GIS

Cloud-Optimized GeoTIFFs (COGs) are a specialized format of GeoTIFF designed to enable efficient access to raster data, particularly in cloud environments. By organizing data into tiled structures and enabling partial reads, COGs allow users to fetch only the portions they need, significantly reducing bandwidth and storage costs

Cartographic Models - temporally static, combined spatial datasets, operations, and functions for problem-solving.

Spatio-temporal models - dynamics in space and time, time-driven processes

Vector - coordinate based data model that represents points, lines, and polygons

Raster - composed of rectangular arrays of regularly spaced square grid cells and each cell has a value (attribute

Sources and Formats: Satellite observations: Data collected at specific time intervals. Numerical models: Data generated by aggregating, interpolating, or simulating from other data sources.

Common storage formats include: netCDF: Often used for oceanographic data. GRIB: Commonly used for weather data. HDF: NASA frequently uses this format for scientific data storage. Esri Cloud Raster Format (CRF): Also supports multidimensional raster data storage. Raster coordinates are stored by ordering the matrix.

Pixel - smallest resolvable piece of scanned image - pixel is always a cell but a cell is not always a pixel.

Geodatabase - object oriented spatial model (feature classes, feature datasets, non-spatial tables, topology, relationship classes, geometric networks) Basic components include feature classes, feature datasets, non-spatial tables. Complex components include topology, relationship classes, geometric networks. Relationship classes – model real-world relationships that exist between objects such as parcels and buildings.

TIN - Triangulated Irregular Network - portions vector data into contiguous, non overlapping triangles Create Delaunay triangles. Advantages of TIN - small areas with high precision elevation data. More efficient storage than DEM or contour lines Disadvantage of TIN - requires very accurate data source and costs are expensive, TIN production and use are very computer intensive)

GRID - A grid is a structured arrangement of data points or values in equally spaced rows and columns, also known as raster data. It’s commonly used to organize and analyze data, especially in fields like geography, meteorology, and computer graphics. It is often used to represent features on the Earth’s surface, such as elevation, land cover, temperature, precipitation, and more. Geospatial data is typically organized into grids where each cell corresponds to a specific location.

Topological - features need to be connected using specific rules.

Hierarchical - database that stores related information in a tree-like structure. Records can be traced to parent records to a root record. Network - collection of topologically connected network elements (edges, junctions, turns) Each element is associated with a collection of network attributes. Object Oriented - data management structure stores data as objects (classes) instead of rows and tables as a relational database

Adjacency: Adjacent features share a common boundary or touch each other. For example, neighboring parcels of land or adjacent census tracts.

Contiguity: Contiguous features are connected or share a border. In a map, contiguity represents areas that are physically touching. It’s essential for analyzing connectivity, such as transportation networks or ecological habitats.

Overlap: Overlapping features occupy the same space. Examples include land cover classes (e.g., forest overlapping with water bodies) or administrative boundaries

Proximity Proximity refers to how close features are to each other. It’s crucial for analyzing accessibility, clustering, and spatial interactions. For instance, measuring the distance between hospitals or identifying nearby amenities.

Spatial Joins: Spatial joins connect or join data based on their spatial relationship. For instance, associating census data with administrative boundaries or linking weather stations to specific regions.

Colocation Analysis: Colocation analysis examines local patterns of spatial association between two categories of point features. It quantifies how often certain features occur together in proximity.

General types of relationships: One-to-one: each object of the origin table can be related to 0 or 1 object of the destination table. One-to-Many: each object in the origin table can be related to multiple objects in the destination table. Many-to-Many: multiple objects of the origin table can be related to multiple objects of the destination table. Equals: a = b - topologically equal Disjoint: a ∩ b = ∅ - no point in common Intersects: a ∩ b ≠ ∅ - some common interior points Touches: (a ∩ b ≠ ∅) ∧ (aο ∩ bο = ∅) - a touches b, at least one boundary point in common but no interior points Contains: a ∩ b = b - feature b is within a Covers: aο ∩ b = b - every point of b is a point of a Covered By: Covers(b,a) - every point of a is a point of b Within: a ∩ b = a - a is within b Crosses: a crosses b at some point Overlaps - a and b have common interior points.

Basic Topology Rules Polygon rules: Must be larger than cluster tolerance. Must not overlap. Must not have gaps. Must not overlap with Must be covered by feature class of Must cover each other. Must be covered by Boundary must be covered by Area boundary must be covered by boundary of Contains point. Contains one point.

Line rules: Must be larger than cluster tolerance. Must not overlap. Must not intersect. Must not intersect with Must not have dangles. Must not have pseudo nodes. Must not intersect or touch interior. Must not intersect or touch interior with Must not overlap with Must be covered by feature class of Must be covered by boundary of Must be inside. Endpoint must be covered by Must not self-overlap Must not self-intersect. Must be single part

Point rules Must coincide with Must be disjoint. Must be covered by boundary of Must be properly inside. Must be covered by endpoint of Point must be covered by line.

Data resolution plays a crucial role in Geographic Information Systems (GIS) and impacts accuracy, analysis, aesthetics, and practical considerations in GIS. Selecting an appropriate resolution ensures effective spatial representation and informed decision-making.