spatial-db.pdf

SPATIAL DATABASE Non-spatial database → database optimized to store and query data that represents → traditional database that stores and manages data without objects defined in a geometric shape. cons...

SPATIAL DATABASE Non-spatial database → database optimized to store and query data that represents → traditional database that stores and manages data without objects defined in a geometric shape. considering its geographic location. → allow representing simple geometric objects such as points, → uses tabular structures with rows and columns to organize lines, and polygons. information. → handle more complex structures such as 3D objects, Focuses on data and its attributes. topological coverages, and linear networks. Data types: text, numbers, dates SPATIAL DATA Spatial database → data related to space or some predefined location. → designed to handle geographic data. It stores and manages → includes shape, size, location, and orientation information that has a spatial component, such as location, → form of graphic primitives which includes points, lines, shape, and spatial relationships. polygons, or pixels. Focuses on data and its geographic location. → comprised of objects in multi-dimensional space Data types: points, lines, polygons, and their FEATURES associated attributes. ✓ Geometric and varied GIS ✓ Naturally high dimensional → system that visualizes and analyze spatial data. ✓ Can be either discrete or continuous → utilizes SDBMS along with many other components to PROPERTIES generate a desired information. ✓ Geometry SDBMS ✓ Distribution of objects in space → designed for storing, querying, sharing, and retrieving spatial ✓ Temporal changes data. ✓ Data volume → may also be used by application other than GIS. VALUE → In various fields there is a need to manage geometric, CHARACTERISTICS OF SPATIAL DATABASE geographic, or spatial data (data related to space). Spatial Data Types o the 2D abstraction of the surface of the earth — - storing and processing spatial data types such as points, that is, geographic space, lines, polygons, and multi-dimensional geometries o a man-made space like the layout of a VLSI Spatial Indexing design, a volume containing a model of the human - efficiently retrieve and query spatial data based on their brain, or another 3d-space representing the spatial relationships arrangement of chains of protein molecules. Geometric Operations TYPES - intersection, union, buffering, and distance calculations on VECTOR spatial objects - points, line, and polygons in representing spatial features or Coordinate Systems objects on Earth's surface. - different coordinate systems and projections to represent RASTER spatial data accurately on the Earth's surface - pixelated data where each of its pixels carries Integration with GIS corresponding values representing a certain information, - enable seamless analysis, visualization, and manipulation usually about color or tone. of spatial data ATTRIBUTE Scalability - additional information that describes the characteristics of - handle large volumes of spatial data and support for a spatial feature. distributed computing environments TEMPORAL Spatial Reference Systems - data associated with a specific time - manage and convert spatial data between various SRS and COMPONENTS OF SPATIAL DATABASE MANAGEMENT CRS. SYSTEM Integration with Non Spatial Data ✓ SPATIAL DATA MODEL - combine spatial and non-spatial data, allowing for - Defines how spatial objects and their relationships are comprehensive queries that mix geographic and traditional represented within the database database attributes. ✓ SPATIAL DATA TYPES Spatial Data Storage - building block representing geographic features of an - efficiently store large volumes of spatial data, including objects like points, lines, polygons, and multi-part time-series spatial data, 3D data, and high-resolution raster geometries. data ✓ SPATIAL OPERATORS Extensibility - Functions for performing operations on spatial data, - extensible with modules or plugins that add support for new including distance calculations, overlaps, buffering, spatial spatial functions, data types, or custom spatial analyses. joins, and geometric transformations ✓ SPATIAL INDICES - Data structures to accelerate spatial query performance, optimizing search operations ✓ SPATIAL QUERY LANGUAGE - language for querying spatial data, allowing users to retrieve, analyze, and manipulate spatial information.  GEODATABASE - comprise planar 3D rings and triangles that are → a proprietary GIS file format developed in the late 1990s by used in combination to model a three-dimensional Esri to represent, store, and organize spatial datasets within shell. geographic information system. - represent anything from simple objects, such as → Collection of geographic datasets of various types held in spheres and cubes, to complex objects, such as iso- common file system folder such as IBM Db2, Microsoft surfaces and buildings. SQL Server and Oracle. → Physical store of geographic information, primarily using a A geodatabase is a container used to hold a collection of datasets. database management system or file system. File Geodatabase → Geodatabase software logic provides the common → stores as multiple files in a folder with a.gdb extension. application logic used throughout ArcGIS for accessing and → Each dataset is contained in a single file working with all geographic data in a variety of files and → By default, files can grow to 1 TB, but this can be changed formats. to 4 or 256 TB using a config. Keyword. FUNDAMENTALS Mobile Geodatabase ✓ Table → Stored in an SQLite database that is entirely contained in a - Collection of rows, each containing same fields single file and has a.geodatabase extension. ✓ Feature class Enterprise Geodatabase - Table with a shape field containing point, line or polygon → “multi-user geodatabases” geometries for geographic features → Stored in relational databases - Each row is a feature → Can be virtually unlimited in size and number of users; limits ✓ Raster dataset differ depending on the DBMS vendor - Contains raster which represent continuous geographic phenomena CORE BENEFITS OF SPATIAL DATABASE Spatial TYPES: → Relating to, occupy, or having the character of space. POINTS → perception of relationships (as of objects) in space. - Features that are too small to represent as lines or → anything involving space, whether it's physical, geometric, polygons as well as point locations (such as GPS data- related, or cognitive, focusing on how objects are observations). organized, related, and interact within a defined space. LINES Database - Represent the shape and location of geographic → large collection of data organized especially for rapid search objects, such as street centerlines and streams, too and retrieval (as by a computer). narrow to depict as areas. → An integrated set of data on a particular subject. - used to represent features that have length but no Independence Security Efficiency Less area, such as contour lines and boundaries. Redundancy POLYGONS Availability Coherence Informative - set of many-sided area features that represents the shape and location of homogeneous feature types such as states, counties, parcels, soil types, and Spatial DB land-use zones. → database that is optimized to store and query data related to ANNOTATION objects in space, including points, lines and polygons. - Map text including properties for how the text is → database containing geographic data of a particular subject rendered. for a particular area - can also be feature linked and can contain → Describe the fundamental representation of the object of a subclasses. dataset that comes from spatial or geographic entities. DIMENSIONS - special kind of annotation that shows specific CORE BENEFITS lengths or distances. - To store and access spatial data or data that defines a - heavily used in design, engineering, and facilities geometric space. applications for GIS. - Can handle more complex data like 3D objects, topological MULTIPOINTS coverage and linear networks - composed of more than one point. Speed - often used to manage arrays of very large point Spatial databases run on powerful servers with collections, such as lidar point clusters, which can high- speed drives and ample memory, enabling contain literally billions of points. Using a single faster processing of complex queries and analyses row for such point geometry is not feasible. compared to performing these tasks on a client’s Clustering these into multipoint rows enables the desktop. geodatabase to handle massive point sets. Security MULTIPATCHES Security is a crucial aspect of spatial databases, - 3D geometry used to represent the outer surface, and several core benefits helps ensure that spatial or shell, of features that occupy a discrete area or data is protected from unauthorized access, loss, or volume in three-dimensional space. corruption.  Backup and Replication Topological Fine-Grained ATTRIBUTE DATA Recovery and Failover Integrity Permissions - used to describe or quantify an object or entity - additional information that describes the Compliance Access Authentication Data Integrity characteristics of the spatial features and Standards Control & & Validation Both vector and raster data often come with associated Authorization attribute data. RASTER DATA Encryption Auditing and - pixel-based data where each pixel has a value Logging representing information. - useful for representing data that varies Multi-user continuously overs space and is more naturally Multi-user editing is highly efficient and seamless, suited to a grid format allowing multiple users to edit data simultaneously Digital Elevation Models (DEM) from different clients. - represents elevation or depth. Each cell in the Unlike file-based storage systems, which typically raster grid contains a value representing the height allow only one user to edit data at a time while of the Earth’s surface at that location. others are locked out, spatial databases enable Satellite imagery concurrent editing without such restrictions. - Each cell in a raster grid contains color values that SQL Queries collectively make up a satellite image SQL (Structured Query Language), provides - valuable for land cover mapping, environmental powerful tools for managing, analyzing, and monitoring, and disaster assessment. querying spatial data, making them indispensable Temperature maps for applications that require detailed geographic or - each cell contains a value representing the geometric information. temperature at a specific location. Scalability - useful for climate modeling and weather Refers to the ability of the database to efficiently prediction. handle increasing amounts of data, users, or Land Use Maps complexity in spatial queries as the system grows. - divide a region into cells that each represent a type Common Data Storage of land use, such as urban, agricultural, or forested Its ability to integrate non-spatial data with spatial land. data seamlessly. For example, you can link a non- TEMPORAL SPATIAL DATA spatial table (e.g., personal data) to spatial data - data associated with a specific time (or time (e.g., parcel polygons) using a common identifier, range). like a parcel ID. - Might be vector point data or raster imagery data allows different software systems to manage attribute data separately from spatial geometry, ADVANTAGES AND DISADVANTAGES OF SPATIAL simplifying updates and maintaining data DATABASE integrity. ADVANTAGES: ✓ Efficient Storage and Retrieval TYPES OF SPATIAL DATABASE Spatial DB are great at storing and quickly finding GEOMETRIC DATA location-based data as they use special methods like R- - MAPPED ON 2D FLAT SURFACE, SUCH AS MAP OR FLOOR PLAN trees, quad-trees, or grid flies to organize this SPATIAL information. - makes it faster and easier to find data based on GEOGRAPHIC DATA DATA - INFO. MAPPED AROUND A SPHERE, EARTH. location or how different places relate to each - HIGHLIGHTS LAT AND LON OF other, helping with quick and accurate analysis. SPECIFIC OBJECT OR LOCATION ✓ Integration of Spatial and Attribute Data Spatial DB are effective at combining spatial data VECTOR DATA (coordinates and shapes) with attribute data (non- - represent world using points , lines, and polygons. spatial details). - Created by digitizing the base data and it stores info in x, y coordinates - integration allows for thorough analysis by Point data merging location data with related information. - represented by the coordinates of a geographic For example, in real estate system, a spatial DB location. can store property locations with attributes like prices, area, and amenities, offering valuable - used to show the location of a particular feature. insights. Line data ✓ Standardized Query Language - one-dimensional features composed of multiple, Most spatial DB use SQL with spatial extensions, explicitly connected points. allowing users to apply their existing SQL skills to Polygon data manage spatial data. - Closed geometric shapes formed by the - standardized language simplifies data handling combination of at least three points. and ensures compatibility across different - can store both boundary and attribute information database systems, making it easier to work with of an area. spatial data in a consistent way.  ✓Support for Complex Geometric Operations 1. COMPLEX DATABASE Spatial DB support advanced geometric operations, Complex data types enabling detailed spatial analysis. - challenging to manage intricate geometries and - operations include task like buffering, overlaying, guarantee topological consistency (e.g., no intersection, and union. overlapping boundaries) - crucial in applications like GIS, where complex Involves multiple dimensions spatial operations are needed for decision-making - increasing the complexity of data representation and visualizing data. and processing ✓ Scalability and Performance 2. EFFICIENT QUERY PROCESSING Spatial DB are built to manage large amounts of spatial Queries in spatial databases data efficiently. They can scale by distributing data - involve spatial relationships such as containment, across multiple servers, which improves performance proximity, intersection, and overlap, which are and ensures availability. computationally expensive- requires specialized - especially useful in applications with large algorithms. datasets, such as navigation systems, satellite 3. SCALABILITY imagery, and geospatial analysis. Handle large datasets (GIS, satellite imagery, or sensor DISADVANTAGES: networks)  Complexity and learning curve - As the volume of spatial data grows, scaling the Spatial DB introduces an added level of complexity not database infrastructure to maintain performance typically found in simple database. The management of becomes challenging. spatial data types, combined with spatial query Distributing spatial data languages like PostGIS, and SQL extensions, are hard - challenges in maintaining consistency, replication, to grasp, especially for new users- no experience in and partitioning for large-scale data. managing geospatial data. 4. ACCURACY AND PRECISION  Cost of implementation and maintenance Relies on floating-point arithmetic - cost involved in implementing and maintaining - introduce precision errors when calculating spatial database infrastructure. distances, areas, or spatial relationships - - cost incurred by acquiring high-performance challenge in ensuring accuracy hardware for handling large datasets, acquiring 5. DATA INTEGRATION licenses for special software, and hiring competent Comes from various sources, such as GPS devices, satellite personnel to manage, update, and optimize the imagery, or GIS systems system. - complexity in integrating data sources into single,  Data quality and integrity coherent spatial database; maintaining accuracy - efficiency of spatial DB relies on the quality of and compatibility. data hosted in them. 6. TEMPORAL-SPATIAL DATA - crucial to have accurate and precise spatial data as Many spatial DB need to manage not just static spatial data flawed conclusions or misguided decision-making but also dynamic data that changes over time (e.g., moving might emerge from such data. Ensuring data objects, evolving geographical features) quality requires strict validation and updating as 7. DATA QUALITY AND UNCERTAINTY frequently as possible to reduce the potential for Spatial data often contains inaccuracies or inconsistencies inaccuracies in analysis. due to limitations in data collection methods (e.g., errors in  Limited support for non-spatial data GPS readings or remote sensing). Maintaining data quality While spatial DB maintain high performance in and addressing issues like noise, missing data, or outliers is geospatial data storage and management, there are essential. situations where non-spatial data is supported only 8. PERFORMANCE AND OPTIMIZATION poorly. Processing spatial joins, intersections, and spatial - functionality of a spatial database is highly relationships across large datasets can be time- consuming. limited. Optimizing performance for these operations, especially in  Performance degradation with complex queries real-time or near real-time applications is a challenge. Spatial DB allows for advanced spatial queries, such as 9. VIZUALIZATION AND USER INTERACTION finding intersections, executing proximity analyses, or Spatial data is often visualized in maps or 3D models, performing spatial joins requiring efficient rendering techniques, especially for large- - these queries can sometimes be expensive with scale data. Visualization needs to be dynamic and responsive regards to performance, when large datasets have to queries while maintaining accuracy. Users may interact to be processed, or when there is high number of with spatial data by drawing shapes, selecting areas, or spatial objects involved in a calculation and broad querying regions on a map. Supporting interactive spatial extents queries in a user- friendly manner without sacrificing performance is challenging. CHALLENGES OF SPATIAL DB 10. LEGAL AND PRIVACY ISSUE Spatial databases, which store and manage data related to objects in Managing privacy concerns, especially with personal space (such as geographic locations, shapes, and features), face several geolocation data, involves ensuring that spatial databases unique challenges compared to traditional databases. adhere to privacy laws and regulations. Handling sensitive spatial data securely while enabling useful queries poses ethical and technical challenges.  ✓ representing the conceptual model by an appropriate spatial SPATIAL DB: TECHNOLOGY, TECHNIQUES,AND TRENDS data model; and In spatial DB, Technologies refer to foundational technologies and ✓ Selecting an appropriate spatial data structure to store the techniques that are essential for the management, storage, retrieval, model within the computer. and querying of spatial data. These technologies are crucial in the Data Visualization and Mapping context of GIS, location-based services, and applications dealing with - To represent and understand complex data, geospatial data. particularly spatial or geographic data, in an easily CORE TECHNOLOGIES interpretable graphical format. ✓ ORACLE SPATIAL - In many fields like GIS, urban planning, - enterprise-level tool integrated with Oracle construction, and environmental science, these databases for managing complex geospatial data, tools help analyze large datasets and reveal ideal for large-scale applications needing efficient patterns, trends, and relationships that are geographic data processing. otherwise hard to detect. ✓ PostGIS (on PostgreSQL) - powerful open-source extension for PostgreSQL, EMERGING TRENDS IN SPATIAL DATABASES offering advanced GIS capabilities and popular for BIG DATA its flexibility in spatial data management and - vast amounts of structured and unstructured data mapping. that are generated at high velocity from various ✓ Microsoft SQL Server sources, such as sensors, social media, GPS - database system, with built- in tools for querying devices, mobile phones, and many other digital and analyzing spatial data, especially suitable for platforms. businesses using Microsoft’s ecosystem. - characterized by the "3 V's"—volume, velocity, ✓ SpatialLite (with SQLite) and variety—and requires advanced tools and - lightweight geospatial solution for smaller technologies to collect, store, process, and analyze applications, adding spatial capabilities to SQLite, it efficiently. perfect for mobile and compact GIS needs. TECHNIQUES FOR MANAGING SPATIAL DATABASES SPATIAL ANALYTICS Spatial Indexing - focuses on analyzing data that includes - improves the speed and efficiency of spatial geographical or spatial components. This kind of queries. Given the size of spatial datasets, indexing data is associated with physical locations or places allows for quicker searching and retrieval. on the Earth’s surface. R-tree Indexing: - involves understanding patterns, relationships, and Organizes spatial objects into a tree structure using trends by examining the geographic aspect of the minimum bounding rectangles. data. - highly efficient for range queries, nearest neighbor - involves tools like GIS and spatial databases to searches, and finding overlapping spatial objects. manage, analyze, and visualize location- based Quad-tree Indexing: data. Divides a space into four quadrants recursively. - used to partition data spatially for fast region- REAL TIME SPATIAL DATA PROCESSING based queries, such as locating objects in specific - continuous collection, analysis, and visualization regions or finding spatial relationships. of spatial data as it is generated. Geohash: - requires advanced tools and technologies to handle Converts geographical coordinates (latitude, vast amounts of data coming from sensors, GPS longitude) into a hashed string, allowing easy devices, social media, drones, and other real-time spatial partitioning and comparison. sources. - useful for quick spatial lookups and distance - goal is to provide immediate insights and enable calculations. real-time decision-making based on location data Data Partitioning Partitioning of large datasets into smaller, more manageable MACHINE LEARNING AND AI IN SPATIAL DATA pieces helps to optimize performance and manageability. PROCESSING Spatial Partitioning: - use of advanced computational techniques to Divides large spatial datasets into spatial regions analyze and interpret spatial data, such as GIS based on geographic boundaries. data, satellite imagery, and sensor data. Spatial Data Modelling and Design - enhance the ability to process large volumes of - construction of models of spatial form can be spatial data efficiently and derive actionable thought of as a series of stages of data abstraction. insights, improving decision-making across By applying these abstraction techniques, the GIS various fields such as urban planning, designer moves from the position of observing the environmental monitoring, and disaster geographical complexities of the real world to one management. of simulating them in the computer. This involves: ✓ identifying the spatial features in the real world and choosing how to represent them in a conceptual model (points, lines, area);  APPLICATION OF SPATIAL DATABASE CRIME ANALYSIS AND PUBLIC SAFETY REAL-WORLD APPLICATION OF SPATIAL DATABASE - help analyze crime patterns and allocate resources Spatial DB have numerous real-world applications across various effectively. industries. The versatility of spatial databases makes them invaluable - enable law enforcement to identify crime hotspots in numerous industries where location- based data plays a critical role and develop targeted strategies to improve public in decision- making and analysis. safety. RESEARCH AND ACADEMIA GEOGRAPHICAL INFORMATION SYSTEMS (GIS) - analyze and visualize data across various fields. - manage geographic data. They help in mapping study areas and exploring - Cities and governments use it to plan construction, spatial relationships, supporting advanced manage land, and make decisions about land use. academic research. URBAN PLANNING AND INFRASTRUCTURE MANAGEMENT - help urban planners make decisions regarding land SPATIAL QUERY use, transportation networks, and infrastructure A query in a spatial database that can be answered on the basis of development. geometric information. ENVIRONMENTAL MANAGEMENT AND CONSERVATION Query: “List all evacuation centers within 5 kilometers of Mayon - monitor the environment, track ecological Volcano in Albay. changes, and aid in conservation strategies for TYPES OF SPATIAL QUERY vulnerable ecosystems. NEARNESS QUERIES DISASTER MANAGEMENT AND EMERGENCY RESPONSE request objects that present near a specified - crucial for mapping disaster areas and planning location. evacuations. REGION QUERIES - provide real-time data to help emergency services finds all objects of a particular type that are within coordinate their response and allocate resources a given spatial area. efficiently. UNION/INTERSECTION TRANSPORTATION AND LOGISTICS joins the objects of two types based on spatial - optimize transportation by analyzing data on road condition, such as the objects which are networks and traffic patterns. intersecting or overlapping spatially. - helps businesses plan efficient routes and manage SPATIAL QUERY OPERATORS fleets, reducing costs and improving service. a. CROSS RETAIL AND MARKETING - Selects line or region features that are crossed by - Retailers use spatial databases to analyze customer a searching line. locations and market trends, helping to decide - line traverses another feature, intersecting its store locations and target advertising. interior at points. HEALTH AND EPIDEMIOLOGY b. CONTAIN - track disease outbreaks and analyze health data to Finds features contained within the searching object. identify areas with high disease rates and allocate - searched feature is fully or partly inside the healthcare resources. searching object, with possible boundary ARCHAEOLOGY AND CULTURAL HERITAGE intersection. - support archaeology by mapping historical sites c. WITHIN and artifacts. Researchers use spatial databases to The inverse of "Contain." map historical sites and study cultural heritage, - returns features that completely encompass the preserving artifacts and studying past civilizations. searching object, including boundaries. AGRICULTURE AND FARMING d. OVERLAP - improve crop yields and manage resources. By - Selects features that partially overlap the searching analyzing soil quality and weather data, they can object, with both sharing an area but not fully optimize irrigation and fertilization, leading to encompassing each other. more efficient farming practices. e. DISJOINT REAL ESTATE AND PROPERTY MANAGEMENT - Returns features that have no shared area or - In real estate, spatial databases analyze property boundary with the searching object. values and market trends, helping assess f. TOUCH investment opportunities for buying and selling - Selects features that touch the searching object at properties. a boundary but do not overlap. TOURISM AND RECREATION g. IDENTITY - enhance tourism by integrating data on attractions - Finds features that completely match the searching and visitor patterns. This helps organizations plan object in type, position, and coordinates. better destinations and manage visitor h. INTERSECT experiences, boosting tourism and satisfaction. - Returns features that share any point with the ENERGY AND NATURALRESOURCES searching object, including boundaries or interior. - support the management of energy resources by locating optimal sites for projects and analyzing energy consumption. - help companies operate efficiently while minimizing environmental impact. QUERYING TECHNIQUES IN GIS SOFTWARE? Attribute Query (aspatial) - Retrieve data based on non-spatial attributes (e.g., population, area, type). Spatial Queries - Retrieve data based on the location or spatial relationship between features. Topological Queries - Analyze spatial relationships like adjacency, connectivity, and containment between features. Buffer Queries - Create buffer zones around a feature (point, line, or polygon) and find other features within those zones. Spatial Joins - Combine two datasets based on their spatial relationship (e.g., associating points with polygons). Raster Queries - Used for querying raster data (gridded data like elevation or satellite images). Overlay Queries - Combine two or more spatial datasets to analyze areas of overlap or difference. Map Algebra (Raster-based Analysis) - Perform mathematical operations on raster layers (e.g., summing or subtracting layers). USE CASES OF SPATIAL QUERIES 1. Urban planning and zoning 2. Environmental management 3. Disaster management 4. Public safety and emergency services 5. Transpiration and logistics 6. Real estate development 7. Telecommunications and network planning 8. Archaeology and cultural heritage conservation 9. Agriculture and land use 10. Natural resource management ADVANTAGES OF SPATIAL QUERY ✓ Efficient Geographic Analysis ✓ Improved Decision-Making ✓ Accurate Resource Allocation ✓ Time and Cost Efficiency ✓ Enhanced Data Visualization ✓ Improved Risk Management