technology has been widely used for vehicles in general and for fleet management. Many studies have focused on improving vehicle positioning accuracy, although few studies have focused on efficiency improvements for managing large truck fleets in the context of the current complex network of roads. Therefore, this paper proposes a multilayer-based map matching algorithm with different spatial data structures to deal rapidly with large amounts of coordinate data. Using the dimension reduction technique, the geodesic coordinates can be transformed into plane coordinates. This study provides multiple layer grouping combinations to deal with complex road networks. We integrated these techniques and employed a puncture method to process the geometric computation with spatial data-mining approaches. We constructed a spatial division index and combined this with the puncture method, which improves the efficiency of the system and can enhance data retrieval efficiency for large truck fleet dispatching. This paper also used a multilayer-based map matching algorithm with raster data structures. Comparing the results revealed that the look-up table method offers the best outcome. The proposed multilayer-based map matching algorithm using the look-up table method is suited to obtaining competitive performance in identifying efficiency improvements for large truck fleet dispatching.

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