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Location is an inherent part of business data: organizations maintain customer address lists, own property, ship goods from and to warehouses, manage transport flows among their workforce, and perform many other activities. A majority of these activities entail managing locations of different types of entities, including customers, property, goods, and employees. Those locations need not be static—in fact, they may continually change over time. For instance, goods are manufactured, packaged, and channeled to warehouses and retail/customer destinations. They may have different locations at various stages of the distribution network

In Chapter 1, you observed that spatial information can add value to a range of applications. You examined the benefits of storing spatial information with other data in the database

Consider a business application that stores information about its branches (or stores), customers, competitors, suppliers, and so on. If you location-enable such a business application, you can perform the following types of analysis:

In the previous chapter, we discussed how to location-enable application data and how to organize geographic data into multiple tables, each containing SDO_GEOMETRY columns. In this chapter, we focus on storing and modeling different types of location information using the SDO_GEOMETRY data type in Oracle. The SDO_GEOMETRY type can store a wide variety of spatial data, including the following:

In the previous chapter, we introduced a new data type called SDO_GEOMETRY to store spatial data. This data type can store a variety of spatial objects: points (including those obtained by geocoding address strings), line strings, polygons, or more complex shapes. Points primarily represent the locations of application-specific entities such as businesses, customers, or suppliers. Line strings and polygons, on the other hand, represent the boundaries of geographical entities such as roads, cities, or states. In CAD/CAM-type applications, line strings and polygons can represent different entities such as the layouts of buildings, printed circuit boards, or shapes of different parts of an automobile

In preceding chapters, we discussed how to perform spatial searches and analysis. In each example, the entities manipulated (customers, ATMs, stores, and so on) were spatially located. They all included an SDO_GEOMETRY column containing their spatial location using geographical coordinates (longitude and latitude).

So far, you have seen how to define and load spatial objects using the SDO_GEOMETRY type. You have also seen how to read spatial objects from SQL using SQL*Plus. In this chapter, we cover how to manipulate SDO_GEOMETRY types in the PL/SQL and Java programming languages

In previous chapters, we showed how to store location information in Oracle tables. We augmented existing tables, such as branches, customers, and competitors, with an SDO_GEOMETRY column to store locations of data objects. In this chapter, we describe how to use this spatial information to perform proximity analysis

In Chapter 8, we discussed how to perform proximity analysis using a spatial index and associated spatial operators. In this Chapter, we describe functions, which are also referred to as (whenever there is no ambiguity), which complement this functionality. In contrast to the spatial operators, these geometry processing functions

In the previous Chapters, we described geographical objects as points, lines, and polygons. In Chapters 8 and 9, you saw how to search geographical objects based on the way they are positioned with respect to other objects. In particular, you learned how to find objects that are within some distance from another object (with the SDO_WITHIN_DISTANCE operator) or simply to find the object nearest to another one (with the SDO_NN operator).