How to effectively search and query map information

Classification of search methods

Retrieval is the process of determining the address of the objects in the space from the database based on the given conditions. The practicality of a database system depends largely on its retrieval function.
Based on the nature of the given search conditions, you can divide the image data retrieval into two types: regular search and topology search. The nature and further classification of the two types of search are as follows:
1. Regular search: search based on the logo of the spatial object. It is divided:
(1) qualitative search: search by the topic nature of the spatial object.
(2) location search: search by the geometric location of the spatial object. Generally, the search is based on the rectangle range given by the user. It is also called window search.
2. topology search: search based on the spatial relationship between spatial objects. Spatial relationships include: Topological joining, topological joining, and Topological inclusion.
Topology search is mainly used in line networks (border networks, traffic networks, and river networks ). The meaning of topology search is: to give an element of a curve network, that is, a node, an arc segment, or a polygon, it is required to select a batch of elements of the curve network.
Topology search can be divided:
(1) Direct topology search: search based on the topological relationship explicitly indicated. This method provides a high retrieval speed.
(2) Indirect topology retrieval: Topology retrieval is based on the relationship between spatial objects instead of the symbols of spatial objects.

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In order to better understand the content, we will give a brief introduction to explicit and implicit representations.
Explicit Representation: The relationships between points, arcs, and polygon are clearly indicated. A Deterministic model.
Implicit representation: Relationships between points, arc segments, and polygon graphic entities are sometimes deterministic and sometimes approximate (such as fuzzy) relationships. These relationships are dynamic and flexible. They do not need to be specifically established, but are hidden in the data structure.

6.2 topology search

String 5

 

6.2.1 direct topology searchString 7

1. Simple topology search
Simple topology search can be classified into the following four types:
(1) determine the arc segments (1 element) on the given node (0-dimensional element), which are represented by operators.
(2) determine the node at the endpoint of the given arc segment, expressed by an operator.
(3) determine the arc segments of the given polygon (2d element), which are represented by operators.
(4) determine the Polygon on both sides of a given arc segment, represented by an operator. Operator indicates that dimension space objects can be exported by performing operations on dimension space objects. String 3

Figure 6-1 adjacent polygon set operator sequence of polygon
The above calculation is based on the display topology Association established between the Arc Segment and the node and the Arc Segment and the polygon. You can also establish a topological relationship between a node and a polygon. Generally, because the existing data structure does not establish a direct association between the node and the polygon, therefore, in some special cases (that is, when there are more than three sides merged on a node), it is incomplete to query the adjacent relationship of a polygon. For example, in Figure 6-1, if the adjacent polygon set of polygon A is determined simply based on the relationship operators of edges and polygon, only B, C, D, F are obtained, the polygon e is omitted. The reason is that the query is based on an operator, rather than expressing the association between a node and a polygon. However, if a direct association between a node and a polygon is established in the data structure, a large amount of storage space will be occupied. Currently, the solution to this problem is implemented through a program. The arc segment is obtained from (a) and obtained from the node. The obtained edge and the side of a polygon are obtained after an exclusive or operation, finally, the adjacent polygon set of polygon A is B, C, D, E, and F. String 7
2. Compound topology search
Through the series and repeated applications of the above simple topology retrieval, a more complex topology retrieval can be obtained. Composite topology search mainly involves the following three basic adjacent relationships.
(1) The basic adjacent node: the implementation steps are as follows:
1) determine the arc segments associated with the node ();
2) determine the polygon () associated with the obtained Arc Segment Set ().
(2) The Arc Section is basically adjacent: the implementation steps are as follows:
1) determine the node associated with the Arc Segment ();

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2) determine the arc segments associated with the two nodes ();
3) determine the polygon () associated with the obtained Arc Segment Set ().
(3) Basic adjacent to a polygon: the implementation steps are as follows:
1) determine the Arc Segment Set associated with the polygon ();
2) determine the node set associated with the obtained Arc Segment Set ();

String 7

3) determine the Arc Segment Set associated with the obtained Node Set ();
4) determine the polygon set () associated with the obtained Arc Segment Set ().

String 7

 

6.2.2 indirect topology searchString 7

The implementation of indirect topology retrieval is divided into two sub-processes: pre-check and related retrieval.
1. Pre-Search: form a set of objects in the starting space.
The purpose of pre-search is to provide the first set of spatial objects for topology search-the set of starting spatial objects. Three types of pre-retrieval can be performed here.
1) retrieve the set of point space objects, such as residential areas;
2) retrieve a collection of objects in a linear space, such as a transportation line;
3) a collection of objects in a plane-like space, for example, a number of adjacent provincial or county-level district units.
Pre-retrieval is basically achieved through qualitative retrieval (such as classification and classification encoding.
2. Search: obtain the final set of objects in the target space.
The related search here is based on the first set of spatial objects provided by the Pre-search, computing and logical comparison are used to determine the second set of space objects-the set of objects in the target space, so that the Set and the set of objects in the starting space meet the proposed topological relationship.
Generally, if the object set in the target space belongs to a fuzzy set, the object set in the target space is determined by means of location and retrieval. If the object set in the target space is a deterministic set, the qualitative search method is used to form the set of objects in the target space. String 5

6.3 application of topology search

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1. Point-based topology search
Here, the set of point space objects (such as important residential areas) obtained in the pre-search is used as the basis for topology search. For example, you can retrieve the linear spatial objects (railway and highway objects) associated with point space objects ).
2. topology search based on line Sets
For example, you can use a transportation line to query the surrounding villages and the administrative area line to query the information of adjacent political areas.
3. Polygon-based topology search
This mainly involves the application of topological joining relationships. Answer:
(1) areas around Beijing;
(2) If several political district units are regarded as economic cooperation zones of some form, the neighboring provinces of the Cooperation Zone will be determined as such provinces.

String 8

 

6.4 query spatial information in GIS

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1. Spatial Feature-Based Query
Currently, the query function of most mature commercial Geographic Information System software can perfectly implement simple query of spatial objects. For example, based on the space position pointed by the mouse, the system can find the space objects and their attributes at the location, display the attribute list of the space objects, or perform related statistical analysis.
2. Query Based on Attribute features
Generally, the query operations based on attribute information are mainly completed in the attribute database. Currently, most GIS software stores attribute information in relational databases, and mature relational databases provide us with a complete data indexing method and information query method. Almost all relational database management systems support standard structured query languages (SQL ). Using SQL, we can easily implement composite condition query of attribute information in the attribute database, and filter the ID values of space objects that meet the conditions, then, retrieve the space object based on the ID value in the graphic database.
3. Spatial link-based Attribute feature Query
There are multiple spatial relationships (such as topological relationships, order relationships, and measurement relationships) between spatial objects ). In practical applications, users often want GIS to provide functions that can directly calculate spatial object relationships. If you want to retrieve cities that meet the following conditions:
(1) in the East of a railway;
(2) the railway cannot exceed 30 kilometers;
(3) The city has a population greater than 0.7 million. String 8
(4) the area selected by the city is a specific polygon. The whole query calculation involves the spatial order relationship (east of the railway) and spatial distance relationship.
(No more than 30 kilometers away from the railway), spatial topological relationship (selected city in a specific selection area), and attribute information query (the urban population is greater than 0.7 million ). As for the current mature geographic information system, it is still difficult to systematically complete the above GPS query tasks. Therefore, many geographic information system experts have proposed the "Spatial Query Language" (spatial query language) as a solution to the problem, but it is still in the theoretical development and technical exploration phase. Word