Abstract—Geo-spatial data becomes more and more large amount of data on the Web. On the other hand, managing massive spatial data is one of challenges for supporting spatial queries and high performance computational is also needed to support spatial queries. Thus there is needed to solve this criteria is to create a better spatial indexing method. The proposed method is to create Grid-based R-tree index structure for k nearest neighbour query and range query. R-tree is constructed with Minimum Bounding Rectangle (MBR) that contains a group of objects. The proposed system is combined R-tree with grid index that is reduced overlapping and covering area. The proposed system is to support spatial queries efficiently and also supports speed up computational performance. Index Terms—K nearest neighbor search, R-tree, grid-index, LBS. I. INTRODUCTION The increasing of inter-network technologies and Internet of Things (IoTs) devices has become commonly useful for consumers on their mobile smart aware devices. Thus the quality of spatial data is needed to support on their real-time location via location-based services (LBS). Google Maps Services and Social networking services (SNS), such as Twitter and Facebook are provided online users to reveal their location during emergencies, retrieve information about nearby restaurants and hotel, and acquire local traffic. The current growth of LBS services, companies is boosting their efforts to automatically locate consumers for advertising and marketing purposes. LBS software services are also considered for mobile applications to represent a new business sector and E-commerce. Meanwhile, retrieval of k-Nearest Neighbour (k-NN) efficiently and quickly requires an informative and effective index structure that can reduce the search space. Spatial index is used by spatial databases to optimize spatial queries. Spatial databases are fully-fledged databases that, in addition, enable the storage, retrieval, manipulation, querying, and analysis of geometries like points, lines, and retrieval, manipulation, querying, and analysis of geometries like points, lines, and regions representing, for example, the geometries of cities, roads, and states respectively. In order to handle spatial data efficiently, database management system needs an index mechanism that will output the data quickly according to their current location. Manuscript received June 25, 2019; revised October 12, 2019. Aung Zaw Myint is with Faculty of Computing, University of Computer Studies, Yangon (UCSY), Myanmar (e-mail: aungzawmyint@ucsy.edu.mm). Khin Mo Mo Tun is with the Faculty of Computing, University of Information Technology, Myanmar (e-mail: khinmomotun@uit.edu.mm). There are many index structures such as K-D-B tree works only in points data, B- tree, R-tree, Hilbert-Curve tree, BSP-tree and Quard-tree. Most spatial database application uses R-tree indexing method because it is the most widely accessed method [1]. This study proposes a new algorithm to speed up the performance of k-Nearest Neighbour (k-NN) retrieval on spatial database. The proposed method named as R-tree based grid index is a hybrid index structure of R-tree and grid indexing technique [2]. Grid index is used for locating objects. As grid index is easy in implementations such as updating and creating index, it is simple and efficient way of spatial indexing. The grid index extracts only locations from nearest indices and sends theses indices to R-tree. R-tree is used to retrieve nearest objects [3]. Additionally, the remainder of this paper is organized as follows. In Section II, we describe related works of this paper. In Section III, we describe background theory. In Section IV, we discuss the overview of proposed system. In Section V, we explain computing grid index. In Section VI, we describe construction of R-tree. In Section VII and VIII, we discuss our expected experimental and then conclude the paper. II. RELATED WORKS Processing k-Nearest Neighbour (KNN) query based on location has been well studied in spatial database. R-tree method was proposed by Guttman in 1984 in order to handle spatial data efficiently, as required in computer aided design and geo-data applications and to extract the objects by current location [4], [5]. Zhang et al. proposed a grid cell based continuous k-NN query processing method (CkNN) [6].Inverted file-R* tree and R* tree – inverted file are geo-textual indices that loosely combine the R* tree and inverted file [7]. Hariharan, B. Hore, C. Li,S. Mehorotra [8] proposed the KR*-tree structure that captures the join distribution of keywords in space and significantly improves performance. The query performance of grid index is robust while updating positions of the objects [9]. The proposed index used Distributed grid index from server transmission and clients examine the received index with the unique ID number to each grid-cell [10]. Z. Li,K.C. K. Lee, B. Zheng,W.C. Lee,D.LLee,X [2] proposed IR-tree. This paper proposed to support efficient geographic document and IR-tree enables the pruning of textually and spatially irrelevant subsets. As to k-NN query algorithm [3], its system has studied well in traditional databases. The idea of this system is to establish a static R-tree-like structure which is developed from Rtree. It cannot handle continuous queries and update Grid-Based Spatial Index Method for Location-Based Nearest Neighbour Search Aung Zaw Myint and Khin Mo Mo Tun International Journal of Future Computer and Communication, Vol. 9, No. 2, June 2020 40 doi: 10.18178/ijfcc.2020.9.2.563