Enabling effective location-based services for road networks using spatial mining

G Kiran Kumar; M Venu Gopalachari; Rupesh Mishra; D Jayaram; S Rakesh; D. Malathi Rani

doi:10.53730/ijhs.v6nS4.9313

Authors

G Kiran Kumar Assistant Professor, CSE Department, Chaitanya Bharathi Institute of Technology, Hyderabad, India
M Venu Gopalachari
mvenugopalachari_it@cbit.ac.in
Associate Professor, IT Department, Chaitanya Bharathi Institute of Technology, Hyderabad, India
Rupesh Mishra Assistant Professor, CSE Department, Chaitanya Bharathi Institute of Technology, Hyderabad, India
D Jayaram Assistant Professor, IT Department, Chaitanya Bharathi Institute of Technology, Hyderabad, India
S Rakesh Assistant Professor, IT Department, Chaitanya Bharathi Institute of Technology, Hyderabad, India
D. Malathi Rani Assistant Professor, ECE Department, Marri Laxman Reddy Institute of Technology and Management, Hyderabad, India

Keywords:

spatial data, co-location patterns, road network distance, decision making, SVM, decision tree, random forest, Naïve Bayes

Abstract

A co-location pattern represents a subset of Boolean spatial attributes whose instances are located in a close geographic space. These patterns are important for location-based services. There are many methods for co-location pattern mining where the distance between the events in close geographic proximity is calculated using a straight-line distance called Euclidean distance. Since most of the real-time tasks are bounded to the road networks, the results computed using Euclidean distance is not appropriate. So to compute co-location patterns involving network we define a model where initially a network model is defined and the neighbourhood is obtained by using network distance. By comparing this approach with the previous Euclidean approach, the results obtained for co-location patterns on a road network are accurate. Our experimental results for synthetic and real data show that the proposed approach is efficient and accurate for identifying co-location patterns involving network entities.

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