GIS Exam Notes PDF

Introduction to the Raster Data Model The Raster Data Model offers an alternative to the vector model by representing the world as a continuous field rather than discrete objects. This model is especially useful for phenomena that vary continuously, like temperature, elevation, or land cover. Key...

Introduction to the Raster Data Model The Raster Data Model offers an alternative to the vector model by representing the world as a continuous field rather than discrete objects. This model is especially useful for phenomena that vary continuously, like temperature, elevation, or land cover. Key Features of the Raster Data Model 1.​ Grid-Based Representation ○​ Space is divided into square grid cells (or lattice/tessellation). ○​ Each cell represents an attribute value for a specific spatial location. ○​ This differs from vector models, where geometry is associated with multiple or single points, lines, or polygons. 2.​ Spatial Resolution ○​ Spatial resolution refers to the size of each grid cell, measured as ground distance. ○​ Finer resolutions (smaller cell sizes) capture more detail but require more storage. ○​ Spatial extent, on the other hand, refers to the total area covered by the raster. 3.​ Attribute Storage ○​ Each raster layer can store only one variable. ○​ Rasters can store qualitative data (categorical, nominal, ordinal) or quantitative data (numerical). ○​ Examples: ​ Qualitative Raster: Land use types (e.g., forest, water). ​ Quantitative Raster: Elevation or temperature measurements. 4.​ Geo-referencing ○​ Raster cells are geo-referenced using the X, Y coordinate of the top-left cell. ○​ The spatial position of other cells is calculated using the raster's resolution, number of rows/columns, and cell order. Types of Raster Data 1.​ Binary Rasters: Only two values, typically 0 (absence) and 1 (presence). 2.​ Integer Rasters: Whole numbers representing categories (e.g., land cover types) or rounded quantitative data. 3.​ Floating Point Rasters: Continuous data with decimal precision (e.g., rainfall or temperature). 4.​ Character Rasters: Cells represented by strings or letters, though less common compared to integer rasters for qualitative data. Raster Data Structure  1.​ Header Information ○​ Includes the number of rows/columns, cell size (spatial resolution), and starting coordinates. ○​ May include optional information, like legends, for easier interpretation. 2.​ Cell Order and Storage ○​ Attributes are typically stored row-by-row, left-to-right, starting from the top-left corner. ○​ The scan order (how attributes are printed) may vary depending on the file format. 3.​ Storage Efficiency ○​ Unlike vectors, rasters store only one coordinate for the entire layer, making reconstruction faster and more storage-efficient. ○​ This is why "raster is faster" compared to vector data. Important Takeaways ​ Rasters are ideal for continuous phenomena and require separate layers for each attribute. ​ Spatial resolution determines detail, while extent refers to the total study area. ​ Raster data is computationally efficient due to its simple structure, storing one spatial coordinate and calculating others as needed. ​ The type of raster data influences its applications, ranging from simple presence/absence maps to detailed quantitative analyses. By understanding the fundamentals of raster data, you can effectively apply this model to GIS applications and compare it with vector-based approaches. Process of Representing Geographic Data in GIS: 1. Real-world Entity or Event: ○ The process begins with identifying a real-world spatial entity or event to represent digitally in a computer. 2. Data Model Selection: ○ Based on the characteristics we wish to abstract, an appropriate data model is chosen. ○ The selected model simplifies the entity or event while maintaining essential attributes and spatial relationships. 3. Data Structure and Format: ○ The chosen data model is translated into a structure and format suitable for computer processing. Note: The choice of data model impacts how real-world complexities are represented and must be made carefully, as each model has specific strengths and weaknesses. Spatial Data Models: 1. Vector Data Model Key Concept: Represents the world as discrete objects with fixed spatial locations. This approach assumes space is empty except where objects exist. Features Represented: ○ Points: Single locations in space with no length or area (e.g., tree locations). ○ Lines: Linear features representing one-dimensional objects with length but no area (e.g., rivers). ○ Polygons: Two-dimensional features with a definable area (e.g., lakes, land parcels). Key Characteristics: ○ Each object is discrete and can be assigned multiple attributes (e.g., a land parcel labeled as "commercial"). ○ Captures topology, the spatial relationships between objects (e.g., a river flows into a lake). Applications: Best for mapping features with clear boundaries, such as census tracts, state borders, or road networks. 2. Raster Data Model Key Concept: Represents the world as a continuous field of variables, divided into a grid of cells (lattice or tessellation). Features Represented:  ○ Continuous phenomena, such as elevation, temperature, or land cover. ○ Each cell is assigned one attribute value (e.g., a specific land cover type). Key Characteristics: ○ Uniform shape: Always rectangular with a fixed number of rows and columns. ○ Spatial resolution: The size of each cell determines the level of detail. ○ Only one attribute per raster dataset; multiple attributes require separate rasters. Applications: Commonly used for land cover classification, elevation models, and temperature maps. 3. Image Data Model Key Concept: Similar to raster data but specifically records electromagnetic reflectance values for each pixel. Features Represented: ○ Pixels (picture elements) store spectral reflectance intensities rather than physical attributes. Key Characteristics: ○ Pixels have spatial properties, such as X, Y coordinates and resolution. ○ Values (digital numbers or brightness) range from 0 to 255 for each band of spectral data. Applications: Used in remote sensing, aerial photography, and creating new data by digitizing features. Unlike raster datasets, images often do not directly describe physical features. Note: This course covers image data briefly, with detailed study reserved for remote sensing courses. Comparisons and Strengths of Data Models: Aspect Vector Data Model Raster Data Model Image Data Model Representation Discrete objects (points, Continuous data Spectral reflectance lines, polygons) (fields) intensities Attributes Multiple per object One per raster None, only dataset reflectance values Spatial Objects with distinct Uniform grid Pixels in a Organization shapes (rows/columns) rectangular lattice Applications Boundaries, roads, Land cover, elevation, Remote sensing, features climate digitizing data Topology Captures relationships N/A N/A Summary of Key Points: 1. Vector Data Model: ○ Ideal for discrete objects with defined boundaries. ○ Attributes and spatial relationships can be represented. 2. Raster Data Model: ○ Best for continuous data and spatial phenomena. ○ Each cell represents one attribute, and spatial resolution affects detail. 3. Image Data Model: ○ Records spectral reflectance for remote sensing. ○ Provides visual context and supports data creation through digitization. Introduction: The Need for Accurate Spatial Data 1.​ Importance of Data: ○​ Accurate and comprehensive spatial data is essential for creating reliable maps. ○​ Lack of appropriate data can lead to frustration and ineffective mapping. 2.​ Advances in Data Accessibility: ○​ In the past, GIS practitioners relied on hard-copy maps from libraries or government agencies. ○​ These maps had to be digitized manually for GIS use. ○​ Today, advancements in GIS and internet technologies provide access to large, ready-to-use datasets from online sources. 3.​ Key Takeaway: ○​ Always check if a dataset exists from a reputable source before creating your own. ○​ This saves time and ensures quality, as updates and accuracy are managed by the source agency. Sources of Spatial Data: Spatial data can be obtained from state, local, and federal agencies and is often made available through GIS clearinghouses. State and Local GIS Data Clearinghouses ​ State governments, local governments, and non-profits often maintain GIS data repositories. ​ Examples in Oklahoma: ○​ Oklahoma Water Resources Board: Groundwater and surface water data. ○​ Oklahoma Geographic Information Council: Digital ortho-photography. ○​ Association of Central Oklahoma Governments (ACOG): Local transportation data. ○​ City of Norman: Planning, zoning, and oil/gas data. Federal GIS Data Clearinghouses ​ Federal agencies manage datasets critical for infrastructure, environmental protection, and national security. ​ Examples of agencies and their datasets: ○​ National Geospatial Intelligence Agency (NGA): Global place names database. ○​ US Fish and Wildlife Service: Species distribution and critical habitat data. ○​ US Environmental Protection Agency (EPA): Environmental data.  ○​ National Weather Service: Climate and weather data. Key Federal Data Sources: 1. US Geological Survey (USGS) ​ Produces vector and raster data for natural feature mapping. ​ Common datasets: ○​ Digital Elevation Models (DEMs): Raster data representing elevation. ○​ Digital Ortho Photo Quads (DOQs): Spatially corrected aerial imagery. ○​ National Hydrography Dataset (NHD): Rivers and streams as vector line datasets. 2. US Census Bureau (TIGER Data) ​ Manages topographic and demographic GIS data through TIGER (Topologically Integrated Geographic Encoding and Referencing) products. ​ TIGER includes: ○​ Census geographies: Block groups, census tracts, counties, metropolitan areas, states (vector polygons). ○​ Roadways, railways, hydrography (vector lines). ​ FIPS Codes: ○​ Unique identifiers for census geographies. ○​ Hierarchically structured to reflect geographic nesting. ○​ Examples: ​ State-level: Two digits. ​ County-level: Five digits. ​ Census tract-level: Eleven digits. Significance of FIPS Codes: ​ Facilitate linking tabular data (e.g., population statistics) with spatial data. ​ Example: ○​ A FIPS code (e.g., 40027201201) indicates location in state 40, county 027, and census tract 201201. Importance of Data Familiarity: 1.​ Understanding Dataset Design and Limitations: ○​ Familiarity with datasets (e.g., TIGER or USGS products) is crucial to avoid errors.  ○​ GIS software does not review or validate the quality of maps you produce. 2.​ Common Mistakes to Avoid: ○​ Assuming data capabilities without verification. ○​ Misinterpreting attributes or spatial relationships. ○​ Failing to check for dataset updates or quality issues. 3.​ Responsibility for Accuracy: ○​ As a map creator, you are accountable for ensuring that your maps are accurate. ○​ Inaccurate maps can misinform and may have real-world consequences. Key Takeaways: 1.​ Leverage Reputable Data Sources: ○​ Avoid recreating existing datasets—use state and federal agencies' resources. 2.​ Understand Your Data: ○​ Know the limitations and intended uses of datasets. 3.​ Accountability: ○​ Ensure your maps are accurate and represent data truthfully, as they are often consumed as factual by the public. By following these guidelines, GIS practitioners can save time, improve the quality of their maps, and maintain credibility in their work. 1. Introduction to Georeferencing ​ Definition: Georeferencing refers to the process of associating data with a specific location on Earth using coordinate systems. ​ Purpose: Enables GIS to represent features accurately on a map. Key Properties of Georeferencing Systems 1.​ Uniqueness: Locations must be uniquely identifiable (e.g., distinguishing Springfield, MO, from Springfield, UK). 2.​ Persistence: Locations remain consistent over time. 3.​ Spatial Resolution: Specifies level of detail (e.g., "Norman" is more precise than "Oklahoma"). 4.​ Metric vs. Non-Metric: ○​ Metric: Uses coordinate systems to calculate distances mathematically (e.g., lat/long). ○​ Non-Metric: Uses relative locations (e.g., "next to the river"). 2. Geographic Coordinate System (GCS) ​ Description: Uses latitude and longitude lines to specify locations. ​ Components: ○​ Latitude (Parallels): Measures north-south position. ​ Ranges from 0° at the equator to ±90° at the poles. ○​ Longitude (Meridians): Measures east-west position. ​ Ranges from 0° at the Prime Meridian (Greenwich, England) to ±180°. ​ Units: Angular degrees, minutes, and seconds (or decimal degrees). ○​ Example: Norman, OK = 35°13'N, 97°26'W. Terms to Remember ​ Meridian: A line of constant longitude. ​ Parallel: A line of constant latitude. ​ Unprojected Nature of GCS: Cannot compute accurate areas or distances without projection. 3. Map Projections ​ Purpose: Converts Earth’s 3D surface into a 2D map. ​ Challenges: Introduces distortions in:  ○​ Area: Size of features. ○​ Shape: Geometric appearance. ○​ Distance: Space between points. ○​ Direction: Angles or bearings. Projection Properties 1.​ Equal-Area (Equivalent): Preserves area but distorts shape. 2.​ Conformal (Orthomorphic): Preserves shape but distorts area. 3.​ Equidistant: Preserves distances (only in specific areas). 4.​ True-Direction (Azimuthal): Preserves angles/directions. 5.​ Compromise: Balances distortion among properties. Projection Factors 1.​ Developable Surfaces: Shapes onto which the Earth is projected. ○​ Cylindrical: Gridlines form rectangles (e.g., Mercator). ○​ Conical: Gridlines curve (e.g., Albers Equal Area). ○​ Planar: Gridlines radiate from a central point (e.g., Azimuthal). 2.​ Aspect: Orientation of the developable surface. ○​ Normal: Aligned with the poles. ○​ Transverse: Aligned with the equator. ○​ Oblique: At an angle. 3.​ Viewpoint: Light source for projection. ○​ Gnomonic: From the Earth's center. ○​ Stereographic: From the far side of the globe. ○​ Orthographic: From infinity. 4.​ Case: Intersection of the developable surface with Earth. ○​ Tangent: Touches at one line or point. ○​ Secant: Cuts through the Earth. 4. Common Map Projections 1.​ Albers Equal Area: ○​ Type: Conic. ○​ Properties: Preserves area; distorts shape, distance, and direction. ○​ Uses: Regional/national maps. 2.​ Lambert Conformal Conic: ○​ Type: Conic. ○​ Properties: Preserves shape and direction; distorts area. ○​ Uses: Navigation, State Plane Coordinate System. 3.​ Mercator: ○​ Type: Cylindrical.  ○​ Properties: Preserves direction and shape; distorts area at high latitudes. ○​ Uses: Navigation, world maps. 4.​ Azimuthal Equidistant: ○​ Type: Planar. ○​ Properties: Preserves distances and directions from a central point. ○​ Uses: Polar maps, air/sea navigation. 5. Projected Coordinate Systems (PCS) ​ Definition: A grid superimposed on a map projection to specify locations in a plane. ​ Components: ○​ Ellipsoid: A mathematical model of Earth’s shape (e.g., GRS80, WGS84). ○​ Datum: Links the ellipsoid to specific Earth locations (e.g., NAD83). ○​ Projection: Converts the 3D Earth into 2D coordinates. Common PCSs 1.​ Universal Transverse Mercator (UTM): ○​ Divides the globe into 60 zones, each 6° wide. ○​ Uses meters for coordinates (e.g., easting and northing). ○​ Popular for GPS mapping. 2.​ State Plane Coordinate System (SPCS): ○​ Divides states into smaller zones to minimize distortion. ○​ Uses different projections (e.g., Lambert for wide zones, Mercator for tall zones). 6. Practical Applications ​ GCS vs. PCS: ○​ GCS: Based on lat/long; unprojected; suitable for global contexts. ○​ PCS: Projected; allows accurate distance, area, and direction measurements. ​ Choosing the Right Projection: ○​ Depends on the purpose of the map: ​ Preserve area for population density studies. ​ Preserve distance for transportation routes. ​ Preserve direction for navigation. Understanding Vector Data and Attributes ​ Vector Data: Composed of points, lines, or polygons. These objects are linked to tables storing information (attributes) about each object. ○​ Attribute Tables: The non-spatial data stored for each vector object. ​ Point Attribute Table: Contains attributes for point features. ​ Line Attribute Table: Contains attributes for line features. ​ Polygon Attribute Table: Contains attributes for polygon features. ​ Table Structure: ○​ Rows (Records): Represent individual objects (e.g., a specific building or road). ○​ Columns (Fields): Store different types of information about the objects (e.g., name, size, type). ​ Relational Database: GIS links spatial data to attribute data, functioning as a spatial database. This relationship enables operations like: ○​ Attribute queries ○​ Attribute calculations ○​ Table joins Attribute Table Data Types ​ Numerical Data: ○​ Short Integer: Whole numbers with small ranges, used for categories or simple numeric data. ​ Example: Codes (1, 2, 3) or small ranges (-32,768 to 32,767). ○​ Long Integer: Whole numbers with larger ranges, used for longer codes or larger numeric data. ​ Example: ZIP codes or population counts. ○​ Float: Numbers with decimals, used for fractional calculations or precise measurements. ​ Example: Population density (123.45 people/sq. mile). ○​ Double: Higher-precision decimal numbers, used for calculations requiring greater accuracy. ​ Example: Geographic coordinates with high precision. ​ Text/String Data: ○​ Stores alphanumeric characters. ○​ Example: Building names ("Library"), categories ("Urban"), or hours ("08:00"). ○​ Numeric-like data (e.g., "12345") can also be stored as strings when arithmetic calculations are unnecessary. ​ Date/Time Data: ○​ Stores temporal information. ○​ Example: Event timestamps, trajectory data. Attribute Data Management Tasks 1.​ Attribute Calculations: ○​ Modify or compute new data within an attribute table using tools like the Field Calculator. ○​ Operations include arithmetic manipulations (e.g., converting feet to miles: Length_feet / 5280). 2.​ Attribute Queries: ○​ Automates record selection based on specific conditions. ○​ Utilizes Structured Query Language (SQL) to define criteria. ​ Example: State = 'Oklahoma' AND Population < 1000. ○​ Logical operators: ​ =, , =: Equality or comparison. ​ AND: Combines conditions (both must be true). ​ OR: Combines conditions (either can be true). ​ NOT: Excludes specific records. 3.​ Table Joins: ○​ Combines two tables based on a shared field (key field). ○​ Types: ​ Simple Join (1:1): Matches one record in Table A to one record in Table B. ​ Summarized Join (1:Many): Aggregates data from multiple records in Table B for each record in Table A. ○​ Direction matters: ​ Joining A → B vs. B → A can yield different results. ○​ Example: Joining income data to census tracts using a shared "tract ID" field. Practical Examples 1.​ Attribute Calculations: ○​ Field Shape_Length (in feet) → Convert to miles: ​ Formula: Shape_Length / 5280. 2.​ Attribute Queries: ○​ Find census tracts in Oklahoma with a population under 1000: ​ Query: State = 'Oklahoma' AND Population < 1000. 3.​ Table Joins: ○​ Scenario: A polygon attribute table lacks income data but has tract IDs. Income data is in another table with tract IDs. ○​ Solution: ​ Perform a table join using "tract ID" as the key field.  ​ Result: The income data is now appended to the polygon attribute table. Key Exam Takeaways 1.​ Understand Attribute Data Types: ○​ Know when to use integers (short vs. long), floats, doubles, strings, and date/time fields. 2.​ Know Table Operations: ○​ Attribute Calculations: Modify fields using mathematical or statistical operations. ○​ Attribute Queries: Retrieve specific records using SQL-like syntax. ○​ Table Joins: Combine data from multiple sources for comprehensive analysis. 3.​ Recognize Logical Operators: ○​ AND, OR, NOT are essential for constructing complex queries. 4.​ Consider Relationships in Joins: ○​ 1:1: Direct match between tables. ○​ 1:Many: Summarizes multiple matches into one. 5.​ Importance of Spatial Integration: ○​ Joining attribute data to spatial objects enhances the ability to perform spatial analysis and create meaningful visualizations. 1. Spatial Calculations in GIS ​ Spatial vs. Attribute Data: Spatial data contains both geometry (spatial information) and attributes (non-spatial information). Unlike attribute calculations, which focus on non-spatial attributes, spatial calculations involve properties related to feature geometry based on X, Y coordinate vertices. ​ Spatial Operations: These include calculations like distance, perimeter, area, centroid, and minimum enclosing rectangle. These calculations involve arithmetic operations on spatial properties. 2. Euclidean Distance ​ Definition: The Euclidean distance is the straight-line distance between two points in space. ​ Formula: You can calculate it using the Pythagorean theorem. For example, the distance between points (−2,−3)(-2, -3)(−2,−3) and (−4,4)(-4, 4)(−4,4) can be calculated as: Distance=(x2−x1)2+(y2−y1)2\text{Distance} = \sqrt{(x_2 - x_1)^2 + (y_2 - y_1)^2}Distance=(x2​−x1​)2+(y2​−y1​)2​ ​ Application to other objects: When working with lines or polygons, you find the point on each object closest to the other and calculate the distance between those points. ​ Impact of Spatial Definition: The calculation method depends on how the spatial relationship is defined in GIS. For example, the distance between a polygon and a line could vary depending on whether the closest point is on the edge or the vertices of the objects. ​ Practical Considerations: When using tools like the "Near Tool" in GIS software, it will calculate the distance between objects using the closest edge or vertices. 3. Perimeter and Length ​ Polygon Perimeter: The perimeter of a polygon or the length of a polyline is the sum of all its edge lengths. Each edge is calculated using Euclidean distance (Pythagorean theorem). ​ Example: If a polygon has four edges of lengths 7, 7, 3, and 3 units, the total perimeter is 7+7+3+3=207 + 7 + 3 + 3 = 207+7+3+3=20 units. 4. Area Calculation Using the Trapezoid Algorithm ​ Trapezoid Algorithm: This method calculates the area of polygons by approximating the shape with trapezoids and summing their areas. ○​ Process: 1.​ The vertices of the polygon are numbered in order, starting from the leftmost point. 2.​ Trapezoids are formed by dropping lines from the vertices to the X-axis.  3.​ The area of each trapezoid is calculated and then summed. If a trapezoid is below the polygon, its area is subtracted. ​ Formula for a Trapezoid: Area=b1+b22×h\text{Area} = \frac{b_1 + b_2}{2} \times hArea=2b1​+b2​​×h where b1b_1b1​and b2b_2b2​are the lengths of the parallel sides, and hhh is the height. ​ Complex Shapes: This method works for non-rectangular shapes as well. ​ Example: The area of a polygon with several vertices can be computed by summing the areas of the trapezoids formed by each pair of vertices. 5. Centroid Calculation ​ Definition: The centroid is the center of mass (or geometric center) of a polygon and is calculated by averaging the X and Y coordinates of its vertices. ​ Calculation: ○​ The centroid’s X coordinate is the average of the X coordinates of all vertices. ○​ Similarly, the centroid’s Y coordinate is the average of the Y coordinates. ​ Example: For a polygon with vertices at (2,2),(4,1),(4,4),(6,6),(8,6)(2, 2), (4, 1), (4, 4), (6, 6), (8, 6)(2,2),(4,1),(4,4),(6,6),(8,6), the centroid is calculated as: Xcentroid=2+4+4+6+85=4.8X_{\text{centroid}} = \frac{2 + 4 + 4 + 6 + 8}{5} = 4.8Xcentroid​=52+4+4+6+8​=4.8 Ycentroid=2+1+4+6+65=3.8Y_{\text{centroid}} = \frac{2 + 1 + 4 + 6 + 6}{5} = 3.8Ycentroid​=52+1+4+6+6​=3.8 ​ Issues: The centroid may fall outside the polygon, especially for irregular shapes. This is common in GIS applications. ​ Uses: Centroids are used to reduce storage by representing polygons as points, calculate distances between objects, geocode locations, and determine site suitability (e.g., the center of a forest for a watchtower). 6. Minimum Enclosing Rectangle (MAR) ​ Definition: The minimum enclosing rectangle (MAR) is a rectangle that surrounds an object (point, line, or polygon), defined by the minimum and maximum X and Y values of the object. ​ Example: For a polygon with vertices at specific coordinates, the MAR is drawn based on the outermost points. ​ Uses: ○​ Computational Efficiency: MAR reduces computational effort when checking for intersections between objects. If the MARs do not overlap, the objects themselves do not intersect. ○​ Bounding Geometry: MAR is used for default extents in calculations and clipping background layers. ○​ GIS Tools: The MAR can be calculated using the "Minimum Bounding Geometry" tool, specifically the "Envelope" option. 7. Conclusion ​ Spatial Calculations Summary: Spatial calculations in GIS involve arithmetic operations that identify spatial properties of feature geometry. Common calculations include distance, perimeter, area, centroid, and the minimum enclosing rectangle. ​ Important Concepts: ○​ Understanding the definition of spatial relationships between objects (e.g., how distance is calculated). ○​ Knowing how geometries are recorded in GIS. ○​ Using the appropriate GIS tools for these spatial operations. Spatial Queries in GIS Spatial Queries are used to select geographic features based on their location or spatial relationship with other features. Unlike attribute queries (which search by attribute values like population or name), spatial queries work with geometry (the shape and position of objects in space). They help answer questions like: "Which polygons intersect with this point?" or "Which areas are within a certain distance of this feature?" Key Spatial Relationships: 1. Intersect: Selects features that overlap (fully or partially) with another feature. ○ Works with all vector objects (points, lines, polygons). 2. Within a Distance: Creates a buffer around features and selects those that intersect the buffer zone. ○ Works for all vector objects. 3. Within: Selects features where the geometry is inside another feature’s geometry (e.g., selecting points inside a polygon). ○ A polygon cannot be "within" a point (as a point is 0-dimensional). 4. Completely Within: Selects features that are completely inside another feature (without touching boundaries). 5. Contains: Opposite of "within." The source feature's geometry must contain the target feature's geometry (including boundaries). Select by Location in ArcGIS: This tool is used to conduct spatial queries. Target Layer: The layer from which you want to select features. Source Layer: The layer you're comparing to. Spatial Relationship: Defines how the two layers are spatially related (e.g., intersect, within). Common Mistake: When checking how many features were selected, make sure you're looking at the correct layer's attribute table, not the total count for all layers. Spatial Joins in GIS A Spatial Join is a type of join where features from one layer are added to another layer based on their spatial relationship. Unlike attribute joins (which are based on a common field), spatial joins use geographic relationships between features. Key Types of Spatial Joins: 1. Distance-based Join: Joins based on proximity, such as the nearest feature or those within a set distance.  2. Containment-based Join: Joins features based on spatial containment (e.g., a park contained within a county). Types of Spatial Joins: 1. Simple Join: A one-to-one or many-to-one relationship where attributes are copied directly from the source to the target feature. 2. Summarize Join: Used when there are many records in the source layer that need to be summarized (e.g., counting how many parks fall within a county). ○ You can calculate statistics like count, average, sum, etc. Example: Polygon to Polygon Join (e.g., county data to park data): ○ Summarize Join: Counts how many parks intersect each county. ○ Simple Join: Copies attributes of the first county that a park intersects. Geometry Types in Spatial Joins: Point to Point Point to Line Point to Polygon Line to Line Line to Polygon Polygon to Polygon Additional Notes: Spatial queries and joins in GIS can involve complex calculations, but the software (like ArcGIS) helps automate this process. The Plumb Line Algorithm is a common method used in spatial queries, such as checking if a point is within a polygon. Overview of Vector Geoprocessing Definition of Geoprocessing ​ Geoprocessing involves transforming spatial objects into new or modified ones. ​ Unlike spatial queries or spatial joins (which do not modify geometry), geoprocessing modifies both attributes and geometry. ​ Common modifications: ○​ Creation or deletion of features. ○​ Changes in size or shape. ○​ Physical cutting of features. Key Questions for Geoprocessing Operations 1.​ Does the operation change the geometry? 2.​ Does the operation change the attributes? 3.​ What is retained versus discarded in the process? Types of Geoprocessing Operations 1.​ Overlays 2.​ Other Geoprocessing Operations Overlay Operations Overlay operations involve placing one or more spatial layers on top of another to determine spatial relationships and generate new information. They require at least two layers. 1. Clip ​ Purpose: Cuts a portion of the first input layer using a second input layer as a "cookie cutter." ​ Result: Retains geometry from the input layer within the clip boundary. ​ Attributes: ○​ The output table matches the input layer attributes. ○​ No attributes from the clip feature are included. ○​ Example: If the input area is physically reduced, the attribute table (e.g., area field) will not update automatically. 2. Erase  ​ Purpose: Removes the part of the first input layer that overlaps with the second input layer. ​ Result: Retains geometry outside the erase layer boundary. ​ Attributes: ○​ Matches the input layer attributes. ○​ No attributes from the erase feature are included. 3. Union ​ Purpose: Combines all areas of overlap and non-overlap from two or more input layers. ​ Result: Produces polygons that reflect all overlaps and non-overlapping features. ​ Attributes: ○​ Output contains attributes from all input layers. ○​ Overlapping features are duplicated, which can cause double-counting of area or features. 4. Intersect ​ Purpose: Retains only areas common to all input layers. ​ Result: ○​ Output geometries can be polygons, lines, or points depending on input. ○​ Only overlapping areas are included. ​ Attributes: ○​ Retains attributes from all input layers. 5. Identity ​ Purpose: Combines input layer geometry with attributes from an identity layer, but keeps all features from the input layer. ​ Result: ○​ Geometry: Retains input layer geometry. ○​ Attributes: Adds attributes from the identity layer where overlap occurs; assigns null values where no overlap exists. Other Geoprocessing Operations These operations modify features and attributes but do not involve overlapping layers. 1. Merge ​ Purpose: Combines features from two or more layers into a single layer. ​ Result: Produces a unified layer containing all input features. ​ Attributes: ○​ Attributes can be summarized or preserved, depending on user specifications.  ○​ Example: Combining state-level data for a region (e.g., Midwest). 2. Dissolve ​ Purpose: Aggregates features based on specified attributes, forming larger features from smaller ones. ​ Result: ○​ Reduces the number of features by combining polygons that share a common attribute. ○​ Example: Combining counties into water management districts. ​ Attributes: ○​ Summarized based on the aggregation process. 3. Buffer ​ Purpose: Creates a zone of specified width around a spatial feature. ​ Result: Produces a polygon layer, regardless of whether the input is a point, line, or polygon. ​ Attributes: ○​ Can dissolve overlapping buffers to prevent double-counting of areas. Comparing Operations Overlay Operations vs. Other Operations ​ Overlay operations involve spatial relationships between layers and often produce attributes from multiple sources. ​ Other operations focus on modifying single layers or combining layers without creating spatial relationships between them. Critical Questions For each geoprocessing operation, consider: 1.​ Geometry: ○​ Is it modified? How? ○​ How many features are created or remain? 2.​ Attributes: ○​ Are they retained or modified? ○​ Which attributes are included in the output? Practical Considerations  1.​ Area Calculation: ○​ Geometry modifications (e.g., clip, erase) require manual updating of area-related fields in the attribute table. ○​ Overlaps in buffers or unions can lead to overestimation if not dissolved or handled carefully. 2.​ Choosing the Right Tool: ○​ Geoprocessing operations are goal-specific. ○​ Example: ​ Use clip to define boundaries. ​ Use union to combine overlapping attributes. ​ Use buffer for distance-based analyses. Summary ​ Geoprocessing is a fundamental aspect of spatial analysis in GIS. ​ Overlay operations like clip, erase, union, intersect, and identity are used to analyze spatial relationships. ​ Other operations like merge, dissolve, and buffer modify or combine features and attributes for different purposes. ​ A deep understanding of how each operation affects geometry and attributes ensures accurate and meaningful analysis results. Raster Operators for Analysis: Local Functions The Raster Data Model represents continuous data using grid cells, contrasting with the object-based Vector Data Model. Raster analysis focuses on different tools and operations, categorized into four main function types: local, focal, zonal, and global. This lecture focuses on local functions, which analyze data on a cell-by-cell basis. Categories of Local Functions 1.​ Reclassification ○​ Binary Masking: Assigns values (0 or 1) to represent absence/presence or suitability. Often an intermediate analysis step. ○​ Classification Reduction: Consolidates multiple attribute types into fewer classes, e.g., merging coniferous and deciduous forests into "forest." ○​ Classification Ranking: Assigns ranks or weights for further analysis, such as in least-cost path analysis. 2.​ Arithmetic Operations ○​ Include addition, subtraction, multiplication, and division applied to corresponding cells across layers. ○​ Example: Converting elevation data from feet to meters by multiplying by 0.305. 3.​ Logical Statements ○​ Boolean AND: Both conditions must be true for a value of 1; otherwise, the output is 0. ○​ Boolean OR: If either condition is true, the value is 1. ○​ Logical queries and map algebra can be performed using raster calculators. 4.​ Proximity Analysis ○​ Euclidean Distance: Calculates the straight-line distance of each cell to the nearest source cell. Useful as an intermediate step for analyses like buffering. Key Takeaways ​ Raster and vector operations are inherently different due to the data models. ​ Local functions focus on individual cells or corresponding cells across layers. ​ The local level serves as a foundation for advanced raster analyses involving focal, zonal, and global operations. Next, we'll explore focal functions, which include neighborhoods or windows surrounding cells. Additional Context for Focal Operations Definition and Purpose: Begin with a broader definition of focal operations, emphasizing their role in analyzing spatial patterns and relationships, such as terrain analysis, resource management, and land-use planning. Comparison to Other Raster Analysis Categories: Briefly compare focal operations to zonal and global operations to provide a full context of raster analysis. Applications and Real-World Examples Filtering: ○ Include examples like smoothing satellite imagery to reduce noise or enhancing contrast for land-use detection. ○ Highlight how filtering can be used in hydrology (e.g., refining digital elevation models for watershed analysis). Focal Statistics: ○ Provide use cases, such as calculating the average temperature or precipitation within neighborhoods for climate studies. ○ Discuss how focal statistics can identify hot spots or patterns of interest in public health data. Spatial Aggregation: ○ Emphasize its relevance in downscaling or generalizing data for computational efficiency and trend analysis (e.g., regional vegetation cover studies). ○ Mention methods like majority or mode statistics for categorical data aggregation (e.g., dominant land-use type). Euclidean Distance and Allocation: ○ Include real-world scenarios like determining accessibility to healthcare facilities or public transportation. ○ Discuss their applications in urban planning, such as proximity to schools, hospitals, or green spaces. Buffering in Raster Analysis: ○ Explore examples such as habitat preservation (e.g., buffering rivers to protect riparian zones) or disaster preparedness (e.g., creating buffer zones around fault lines). Slope Analysis: ○ Provide examples such as identifying areas prone to landslides, designing hiking trails, or modeling agricultural suitability. Technical Enhancements  Calculation Methods: ○ Add more technical detail on how focal functions compute values (e.g., kernel convolution for filtering). ○ Discuss the importance of window shape and size in determining analysis accuracy and sensitivity. Data Preparation: ○ Include a section on preprocessing requirements, such as ensuring consistent resolution and handling NoData cells to avoid skewed results. Software-Specific Features: ○ Highlight tools in various GIS platforms (ArcGIS, QGIS) for conducting focal analysis, including advanced configuration options. Advanced Considerations Handling Large Datasets: ○ Provide tips on optimizing focal operations for large rasters, such as parallel processing or tiling. Combining Operations: ○ Explain how focal operations integrate with other GIS tools for multi-criteria analysis (e.g., combining slope and distance maps for site selection). Limitations and Caveats: ○ Discuss potential pitfalls, such as edge effects and computational intensity for large or high-resolution datasets. Summary and Visuals Add a summary table contrasting the various focal operators and their use cases. Include or reference illustrative visuals or diagrams for each operation type to help with conceptual understanding. Raster Analysis: Zonal and Global Functions Categories of Raster Functions 1.​ Local Functions: New cell values depend on the same cell in one or more input layers. 2.​ Focal Functions: New cell values depend on neighboring cells. 3.​ Zonal Functions: Operate on groups of cells (zones) treated as single units. 4.​ Global Functions: Treat the entire raster as one unit of analysis. Zonal Functions ​ Definition of Zones: ○​ Homogeneous areas based on specific attributes. ○​ Zones can be contiguous (connected) or non-contiguous (separate). ​ Basic Operations: ○​ Parceling: Identifies zones by assigning unique values to clusters of cells with similar attributes. ​ Achieved through reclassification or converting polygons to rasters. ​ Example: Assigning unique values to lakes in a raster. ○​ Area & Perimeter Calculations: ​ Summing cell dimensions based on spatial resolution. ​ Less accurate for irregular shapes unless zones align with raster orientation. ​ Zonal Statistics: ○​ Calculations like mean, maximum, or standard deviation for each zone. ○​ Examples include calculating land cover area or analyzing specific attribute values. Global Functions ​ Apply to the entire raster as a single unit. ​ Examples of global operations: ○​ Global Statistics: Mean, maximum, or standard deviation for the entire dataset. ○​ Found in the Source tab in software like ArcGIS. Map Algebra ​ Combines local, focal, zonal, and global operations for complex analyses. ​ Performed using tools like the raster calculator in ArcGIS.  ○​ Example: Combine focal statistics from one layer with local operations on another. Key Takeaways ​ Understand the distinctions between local, focal, zonal, and global functions. ​ Recognize that zonal and global operations, while fewer, are crucial for real-world applications and often serve as inputs to other analyses. ​ Mastery of these concepts is essential for effectively leveraging tools like ArcGIS in unscripted scenarios.