Introduction To Geographic Data Science - Spatial Databases Postgis

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Introduction Of Geographic Data Science - Spatial Databases Postgis

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The planning, designing, and implementation of a geographic information system (GIS design) involves a number of tasks, including feasibility analysis, requirement identification, conceptual and thorough database management system, and software and hardware selection. GIS analysts have recently become interested in and have started using system design methodologies that were adapted from software engineering, such as the software life-cycle model, which outlines the system design duties stated above. In addition, the following task has been executed with the help of software called PGadmin. Moreover, the scripting language that has been used in the following database task is PostgreSQL.

Part A: ER modeling

There are many tools available to enable conceptual database modeling. The entity-relationship modeling method, created by a writer by the name of Chen, is one that is generally acknowledged and used. Numerous scholars have improved and expanded the entity-relationship (E-R) model to fulfill a range of particular demands. It has been applied to many areas. The E-R methodology is a graphical way for depicting the objects of a database system, all significant relationships in between entities along with all attributes from either entities as well as relationships that need to be recorded in the database. The description of entities, connections, and qualities as well as how they are represented diagrammatically are controlled by the set of rules. Each item is given a name, and the diagram also includes details about the relationships between them, including their cardinality. However, the ER modeling comprises various parameters that are described in the later section;

  • Entities
  • Specific Relationships in between entities
  • Specific attributes of entities

There are rules for creating a unique graph such as, an ER model of a specific database system using the three fundamental symbols, each component of which has a graphic symbol. Relationships are shown as diamonds, relationships are shown as rectangles, and attributes are shown as ellipses (Limlahapun and Jongkroy, 2022).

 ERD diagram in PGadmin

(Source: Rahman et al. 2019)

The corresponding diagram is regarding the ER diagram that has been developed regarding the UK Healthcare providers. In addition, four types of tables have been developed with the help of database software called PGadmin. Moreover, the tables that has been provided for the following healthcare includes Doctors, Patients, Disease along with Clinic. However, all the tables have been filled with necessary attributes. In addition, the tables named DOCTORS have various kinds of attributes such as name, DOB, Specialty along with doctor_id. Moreover, a table called CLINIC has been filled with attributes such as, Address, Geolocation, Catchment area along with Clinic_id. The table named PATIENTS has been provided with attributes such as, Name, DOB, DOD, gender, geolocation along with patient_id as primary key. In addition, the final table called DISEASE have been provided with necessary attributes such as, Disease Name, Disease Severity, Disease Start date, Disease end date, Disease Speciality along with a primary key called Disase_id as primary key.

Many discrepancies or conflicts in the description of entities, relations, and attributes are discovered during the construction of an E-R diagram. Many of these are addressed during the building of the initial E-R diagram, while others are addressed through a sequence of transformations applied to the diagram after it has been created. There should be no definitional contradictions or inconsistencies in the final E-R diagram. An E-R diagram can be implemented directly into the physical and logical database schema of a relational, hierarchical, or networking type database if it is created correctly.

Geographical Data Models

The majority of modern GISs still use data models that are based on the perspective of the spatial or cartographic data objects. Though they have started to develop, other data models are still quite limited. Current and proposed spatial data models include:

  • The topologically encoded points, lines, and polygons in the cartographic model each have one or a few associated attributes, such as the polygonal representation of a land use layer that has a land utilised code.
  • Geometric objects with numerous properties, such as a census tract data set, are included in the expanded attribute geographic data mode.
  • Conceptual object or spatial data model: explicitly recognising user-defined objects, zero or more related spatial objects and sets of characteristics for user-defined objects (for instance, user-defined objects for a land parcel, a building, and an occupant, each with a unique set of attributes but with related potential spatial objects: a polygon for the land parcel, a footprint for the building, and no associated spatial object for the occupant).
  • Multiple items and numerous associated spatial objects make up conceptual objects or complex spatial objects (for instance, a roadway network with segments that are both line and polygonal in shape and intersections that are both point and polygonal in shape).

Part B: Spatial databases

The essential elements of the design process are outlined in somewhat abstract form in GIS design models, which explain the implementation processes for the life-cycle model. An updated version of earlier design models is shown in Figure 2. The current models are flawed in that they only describe high-level operations, despite being valuable as fundamental guidelines for GIS designers. Furthermore, these manuals typically don't offer any instructions on how to carry out the recommended design tasks. The conceptual and specific design processes for geographic databases are one part of such models that has not received enough additional clarity.

Figure 2: Life Cycle of GIS

(Source: Saraee and Silva, 2018)

Current GIS design approaches do not fully address the database design issue. On the grounds that adopting a commercial GIS format eliminates the need for further database design work, it is common for data of importance to simply be listed in tabular form. Modern GIS design methodologies relate the data required by the GIS to apps as well as GIS processes. These methods, however, provide little help with the concept or logical layout of the GIS database. Even now, costly database design mistakes can happen. The design of geographic databases requires more consideration.

 Design Model of GIS

(Source: Bartoszewski, Piorkowski and Lupa, 2019)

The majority of GIS deployments employ a GIS Software software package, frequently in conjunction with a commercialized database system. In certain situations, the complete physical schema as well as the basic form of the logical schema are already set. The objective of a GIS designer is to create a conceptual schema which accurately defines the complete GIS database and can be translated in a specific logical schema of a suggested GIS and database software.

Part C: Spatial queries

The method of extracting a data set from a single map by directly interacting with the mapping features is known as spatial query. Data is kept in tables and white and functional tables in a spatial database. SQL is a database "query" language created for retrieving data from relational databases. Although both scalar and vector information use boolean connections, raster deep contextual these connectors to separate raster dct coefficients from those other cell values inside one raster layer and link the specific cell value to cell values of many other raster layers. Due to the fact that spatial queries can hold numerous characteristics at once, they can only be used with a single vector file. The key feature that sets GIS apart from other visual information systems is spatial query. It includes the search for spatial objects depending on their spatial relationships with the other features. A nonspatial question would be something like "List the regions with populations over 500,000 inside the United States”. On the other hand, the query "List crime hot spots within 10 miles of downtown Minneapolis'' illustrates a spatial query because it uses a query "List high crime spots within 10 kilometers of central Minneapolis," on the other hand, is an illustration of a geographical query because it makes use of the spatial idea of distance.

Conclusion

In order to represent existing geographic databases, the author has provided a relatively straightforward but helpful addition to the fundamental Entity-Relationship data modeling technique. The extension can support the widely used geographic data models at the moment. Geographic data modeling tools will face increased demands as a result of the explosive rise of GIS applications. The modeling structure described here cannot handle the temporal presentation of geographic information and events with numerous geographic locations. These geographic data kinds, as well as probably others, will necessitate further additions to geographic data modeling techniques. Moreover, the software named PGadmin has been sued to perform the following analysis.

Reference

Bartoszewski, D., Piorkowski, A. and Lupa, M., 2019, May. The comparison of processing efficiency of spatial data for PostGIS and MongoDB databases. In International Conference: Beyond Databases, Architectures and Structures (pp. 291-302). Springer, Cham.

Limlahapun, P. and Jongkroy, P., 2022, June. Development web spatial database system of the Royal Projects under the Inspiration of King Rama 9th. In 2022 The 8th International Conference on Frontiers of Educational Technologies (ICFET) (pp. 201-209).

Bereta, K., Xiao, G. and Koubarakis, M., 2019. Ontop-spatial: Ontop of geospatial databases. Journal of Web Semantics, 58, p.100514.

Rahman, A.A., Rashidan, H., Musliman, I.A., Buyuksalih, G., Bayburt, S. and Baskaraca, P., 2019. 3D GEOSPATIAL DATABASE SCHEMA FOR ISTANBUL 3D CITY MODEL. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, pp.11-16.

Saraee, M. and Silva, C., 2018, April. A new data science framework for analysing and mining geospatial big data. In Proceedings of the International Conference on Geoinformatics and Data Analysis (pp. 98-102).

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