Geospatial Intelligence - 2nd Year, 1st Semester PDF

GEOSPATIAL INTELLIGENCE 2nd Year, 1 st Semester 1. INTRODUCTION TO GIS “An integrated collection of computer software and data used to view and manage information about geographic places, analyze spatial relationships, and model spati...

GEOSPATIAL INTELLIGENCE 2nd Year, 1 st Semester 1. INTRODUCTION TO GIS “An integrated collection of computer software and data used to view and manage information about geographic places, analyze spatial relationships, and model spatial processes. A Geographic Information System (GIS) provides a framework for gathering and organizing special data and related information so that it can be displayed and analyzed. As so, it is a toll for working with geographic information” GIS are used: (some examples) o To fight crimes o Cope with natural disasters o Protect endangered species o Treat epidemics o Reduce pollution o Improve public health It´s widely used for a range of purposes: o An urban planner might want to assess the extent of urban fringe growth in this city and quantify population growth o A natural hazard analyst might look to identify the high-risk areas of annual monsoon-related flooding o A mining engineer could be interested in determining which prospective copper mines should be selected for future exploration o A geoinformatics engineer hired by a telecommunications company may want to determine the best sites for the company´s real stations o A forest manager might want to optimize timber production using data on soil and current tree distribution o A tourism planner may be interested to investigate which is the best place to build a hotel Some History of GIS o The origin of GIS is associated with Cartography history and with the first attempts to represent graphically space o GIS history is indissociable from human history, where maps assumed always a fundamental role in the process of spatial knowledge production and an adequate language for their communication Characteristics of Geographic Information First law of geography (Waldo Tobler 1970) Everything is related to everything else, but near things are more related than distant things (physical and temporal distance) Fractal´s Principle (Benoit Mandelbrot, 1983) “a repeated geometric pattern at progressively smaller scales, which produce irregular shapes and surfaces” 1 Uncertainty of geographic information Uncertainty arises from the way that GIS users conceive of the world, how they measure and represent it, and how they analyze their representation of it. Function of GIS Data collection: aerial photography; satellite images; scanning; digitizing; global positioning system (ex. GPS); surveys; cell phone data; LIDAR data; Wi-Fi data Data storing processing and analysis: store data; query data; analyze data Output production: display data; produce output Community involvement and participation: community participation is seen as important for asserting some degree of local control over decisions on development plans and for enhancing commitment to their implementation. GIS are used to provide the maps which were used to facilitate locals and planners’ discussion and provide focus at public meeting Decision support: provide the necessary information to perform calculations, visualize results, and therefore, support several decisions. The value-added info is a product of GIS ability to identify patterns or relationships based on particular criteria thanks to its graphical display, data manipulation and spatial analysis and modelling functions. Components of GIS People: o Define and develop procedures used by a GIS o The most important part of GIS, and is related to anyone that uses a GIS Methods and procedures o Simple steps taken in a well-defined and consistent method to produce correct and reproducible results from the GIS system o The procedures used to input, analyse and query data determine the quality and validity of the final product Data o A GIS can ingest spatial and non-spatial data in different type of formats Hardware o Determines the speed at which a GIS will operate; attention: enough space to store our data is needed o It may influence the type of software used and the personas of the people working with the GIS Software o It encompasses all the software used for the databases, drawing, statistics and imaging o Determines the type of problems that the GUS may be used to solve o Must match the skills and needs of the end-user 2  Network o Refers to both computer and social network o Assists in the dissemination of data (transfer datasets, collaboration) o Display of information (web maps, web applications, paper maps) Spatial data models Data in GIS represent a simplified view of physical entities. A spatial data model may be defined as the objects in a spatial database and their relationships. Most GIS stores data as a set of layers, each one with the respective spatial and attributes data, including, for example, soil data, population data, elevation data or road data as an example. Coordinate systems Geographic coordinate system (GCS) -> defines where the data is located on the earth’s surface (location stored in degrees) o Advantage: Any point on Earth’s surface is accurately represented o Disadvantage: Time-consuming arithmetic calculations (compute distances or areas). Latitude and longitude numbers are distorted when plotted in a projected coordinate system Projected coordinate system (PCS) -> draws the data on a flat surface (location stored in meters) o Advantage: Calculations between points and areas are easy, and graphic representations are realistic o Disadvantage: Projections introduce errors in the exact location. Depending on the projection these errors can be in distances, sizes, shapes or directions Conceptualization of spatial data models Vector Three types: point, lines, polygons Are represented by x,y coordinates Information about features are called attributes and are stored in a table Useful to represent discrete data (ex. streets, rivers, administrative boundaries) Advantages: o Make maps look more like maps we are used to seeing on paper o The shapes of features are accurately represented o Is good for managing attributes o Has smaller storage requirements Disadvantages: o Complicated data structure o Software must manage many data tables o Not good for representing continuous features o Slower processing time Raster Data is represented as a surface modelled by a matrix of values (pixels) Useful to represent continuous data (ex. satellite imagery, elevation, slope, aspect, population) Advantages: 3  o Good for representing continuous surfaces o A simple data structure o Easier for the computer to make analytical calculations Disadvantages: o Maps can be blocky looking o Cells can only be coded for one attribute when there may be more than one attribute at each location o Can have very large datasets (depending on the size of the grid cell) 2. ARCGIS PRO Single & comprehensive desktop GIS application that supports data visualization, advanced analysis, and authoritative data maintenance in 2D, 3D and 4D. It supports data sharing across a suite of ArcGIS products such as ArcGIS Online and ArcGIS Enterprise and enables users to work across the ArcGIS system through Web GIS. Features: Exploration and Visualization Cartography and Design Imagery Analytics and Data Science Data management Share your Work Customize and Create Launch New Capabilities ABOUT ArcGIS: Learning Resources -> Have resources to help you develop skills, solve problems, and answer questions. Learning resources can also be accessed from a side tab on the Settings page or from the Learning Resources button on the Help tab of the ribbon. Projects -> body of related work that may include maps, scenes, layouts, and connections to resources such as system folders and databases. Project files have the extension.aprx. By default, a project is stored in its folder along with an associated file geodatabase and toolbox. 4  ArcGIS Pro user interface o Ribbon -> A horizontal ribbon at the top of the application window organizes functionality in a series of tabs. Some tabs (core tabs) are always present. Others (contextual tabs) appear when the application is in a particular state. For example, a contextual Feature Layer tab set appears when a feature layer is selected in the Contents pane of a map o Views -> Views are windows for working with maps, scenes, layouts, tables, charts, the catalogue, and other representations of your data. Several views can be opened at the same time. o Panes -> window that displays the contents of a view (the Contents pane), the items in your project or active portal (the Catalog pane), or commands and settings for an area of functionality (the Symbology pane, the Geoprocessing pane, and so on). Panes provide functionality that may not be available on the ribbon. Panes may have rows of text tabs and graphical tabs to organize functionality. Some panes have multiple pages Navigate maps and scenes o Explore Tool -> default mouse navigation and feature identification tool for both maps and scenes. It incorporates most 2D and 3D navigation functionality and can be coupled with keyboard shortcuts. An on-screen navigator can also be used to pan, zoom, rotate, and tilt the view Create a project o ArcGIS Pro helps you organize and manage the resources related to your work. To do this, it uses a project file (.aprx) as its default file type. An ArcGIS Pro project can contain maps, scenes, layouts, and other items. It can also contain connections to data stored in folders, databases, and servers. Maps, layers, and other GIS content can be added from portals such as your ArcGIS organization or ArcGIS Living Atlas of the World. The content you create in ArcGIS Pro can be shared with your portal. o Add wilderness data to the map -> ArcGIS Living Atlas is a collection of authoritative geographic data that can inform a variety of analyses. To add data from ArcGIS Living Atlas or ArcGIS Online to ArcGIS Pro, you must be signed into your organization which is typically automatic. o Add data from a folder -> You can add data to your project from any folder, database, or other resource to which you make a connection. Every project has a default connection to its home folder—the folder in which the project file, default geodatabase, and default toolbox are stored. Explore data -> Spatial data and attribute data are complementary. Spatial data represents various aspects of geography as layers on a map. Attribute data stores information about those layers as rows and columns in a table. Layers can be queried, symbolized, and analyzed by their attributes to uncover geographic patterns and relationships Author a map -> The first step in making a map is defining the map's purpose. The second step is acquiring and preparing data to support the purpose. The third step is authoring the map. Authoring a map involves making cartographic decisions about how to present data so the map achieves its purpose. In a dynamic environment, where map users can zoom and pan, the map should be designed to work at a variety of scales. It's also important to consider accessibility issues such as colour vision deficiencies 5 3. DATA SOURCES & DATA ENTRY Spatial data ✓ Spatial data, also known as geographic data or geographic information, can be a representation on a map of real-world features and phenomena; however, it can also be any information with a location attached to it, whether on a map or not. ✓ A GIS integrates location and attribute information, storing information about where something is with information about what something is. Spatial data is part of everyday life. At work, you might work with customer addresses, tracking the delivery of packages, or analyzing patterns of sales across a region. Outside of work, mobile devices can use location services to find nearby restaurants and businesses, provide turn-by-turn navigation, or track exercise activity. Types of data Discrete Data: Represents real-world features with well-defined, distinct boundaries. Examples include rivers, buildings, or streets. Discrete features are clear-cut in their spatial extent. Examples: Roads, Districts, Addresses, Soils, utility infrastructure (gas, electricity, water) Continuous Data: Refers to phenomena that do not have distinct boundaries and are measured across a continuum. Examples are elevation, temperature, and rainfall. These phenomena change values across space but do not have clear-cut separations. Examples: annual rainfall, slope, ocean temperature → To ascertain whether data is discrete or continuous, one can consider if averaging values makes sense. If averaging is logical (like average rainfall or temperature), it indicates continuous data. If not (like building types or street names), the data is discrete. Vector data ✓ Vector data model represents discrete objects like streetlights, roads, and buildings as point, line, and polygon (area) features, that have both geometric properties and attributes. Point: A single XY coordinate location or vertex. Line: Made of two or more connected XY coordinate locations or vertices. Polygon: Consists of three or more connected XY coordinate locations or vertices, forming a closed loop. Types of Attributes: → In an attribute table, each column represents a field, or attribute, of the data. Attribute values can be numbers, dates, text, or other values, such as rasters or multimedia files. Automatically Generated: Like ObjectID, Shape, Shape_Length, and Shape_Area. User-Defined: Attributes created and updated by users, offering additional information about the features. Vector Geometry (depends on the scale and purpose of the map) Small-Scale Maps: Show large geographic areas with less detail, suitable for point representations. Large-Scale Maps: Show smaller areas with more details, suitable for more complex geometries like polygons and lines. 6 Raster data ✓ The raster data model represents the surface of the earth as a grid of equally sized cells. An individual cell represents a portion of the earth, such as a square meter or a square mile. Each cell contains the information for that part of the earth. → The terms "raster" and "image" are often used interchangeably, but they should not be. An image is a 2D pictorial representation, while a raster is the data model used to store information in rows and columns of cells. All images are rasters, but not all rasters are images. For example, a dataset that shows rainfall levels is considered a raster but is not an image. → Rasters can contain several bands of information, each captured at a different wavelength. These bands can represent different data types, like visible and infrared light. Raster Attributes Cell-Based Storage: Information in a raster is stored in its cells. The attribute of each cell is typically determined by averaging the value over the space it covers. Resolution: The size of the cells determines the raster's resolution. Smaller cells cover less space and hence provide more detail. Multiband Attributes: In multiband rasters, each cell contains attributes from each band. This can show more than one attribute per cell. Types of Raster Data Continuous and Discrete Rasters: Rasters can represent continuous data (like temperature or elevation) and discrete data (like soil types). Imagery: Commonly used type of raster data, including satellite photos, aerial images, and drone imagery. Scanned Maps: Print maps can be converted into rasters through scanning. These retain the appearance of vector data but are essentially static raster images. Practical Applications Elevation Raster: An example given might be a digital elevation model (DEM), where cell values represent elevations in meters, depicted through varying shades in the raster. Color and Clarity: The presentation might demonstrate changing a raster's symbology to represent elevation values with a broader range of colours for clarity. Multiband Raster Exploration: Investigating a multiband raster, possibly a scanned topographic map, and understanding how different bands contribute to the overall colour and information of the raster. Data Management Using Non-GIS Data in a GIS Integration of Diverse Data: Emphasizes that not all data used in GIS originally comes in a GIS-friendly format. For instance, data from spreadsheets or databases can be mapped in GIS if they contain location information like addresses, geographic divisions, or coordinates. Business Intelligence Solutions: Highlights the enhancement of solutions like Microsoft Power BI with GIS capabilities, showcasing the integration of business data with spatial analysis. Data Extraction from Unstructured Sources: GIS can extract critical information like coordinates or place names from unstructured data (e.g., PDFs, social media posts) and map them. 7 Creating and Sharing Maps Mapping Spreadsheet Data: Explains the process of mapping spreadsheet data, focusing on the use of location information within spreadsheets (like XY coordinates or addresses) to create spatial data representations in GIS. Preparation for Sharing: Discusses preparing data for sharing, including the use of metadata and ensuring data integrity and comprehensiveness for dissemination. Sharing via Web Layers: Covers the process of sharing GIS data as web layers, allowing for broader accessibility and integration with platforms like ArcGIS Online. Enhancing Data in GIS Data Enrichment: Details the process of enhancing spatial data by adding additional layers or attributes from authoritative sources available through platforms like ArcGIS Online. This can involve augmenting a simple dataset with more complex or detailed information for richer analysis. Analysis and Insight Generation: Illustrates how enriched data can be used for generating deeper insights, transforming basic spatial datasets into more valuable tools for decision-making and analysis. Editing in ArcGIS PRO Creating Features and Attributes Editing Features: Involves creating new features, modifying existing ones, or deleting them. Editing can be done on geometry (shape and size) or attribute information (data associated with the feature). Types of Editable Features: Points, lines, polygons, multipatch features, and annotations. These features can originate from various data sources like geodatabases, shapefiles, or hosted feature layers. Editing Scope: Editing can be applied to a wide range of feature types and from different data sources, including enterprise geodatabases or file geodatabases, and editable feature layers from ArcGIS Online or ArcGIS Enterprise. Editing Templates Feature Templates: Essential for creating features in ArcGIS Pro. Each editable feature layer has an automatically created feature template when added to the map. Users can modify these templates or create new ones. Template Settings: Control default layer storage, feature attributes, and the default tool used for feature creation. Snapping Tools Snapping Functionality: Helps in avoiding errors during editing by aligning features to coincide with other features or points when the pointer is within a specified distance, known as the snapping tolerance. Maintaining Accuracy: Snapping is crucial for maintaining the accuracy and geometric integrity of features and preserving topology. 4. ATTRIBUTE DATA AND TABLES Selection based on Attributes Attribute Data in GIS: Spatial features in GIS, such as points, lines, and polygons, have associated attribute data. This data is stored in attribute tables and contains information about each feature, like names, types, sizes, or any other descriptive data. 8  Attribute-based Selection: This process involves selecting spatial features based on criteria set within their attribute data. For example, selecting all cities with a population greater than a million. How it Works: Querying Attribute Tables: The selection is typically done by querying the attribute tables using a structured query language (SQL). The query specifies the criteria that must be met for a feature to be selected. Criteria Specification: The criteria can range from simple (like a specific value in a field) to complex (involving multiple fields, conditional statements, or mathematical operations) Tools and Functions in GIS Software Selection Tools: GIS software like ArcGIS Pro provides tools for attribute-based selection, often accessible via a graphical user interface allowing for easy specification of query conditions. Visualization: Once the selection is made, the GIS software can visually highlight or isolate the selected features on the map for further analysis or processing. Benefits and Applications Data Analysis and Management: Enables efficient data analysis and management by focusing on relevant subsets of a dataset. Decision Making: Facilitates informed decision-making in various fields like urban planning, environmental management, resource allocation, etc. Joining or Relating Tables Key techniques for data integration allow for richer analysis and insights by combining spatial data with other relevant datasets. This process enhances the depth and breadth of GIS analyses, making it a fundamental skill in geospatial intelligence. ✓ Joining Tables: Joining involves combining two tables based on a common attribute or field. The result is a single, extended table where each row contains the combined data from both tables. This is typically used when the data in one table relates directly to the data in the other. ✓ Relating Tables: Relating, on the other hand, associates tables based on a key field but keeps them separate. This allows for a more flexible relationship where a single record in one table can relate to multiple records in another. → It's important to maintain data integrity and accuracy during joining or relating. Mismatched or erroneous keys can lead to incorrect results. Data compatibility should be checked too ensuring that the key fields in both tables are compatible in data type and format. Types of Joins: One-to-One: Each record in the first table corresponds to one record in the second table. One-to-Many (Relating): One record in the first table corresponds to multiple records in the second table 9 How to Winnow Columns ✓ The term 'winnow' essentially means to reduce or thin out. In the context of GIS, it involves selecting and retaining only those columns (fields) in a data table that are necessary for a particular analysis or task, thereby eliminating unnecessary data. → How it works: 1) Identifying Relevant Data: First, determine which columns (attributes) are essential for your analysis. This can depend on the goals of your project, the questions you are trying to answer, and the type of analysis you plan to perform. 2) Removing Unnecessary Columns: Once relevant columns are identified, the next step is to remove or hide the columns that are not needed. This can be done manually in the attribute table or through a query or script. Benefits: Enhanced Performance: Reduces the load on the GIS software, potentially improving performance. Focused Analysis: Helps focus the analysis on relevant data, reducing complexity and potential for errors. Joining Two Existing Tables ✓ Involves merging two tables where they have a shared attribute or key. For example, if one table contains geographical data (like locations of cities) and another contains demographic data (like population), these can be joined using a common field (like city names). Types of Joins Inner Join: Only includes records where the key values exist in both tables. Left/Right Join: Includes all records from the left (or right) table, and the matched records from the right (or left) table. Unmatched records will have NULL values in the columns brought from the other table. Full Join: Combines all records from both tables, filling in NULLs where there is no match. Benefits: Comprehensive Analysis: Allows for richer data analysis by integrating different types of data. Flexibility: Offers flexibility in data analysis and decision-making by providing a more complete dataset. Summarizing Tables ✓ Process of consolidating detailed attribute data into summarized, often statistical, formats. This method is essential for understanding the broader patterns or trends within large datasets. Techniques Involved: Common techniques include calculating sums, averages, counts, minimums, and maximums. Usage Scenarios: For instance, summarizing a table of traffic incidents by region to find the total number or the average severity of incidents in each area. Outcome: Helps in transforming raw data into actionable insights by providing a high-level view of the data. 10 Select by Location ✓ GIS query method used to select features based on their spatial relationship with other features in the dataset. Spatial Queries: Involves spatial relationships within a distance, intersecting, contained by, or adjacent to. Examples: Selecting all parks within a 1-mile radius of schools or identifying buildings intersecting proposed roadways. Importance: It’s crucial for spatial analysis tasks such as site selection, impact assessment, and urban planning. Select by Proximity and Adjacency ✓ focuses on identifying spatial features that are either near each other or share common boundaries. Proximity Analysis: Often involves buffer zones, where features within a specified distance from a point, line, or area are selected. Adjacency Analysis: Concerned with features that share a common border or are directly next to each other. Application: Useful in environmental studies, urban planning, and resource management, such as identifying properties adjacent to protected areas or facilities close to hazardous sites. Adding an External Table ✓ Incorporating a table from an outside source into your GIS project. This enhances the GIS database with additional data that was not initially part of it. Data Integration: Includes importing, aligning, and sometimes transforming the external data to ensure compatibility with the existing GIS data. Example: Adding a table of census data to a map of administrative boundaries for demographic analysis. Benefits: Provides a more holistic view of the data landscape, allowing for richer and more comprehensive spatial analyses. 5. BASIC SPATIAL ANALYSIS Selection ✓ Cover techniques and methods for choosing specific spatial data based on defined criteria. Types of Selection Methods By Attributes: Selecting features based on the values in their attribute fields. Useful for narrowing down data for analysis, like selecting all rivers in a dataset that are classified as 'perennial'. By Location (Spatial Selection): Choosing features based on their spatial relationships with other features. This might include proximity, overlap, or being within a specified area. For example, selecting buildings within a flood zone or roads that intersect with a particular land use area. 11 Classification ✓ Involves organizing spatial data into categories or classes, usually based on one or more attributes. This is essential for thematic mapping and analyzing spatial patterns. Classification simplifies complex data, making it easier to analyze and interpret, and is useful for identifying patterns and relationships within the data. Types of Classification Methods Quantitative Classification: Involves grouping data based on numerical attributes. This might include interval, ratio, or ordinal data. Techniques can include equal interval classification, quantile classification, or natural breaks (Jenks) classification. Qualitative Classification: Based on categorical data, such as land use types, vegetation classes, or urban zoning categories. It involves grouping features based on shared characteristics that are not necessarily numerical. Application in Spatial Analysis Thematic Mapping: Classification is often used in creating thematic maps where different classes are represented by unique colours or symbols. Analysis of Spatial Trends: Helps in identifying and visualizing trends, such as areas of high urbanization, regions with similar climatic conditions, or zones of varying soil types. Dissolving ✓ GIS operation that combines adjacent or overlapping polygons into larger single polygons based on shared attributes. The main goal is to simplify complex polygonal maps by reducing the number of features and focusing on larger areas that share common characteristics. Enhances spatial analyses like area calculations or regional studies by consolidating detailed data into more general forms. Buffering ✓ Creating a zone around map features (points, lines, or polygons) at a specified distance. These zones are known as buffers. Buffers are used to analyse spatial relationships, such as proximity or impact zones. Buffer distances can be uniform for all features or variables based on specific attribute data. Overlay ✓ Technique of placing one map layer on top of another to examine relationships between different types of spatial data. This method is used to integrate and analyse multiple datasets, revealing complex spatial relationships and patterns. Types of Overlay Operations Vector Overlays: o Involves combining vector data layers (like points, lines, and polygons). o Common types include intersect, union, and difference operations. For instance, intersecting land use maps with flood zones to identify vulnerable areas. Raster Overlays: o Combining raster data layers, often using mathematical operations. o Used for analyses like environmental impact studies, where different factors (e.g., pollution, land cover) are overlaid to assess cumulative effects. 12 6. RASTER ANALYSIS Map Algebra ✓ Set of operations used in spatial analysis and GIS to manipulate raster datasets. It involves applying mathematical operations to the cell values of one or more raster layers. The fundamental idea is to perform arithmetic, logical, or statistical calculations on a cell-by-cell basis across raster layers. Types of Operations in Map Algebra Arithmetic Operations: This can include adding, subtracting, multiplying, or dividing the values of corresponding cells in different raster layers. For example, adding a raster of precipitation data to a raster of irrigation data to get total water availability. Logical Operations: Involves applying logical operators like AND, OR, or NOT. For instance, identifying areas that are both within a certain elevation range (using a logical AND operation between two elevation rasters) Local Functions ✓ Refer to operations applied to each cell (or pixel) in a raster dataset independently. Each cell's value is modified based on the cell itself and potentially its immediate neighbours. The process involves analyzing and altering the value of each cell without considering the broader context of the dataset. Types of Local Functions Arithmetic Operations: Includes basic mathematical operations like addition, subtraction, multiplication, and division applied to the cell values. Logical Operations: Involves applying logical conditions (like greater than, less than, equal to) to each cell. Reclassification: Changing cell values based on specific criteria. For example, reclassifying a land cover raster to simplify its categories. Neighbourhood, Zonal, Distance and Global Functions ✓ Neighborhood Functions -> involve analyzing the value of a raster cell by considering a specified neighbourhood around it. Commonly used in smoothing or sharpening data, edge detection in images, or calculating slope or aspect. Example: Calculating the average elevation in a 3x3 cell window around each cell in a digital elevation model (DEM). ✓ Zonal Functions -> operate on groups of cells within a raster that share a common value or characteristic, defined by a 'zone'. Used for calculating statistics (like mean, sum, or standard deviation) within zones. Example: Determining the average land cover type within different watershed areas. ✓ Distance Functions -> calculate the distance from each cell to a set of target cells in a raster. Useful in proximity analysis, such as finding the nearest hospital to each location on a map. Example: Computing the distance from each cell in a land use map to the nearest road. ✓ Global Functions -> Consider the entire extent of the raster dataset for their computations. Applied in analyses that require a comprehensive view of the data, like watershed delineation or viewshed analysis. Example: Identifying areas in a landscape visible from a lookout point using viewshed analysis. 13 7. NETWORK ANALYSIS Preparing for Network Analysis ✓ A network is a collection of interconnected parts or elements, often used to represent potential routes for transportation of people, resources, and goods, like road networks. Network analysis involves modelling these potential travel paths to perform various types of analyses, with routing (finding the shortest path between two points) being the most common. Using network datasets for network analysis: o Best Route -> Quickest and Shortest path. Useful for urgencies and reduce costs o Closest Facility -> Allows you to find the locations that are easiest to reach from a place o Origin–Destination Matrix -> Origin-destination cost matrices create time-distance tables from o multiple origins to multiple destinations. o Service Areas -> Generating service areas helps you determine a region (for instance, all o streets that are traversed within a specific distance or time from a location) o Fleet Vehicle Routing -> Routing for a group of vehicles is available to optimally sequence and schedule their stops for maximum efficiency within time, traffic, and other cost constraints, like driver work shifts or customer commitments o Location – Allocation -> helps you choose which facilities from a set of facilities to operate based on their potential interaction with demand points, such as which store locations would be the best to close while still serving the most customers possible Network Elements ✓ Network datasets are composed of network elements. Network elements are created from the source features used to create the network dataset Edges: Linear features connecting via junctions, enabling travel. Edges are directional, having both 'From-To' and 'To-From' aspects. Junctions: Connect edges, facilitating navigation. Derived from vertices or point features, they often represent street intersections. Turns: Store information affecting movement between edges. Kinds of attributes Cost –> function as impedances, which penalize traversal over an element in the network. Network datasets must have at least one. ex. drive time, road surface Descriptor -> contains general information, often referenced by one of the other three attribute types, to calculate their values. ex. number of lanes, speed limit Restriction -> prohibit traversing certain edges (roads) in certain directions. ex. road constructions Hierarchy -> differentiates among road types to help network analysis, and it allows a network dataset to assign priority. ex. highway, primary route Enhancing Network Analysis Functionality: Multimodal network support allows routing over different transportation types. The ability to support multimodal analyses depends on the data source format. Elevation data can be used to model overpasses or bridges where roads cross without intersecting. The direction of allowed travel and traffic consideration is important for accurately modelling real- world conditions and finding the most efficient routes. 14 Creating Optimized Routes The network analyst tool that uses a network dataset to calculate the best route is the Route solver. Cost values are what make one route more desirable than another. The most used cost is distance: a route will take a certain amount of time because of how far away the destination is. Any route must-have stops, which are the points or places along the route. Their sequence is important Barriers: the best route found might not be the best in reality because there might be obstructions or barriers that have not been included in the model. Adding this barrier would allow ArcGIS to return more realistic results. Service area Analysis ✓ Region accessible from a specific location within a certain travel time or distance. Service areas are used to assess coverage, accessibility, or efficiency, often visualized as concentric areas around a point demonstrating how coverage is affected by various costs in the network. Finding the Closest Facility ✓ ArcGIS Network Analyst extension that calculates the nearest facility or facilities to one or more incidents, considering distance or travel time. Allows for simultaneous analyses of multiple incidents to find the nearest facility for each. Finding the Optimal Location of Facilities Facilities in Location-Allocation: o Candidate Facilities: Potential locations suitable for the event or structure being analyzed. o Required Facilities: Existing facilities already in place. o Competitor Facilities: Represent competing businesses in specific problem scenarios. 8. SPATIAL INTERPOLATION ✓ Defined as predicting variables at unmeasured locations based on sampled values at known locations. This is essential in various fields, such as estimating air pollution levels, soil moisture, or population density across regions. Interpolation Methods: o Nearest Neighbor Interpolation (Thiessen Polygon Interpolation): Assigns the value of the nearest sample location to unsampled locations. o Fixed Radius – Local Averaging: Estimates cell values within a raster grid based on the average of nearby samples. o Inverse Distance Weighted (IDW) Interpolation: Uses sampled values and distances to nearby known points, with the influence of each point inversely proportional to its distance. o Splines: Mathematical functions that interpolate along a smooth curve through a set of points. Spatial Prediction ✓ Different from interpolation, spatial prediction is based on mathematical models often built via statistical processes. It involves predicting important but unknown variables using coordinate locations and measured or observed independent variables. Spatial prediction addresses spatial autocorrelation and may include cross-correlation between different variables. 15 Moran’s statistic ✓ Established measure of spatial correlation. If Moran’s: I = 1 (perfect spatial correlation – clustered values) ; I = 0 (perfect randomness - low spatial correlation) ; I = -1 (perfect anti-correlation – dispersed values) Kriging ✓ Statistically based estimator of spatial variables Predictions are based on regionalized variable theory, which includes three main components: Spatial trend, Local spatial autocorrelation, Random, stochastic variation These three components are combined in a mathematical model to develop an estimation function. The function is then applied to the measured data to estimate values across the study area. Like IDW interpolators, weights in kriging are used with measured sample variables to estimate values at unknown locations. With kriging, the weights are chosen in a statistically optimal fashion, given a specific kriging model and assumptions about the trend, autocorrelation, and stochastic variation in the predicted variable. We may plot the semivariance over a range of lag distances, and this plot is known as a variogram or semivariogram. A variogram summarizes the spatial autocorrelation of a variable. The semivariance is usually small at small lag distances and increases to a plateau as the lag distance h increases o Nugget: is the initial semivariance when the autocorrelation typically is highest o Sill: inherent variation when there is little autocorrelation o Range: the lag distance at which the sill is reached. Interpolation Accuracy ✓ Accuracy is measured at assessment points, locations where we know both the true value and the estimated values for a variable. ▪ Root Mean Squared Error (RMSE): Error-values are squared to remove the sign effect, and then the square root is taken on the sum to return to the measured unit scale, instead of a squared unit scale ▪ Mean Absolute Error (MAE): is an alternative error metric, less often used but less sensitive to outliers than the RMSE 9. 3D GIS – DIGITAL TWINS ✓ 3D GIS Modeling -> Involves creating three-dimensional representations of real-world environments. This includes buildings, terrain, infrastructure, and natural features. Utilizes various data sources like LiDAR, aerial imagery, and ground surveys to create accurate 3D models. Used for urban planning, construction, landscape visualization, and environmental studies. ✓ Digital Twins -> Virtual replica of a physical object or system. In GIS, this often refers to a 3D model that mirrors real-world environments. Digital twins are not static; they can be updated with real-time data to reflect current conditions. Common in smart city projects, infrastructure management, and disaster response planning where real-time monitoring and simulation are crucial. In urban planning and construction projects, engaging the community and stakeholders with interactive 3D models is crucial for transparent communication and feedback gathering. The use of colours, symbols, and styles to represent different data themes, is especially important in conveying information clearly in 3D models. 16 10. SHARE AND PUBLISH SPATIAL INFORMATION ArcGIS Online: o Cloud-Based GIS Platform: ArcGIS Online is presented as a cloud-based geographic information system, enabling the creation, use, and sharing of geospatial content across organizations, communities, and the public web. o Software-as-a-Service Model: The platform operates on a software-as-a-service (SaaS) model, utilizing web technology for communication between servers and clients, including web browsers and various ArcGIS applications. o Capabilities: It offers a range of capabilities for map-making, sharing maps and apps, collaboration, data analysis, and working with data. o Content Types: The types of content integral to ArcGIS Online, such as layers, maps, scenes, apps, and files, are discussed, emphasizing their roles in geospatial analysis and sharing. o Sharing Levels: Explains the different levels of sharing within ArcGIS Online, including private, group, organization, and public, and how they control access to content. o Groups and Collaboration: Groups in ArcGIS Online are highlighted as a way to organize and collaborate on content related to specific themes or areas of interest. Story Maps: o Combining GIS Maps and Multimedia: Story Maps combine GIS maps with text, images, and other multimedia to present topics interactively. o Components of a Geographic Story: Theoretical aspects of creating a geographic story are covered, including the use of titles, summaries, interactive maps, links, photographs, videos, immersive sections, and timelines. o Developing a Geographic Story: The process of creating a story with ArcGIS StoryMaps is discussed, highlighting steps such as visualizing the purpose, drafting narrative text, assembling media, authoring using the interactive story builder, and publishing. 11. SPATIAL DATA QUALITY Data Accuracy ✓ Accuracy in spatial data reflects how closely the data represents real-world phenomena in terms of shape, location, or characteristics. Errors in spatial data can arise from several sources, including the data model conceptualization, data collection methods, human error, and outdated information. Raster data models, for example, assume homogeneous pixels, leading to potential errors if multiple categories or values are found within a single pixel. Similar generalization errors can occur in vector datasets. Types of Errors in Data Collection: Errors in feature locations can be due to GNSS, digitized maps, or aerial photographs. These positional errors can arise from optical, mechanical, and human deficiencies. Errors can also result from changes over time, as the dynamic world may alter while the spatial dataset captures a static snapshot. Components of Spatial Data Accuracy: Positional Accuracy: The closeness of data set locations to the true locations of real-world entities. 17  Attribute Accuracy: The accuracy of attribute values compared to their true values, reported as mean errors or proportions. Logical Consistency: The presence or absence of inconsistent data, often requiring comparison among themes. Completeness: How well the data set captures all intended features, with omissions reflecting incompleteness. → Accuracy is most reliably determined through the comparison of true values to those represented in the spatial data set. The approach to assessing accuracy varies based on the type of spatial data, with different methods for evaluating nominal attribute data and measurements recorded on continuous scales. 12. MAPS INTEGRATION IN POWERBI ArcGIS Maps in Power BI Integration and Visualization: Power BI is a mapping visualization tool that enhances data reports and dashboards. It allows for the integration of authoritative data layers and spatial analysis to explore complex data relationships. Creating and Viewing Maps: Outlines the process of creating and viewing ArcGIS map visualizations within both Power BI service and Power BI Desktop. Map Formatting and Customization: Details on various map formatting options in ArcGIS for Power BI, including layer management, map tools settings, and location type specification. Data Analysis and Interactivity: Explains how ArcGIS for Power BI provides tools for data analysis, such as selecting locations, searching for addresses, adding reference layers, and creating drive-time areas. It also discusses the use of interactive infographic cards for demographic information. Mapbox Drill Down Choropleth Map How do we create it? 1. Prepare your data -> Might include geographic boundaries at different hierarchical levels and it should be in a compatible format with PowerBI (typically Excel) 2. Convert Spatial Data for Mapbox -> in the geoJSON format 3. Create a new report and load the data 4. Model data relationships -> Arrange the relationships between tables, ensuring that the spatial data is correctly linked to your thematic data 5. Configure the Mapbox Visual -> Customize the visual appearance such as colours, legends and tooltips 6. Interactivity and Drill-Down-Functionality -> allows to click on a map at a higher level and drill down into more detailed levels 7. Finalize and Publish Why is it important? Enhance data visualization and comprehension Allows interaction. The drill-down capability allows to explore at different levels of granularity Customization and Flexibility -> allows easy updates and customizations Decision Support & Communication -> effective way to communicate with the audience 18