Types of Maps in GIS - PDF

Type of Maps In Geographic Information Systems (GIS), a variety of maps are used to visualize and analyze spatial data. Here are some of the key types: 1. Raster Maps: Made up of pixel grids, useful for continuous data like satellite images or elevation models. 2. Vector Maps: Consist of p...

Type of Maps In Geographic Information Systems (GIS), a variety of maps are used to visualize and analyze spatial data. Here are some of the key types: 1. Raster Maps: Made up of pixel grids, useful for continuous data like satellite images or elevation models. 2. Vector Maps: Consist of points, lines, and polygons to represent features like cities, roads, and boundaries. 3. Topographic Maps: Highlight terrain features using contour lines and elevation data. - A topographic map shows the physical surface features (roads, rivers, buildings) - Contour Lines: These lines mark points of the same elevation. Close lines indicate steep terrain, while widely spaced lines suggest a gentler slope. - Index Contours: Usually, every fifth line is thicker and often labeled with the elevation, making it easier to read the map. - Contours and Features: Valleys, hills, ridges, and slopes are all depicted through the spacing and shape of the contour lines. - Contour lines can represent qualitative date or show zones of equal value for non-numeric attributes, like soil types, vegetation zones, or pollution levels. Think of it as mapping out regions with the same characteristics, rather than exact measurements. 4. Thematic Maps: Focus on specific themes, such as population density, land use, or climate zones. 5. Choropleth Maps: Use different colors or shades to represent data values in predefined areas, like electoral districts. - A choropleth map shows areas characterized by some general common feature (political maps, crop types, soil types). - Uses colours to show anything. Even qualitative data like the level of education, the economic status. It uses different shades of the same colour to show the difference. 6. Dot Density Maps: Show spatial distribution using dots to represent the occurrence of a feature. 7. Heat Maps: Use color gradients to represent the intensity of data points, like crime rates or traffic accidents. 8. 3D Maps: Provide a three-dimensional view of terrain or buildings, enhancing the understanding of spatial relationships. 9. Network Maps: Focus on transportation or utility networks, showing connectivity and flow within the network. Topographic Maps A topographical map is a detailed and accurate graphic representation of the features that appear on the Earth\'s surface. It includes: - Contour Lines: Represent elevation and shape of the terrain, showing hills, valleys, and plains. - Natural Features: Rivers, lakes, forests, and mountains. - Man-Made Features: Roads, buildings, and other infrastructure. - Scale: Indicates the ratio between distances on the map and the real world. - Symbols: Various icons and colors to differentiate between types of vegetation, land use, water bodies, etc. Four main symbols depict features on a map - Points: Indicate specific locations like summits, wells, or buildings. Example: (towns, stations, buildings, etc) -- like the capital of Malaysia - Lines: Represent linear features such as roads, rivers, and contour lines. Example: (roads, rivers, railways, etc.) -- like roadways in Malaysia - Polygons: Show areas like lakes, forests, and urban regions. Example: (lakes, boundaries, states, etc.) -- like states of Malaysia The points, lines and polygons are spatial data because they occupy a space - Textual annotation (names of locations, buildings, oceans) 1. Vector Data Model: - Represents spatial data using points, lines, and polygons. - It\'s ideal for mapping discrete features like buildings, roads, and land parcels. - Each feature is stored as a coordinate-based structure, with attributes describing its characteristics. - The spaghetti model is the simplest vector data structure used in GIS. - In this model, each point, line, and polygon feature is represented as a string of X, Y coordinate pairs. - For vector images with a single point, a single X, Y coordinate pair is used. - Outlines of feature are drawn in full irrespective of other features around them - Polygons that adjoin will have double boundaries. - No way of knowing about lines that cross one another. - Spaghetti data model contains no way of encoding the spatial relationship between individual entities 2. Raster Data Model: - Represents spatial data as a grid of cells or pixels. - Raster data is any pixelated (or gridded) data where each pixel is associated with a specific geographic location. The value of a pixel can be continuous (e.g. elevation) or categorical (e.g. land use).  - Each cell has a value representing a specific attribute, such as elevation or land cover. - It\'s well-suited for continuous data like satellite imagery or temperature maps. - Raster data is highly simplistic but suffers from issues of: resolution, large file sizes and data redundancy A screenshot of a computer generated image Description automatically generated In raster data, the scale of the pixels---also known as the resolution---indicates the area on the ground each pixel represents. For instance: - **High Resolution**: Pixels might represent 1 square meter or even finer, providing very detailed images (used in urban planning or detailed environmental studies). - **Medium Resolution**: Pixels might cover 10 to 30 square meters, balancing detail and coverage (often used in regional planning or agriculture). - **Low Resolution**: Pixels might cover 100 square meters or more, suitable for large-area surveys (like global climate models or broad vegetation mapping).  Higher resolution offers more detail but requires more storage and processing power, whereas lower resolution covers larger areas more efficiently.  Issues affecting conceptualisation (conceptualization refers to how we represent and understand geographical things) 1. Simplification: Real-world complexity vs. the simplified models in GIS. 2. Scale: Level of detail and precision needed---broad vs. detailed. A map with a black background Description automatically generated with medium confidence 3. Data Quality: Accuracy, consistency, and completeness of spatial data. 4. Representation: Choosing between vector and raster, and how to best model spatial relationships. 5. Context: How different users' needs and perspectives can influence what's important to map and analyze.  6. Temporal Changes: How to represent dynamic changes over time. A blackboard with a black background with a river and a city Description automatically generated with medium confidence 7. Boundaries and Edge Effects: Data at the edges of a map might be less accurate or missing, impacting analysis and interpretation.  Databases A database is a tool capable of storing large amounts of complex information in structured ways - Information in a database is organized into individual records that can be referenced, stored, indexed, linked and queried - In large industrial software systems, there will usually be multiple databases operating together in a database management system (DBMS) There are two main types of databases: 1. Relational Database Management System (RDBMS) A Relational Database Management System (RDBMS) is software that enables the creation, management, and manipulation of relational databases.  - **Tables**: Organize data into rows and columns. - **Relationships**: Use primary and foreign keys to establish connections between tables. - **SQL**: Standard language for querying and updating data. - **ACID Properties**: Ensure data integrity and reliability (Atomicity, Consistency, Isolation, Durability). Examples include MySQL, PostgreSQL, and Oracle. These are great for handling structured data with complex relationships. 2. Object-oriented database management system (OODBMS) An Object-Oriented Database Management System (OODBMS) blends database capabilities with object-oriented programming language features. Here\'s what it\'s all about: - **Object Integration**: Stores data as objects, similar to how they\'re handled in object-oriented programming. This means data and its behaviors are encapsulated together. - **Classes and Inheritance:** Uses classes to define data structures and supports inheritance, allowing for more complex data models. - **Data Persistence**: Objects created in an application can be made persistent, meaning they remain in the database after the application is closed. - **Complex Data Types**: Easily handles complex data types, like multimedia files, which can be cumbersome in traditional relational databases. - **Seamless Integration**: Offers better integration with object-oriented programming languages, making it easier for developers to work with. Examples of OODBMS include ObjectDB, db4o, and Versant Object Database. GIS can work with two types of database systems, RDBMS and OODBMS