An overview is presented of the use of spatial data structures in spatial databases. The focus is on hierarchical data structures, including a number of variants of quadtrees, which sort the data with respect to the space occupied by it. Suchtechniques are known as spatial indexing methods. Hierarchical data structures are based on the principle of recursive decomposition. They are attractive because they are compact and depending on the nature of the data they save space as well as time and also facilitate operations such as search. Examples are given of the use of these data structures in the representation of different data types such as regions, points, rectangles, lines, and volumes.

Algorithms are presented for moving between adjacent blocks in an octree representation of an image. Motion is possible in the direction of a face, edge, and a vertex, and between blocks of arbitrary size. The algorithms are based on a generalization and simplification of techniques developed earlier for two dimensions (i.e., in quadtrees). They are also applicable to quadtrees. The difference lies in the graph-theoretical classification of adjacencies-i.e., in terms of vertices, edges, and faces. Algorithms are given for octrees that are implemented with pointers and with pointerless representations such as the linear octree. 0 1989 Academic PRSS. ITIC. 1.

A method, termed approximate splitting, is proposed to model the node distri-bution that results when the PR quadtree is used to store point data drawn from a uni-form distribution. This method can account for the aging and phasing phenomena which are common in most hierarchical data structures. Approximate splitting is also shown to be capable of being adapted to model the node distribution of the PR quad-tree with points drawn from a known non-uniform distribution.

The recent introduction of the Apple iPhone 5 and the accompanying iOS6 software environment which, among other changes, replaced the use of the Google Maps API in iOS5 by Apple’s own MapsAPI,hasledtosignificantchangesintheuserexperiencewith apps that make use of maps and has resulted in closer scrutiny of mapping applications on mobile devices. Many of these changes in the user experience deal with the quality of the data that is being produced and presented to the user, and has led to a wide ranging discussion of data quality and the seeming lack of quality assurance policies and protocols by Apple. These are widely documented inwebpostings. However, equally important are significant changes in the manner in which the data is presented to the user, but, surprisingly, not much attention has been paid to this aspect of the user experience which is somewhat analogous to the concept of the “last mile ” when discussing the bandwidth of communications networks and its associated costs. The changes in the presentation and in the amount of data that are presented to the user on the Apple mapping platform, with an emphasis on mobile devices with a small form factor such as smartphones, are tabulated and compared along with other mapping platforms such as the iOS apps of ESRI, MapQuest, and OpenSeaMap (using the open source map data of OpenStreetMap), as well as Bing Maps and Nokia Maps for which no iOS app exists and thus the corresponding mobile web versions are used. Categories andSubject Descriptors

The database architecture and system design of NewsStand, a database system that analyzes and displays streaming news using a map user interface, is described. Special emphasis is given to News-Stand’s pipe server, which coordinates individual, independent analysis modules in a processing pipeline, and NewsStand’s relational database schema, designed to accommodate responsive spatial querying and retrieval via NewsStand’s user interface. Examples of these spatial queries, which are variants of top-k window queries, are also presented. Experiments on the live NewsStand database system demonstrate its capability for rapidly processing large amounts of streaming news as well as the interactivity of its map user interface as measured by database querying. Categories and Subject Descriptors

TweetPhoto utilizes a map query interface to display news photos from news articles that are extracted from the tweets of 2,000 Twitter users who have been determined to post news related content. These articles are then geotagged and clustered so that a set of locations are associated with a cluster and its associated images. For each of these locations, the images are scored based on the terms associated with the location and the image’s caption. This work differs from traditional work in this area as all topic and location extraction is automated without the need for user entered content or GPS coordinates. Categories andSubjectDescriptors

Disease outbreaks are intimately tied to geographic locations and to times, and as a result, health-related GIS along with open, Web-based data sources are increasingly crucial for public health. One such data source, ProMED-mail, offers disease reports distributed as email postings, along with locations and times of relevance. Locations are specified in text rather than in geometry, which necessitates a method for mapping textual locations to their spatial representations, called geotagging. To address this need, the previously-developed STEWARD system is leveraged for disease detection and tracking by geotagging ProMED-mail postings. While STEWARD was previously used in a disease tracking role, improvements to STEWARD are described including an innovative time slider that allows powerful and intuitive spatio-textual querying. Many additional future improvements for STEWARD and related systems are also discussed. Categories and Subject Descriptors

Many spatial applications require the ability to display locations of data entries on an online map. For example, an online photosharingservicemaywishtodisplayphotosaccordingtowherethey weretaken.Sincemanyphotos canoccupy thesameareaandoverlap each other within a display window, less popular or older images (based on a given measure of importance) can be discarded so that these more popular or newer photos become more distinct. A straightforward solution to this problem is (i) to use a window query to retrieve data entries within a given display window; (ii) to discard data entries in proximity of a more important one. This method works well in a high spatial selectivity setting, e.g., when thewindowqueryreturnsasmallnumberofentries,buttheperformance drastically degrades as the spatial selectivity decreases. We considerthisproblemasselectingdistinctdataentriesfromagiven dataset, where the “distinctiveness ” of a data entry depends on its relative importance in comparison to that of other data entries in proximity. In this paper, we propose a new query type called the multi-resolution select-distinct (MRSD) query. The main novelty of our query processing method is a voting system built upon an ensemble of interrelatedindexes, whichallowsus toefficientlydetermine the degree of distinctiveness of all points within a query window.Usingarealdatasetofover9millionlocations,ourexperimental results show that our proposed method is capable of consistently producing subsecond response times, while the window query-based method takes more than 10 seconds on average in a low spatial selectivitysetting. Categories andSubject Descriptors