Geospatial Data

What is a database?

A database is a collection of interrelated elements or data that can be processed by one or more application systems.

This functionality helps to avoid:

1. a) Data Redundancy: There are data elements that should appear once and only once in the system.
2. b) Poor Data Control: The same data element often has multiple names, depending on the file in which it is contained.
3. c) Inadequate Data Manipulation Capacity: Indexed files allow control over queries, ensuring the existence of unique identifiers.
4. d) Excessive Programming: To make queries and manipulation faster and more efficient. The advantages of designing and working with a database include:

• Having structured data.
• Dividing data and processes, which means greater dependence on data and increased processing flexibility.
• Data Integrity, which ensures consistency, security, and protection of data.
• Long-lasting and durable data.

What is geospatial data?

Geospatial data is the digital record with combinations of attribute values that make it unique and unmistakable compared to other geospatial data. It must be georeferenced and contain an attribute that relates it to time for comparison purposes. Geospatial data must comply with standards that facilitate their availability, access, interoperability, and use in different applications, thus ensuring that the data does not end up underutilized or restrict the usefulness of the databases that store them.

 

Evolution of Geospatial Data

Databases have evolved from:

1. Types of spatial data referring to shapes like points, lines, and polygons.
2. Multidimensional spatial indexing used for efficient processing of spatial operations.
3. Spatial functions defined in SQL for querying spatial properties and relationships.

In the first generation of GIS, all spatial data is stored in flat files with special software for interpreting and manipulating the data. These first-generation management systems are designed to meet user needs, where all required data is found within the user's organizational domain. They are proprietary, standalone systems specifically built for managing spatial data.

In the second generation of systems, non-spatial data (attributes) are stored in relational databases but still lack the flexibility offered by the direct integration of spatial data.

The third generation appears when spatial characteristics are treated as first-class base objects. Spatial databases are fully integrated with the relational object database.

Based on these foundations, the concept of "GEODATABASE" arises, which is a generic model for handling geospatial information, storing geographic objects, their attributes, their relationships (spatial or otherwise), and the behavior of each of its elements.

In this schema, the migration is from thematic layers (a collection of geographic elements) to real entities such as road networks, sewage systems, electrical power, etc., incorporating the concept of entity when referring to transformers, roads, or lakes. The utility of this concept reflects the treatment of our data for spatial analysis.

What are they responsible for?

Geospatial data are information that describes objects, events, or other characteristics with a location on or near the Earth's surface. Geospatial data typically combine location information (usually coordinates on Earth) and attribute information (the characteristics of the object, event, or phenomenon in question) with temporal information (the time or lifespan in which the location and attributes exist).

The provided location can be static in the short term (e.g., the location of equipment, an earthquake, children living in poverty) or dynamic (e.g., a moving vehicle or pedestrian, the spread of an infectious disease).

Geospatial data generally involve large datasets collected from many diverse sources in different formats and may include information such as census data, satellite imagery, weather data, cellular phone data, drawn images, and social media data. Geospatial data are most useful when they can be discovered, shared, analyzed, and utilized in conjunction with traditional business data.

 

What are these data used for?

Geospatial analytics is used to add time and location to traditional types of data and to create data visualizations. These visualizations can include maps, graphs, statistics, and cartograms that show historical changes and current changes. This additional context allows for a more comprehensive view of events.

Insights that might be overlooked in a massive spreadsheet are revealed in visual patterns and easily recognizable images. This can make predictions quicker, easier, and more accurate.

Geospatial Information Systems (GIS) specifically relate to the physical mapping of data within a visual representation. For example, when a hurricane map (showing location and time) is overlaid with another layer showing potential lightning strike areas, you are seeing GIS in action.

 

What advantages does it have?

The advantages of working with a GEODATABASE include multi-user access and compatibility with the following database managers: Oracle, Informix, SQL Server, IBM DB2, Microsoft Access. It resides in a standard database management system, which allows for the utilization of all the benefits of large database management systems, translating into greater simplicity for managing a corporate database.

In addition to these advantages, it offers various benefits, including:

1. Centralized data management: This property is provided by the DBMS being employed. Even if each organization has a unique policy for managing and maintaining geospatial data or not.
2. Multi-user editing: Many users can perform editing tasks on the data. Editing sessions can last for weeks or months.
3. Object behavior: Once the behavior of each type of element is defined, it refers to rivers, roads, parcels, etc. For example, rivers have properties of flow, discharge, etc., and only connect to other rivers or bodies of water. This also gives it determined symbolic properties.

 

What opportunities does it provide?

Geospatial analysis involves the collection, presentation, and manipulation of images, Global Positioning System (GPS) coordinates, photographs, and satellite data (in real-time or historical), using explicit geographic coordinates or identifiers used in geographic models.

Geospatial analysis has advanced significantly in terms of:

• Greater accuracy, precision, and granularity;
• Increased ease and speed of transmission, analysis, and manipulation (for example, the connectivity of mega-constellations of satellites);
• The number and type of devices equipped with geospatial and location identification (for example, different types of devices such as those connected to the Internet of Things, mobile phones, sensor networks, connected cars, etc.).

For instance, fifth-generation mobile technology, IMT-2020 (or 5G), when applied in millimeter wave bands, requires very precise geospatial data and denser telecommunications networks with a significantly greater number of base stations than traditional mobile networks.

Both precise geographic data and advanced spatial analysis are crucial to ensure that these radio networks are cost-effective and efficient. 5G base stations need to be synchronized within nanoseconds to enhance positioning accuracy for intelligent transportation and intelligent traffic management systems.

Geospatial data and information are highly valuable, from the global to the local level, and can be used for many different use cases, including monitoring, verifying, and/or confirming:

• Climate modeling and weather prediction; monitoring of local weather, seasonal, or climate systems (for example, the El Niño effect).
• Tracking urbanization and gas emissions and/or pollution from cities and industry.
• Urban use cases, including intelligent transportation systems, autonomous vehicles, and real-time traffic congestion monitoring.
• Natural disasters (for example, the extent of landslides or floods) and relief activities.
• Identification and mapping of facilities, such as schools, clinics, refugee camp sizes, and installations.
• Monitoring human rights abuses (for example, treatment of refugee populations).
• Identification of archaeological sites of interest.
• Mapping deforestation and land use, and estimating crop yields to predict trends in food and commodity markets.
• Estimating poverty and income levels (for example, based on car types or the quality of roofing materials).
• Migration of populations and animals.