Podcast
Questions and Answers
A GIS should process, store, and transfer data, but does not need to combine these components.
A GIS should process, store, and transfer data, but does not need to combine these components.
False (B)
Working with spatial data involves acquisition, storage and maintenance, analysis, and dissemination.
Working with spatial data involves acquisition, storage and maintenance, analysis, and dissemination.
True (A)
Spatial data in a GIS can be stored digitally using world coordinates.
Spatial data in a GIS can be stored digitally using world coordinates.
True (A)
Geo-informatics deals with only a few aspects of spatial data handling.
Geo-informatics deals with only a few aspects of spatial data handling.
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All commercial GIS packages have the exact same strengths and weaknesses.
All commercial GIS packages have the exact same strengths and weaknesses.
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ArcGIS is an open-source desktop GIS application.
ArcGIS is an open-source desktop GIS application.
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ILWIS only integrates image and vector data, and can't handle thematic data.
ILWIS only integrates image and vector data, and can't handle thematic data.
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Analytical functions in GIS software allow the derivation of new geoinformation from existing data.
Analytical functions in GIS software allow the derivation of new geoinformation from existing data.
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A map with a scale of 1:1000 shows more detail than a map with a scale of 1:2,500,000.
A map with a scale of 1:1000 shows more detail than a map with a scale of 1:2,500,000.
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Map scale refers to the cell width of the tessellation applied to spatial data.
Map scale refers to the cell width of the tessellation applied to spatial data.
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Digital spatial data stored in a GIS inherently possesses a scale.
Digital spatial data stored in a GIS inherently possesses a scale.
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GIS has no application in emergency management.
GIS has no application in emergency management.
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Cartography involves the making of maps.
Cartography involves the making of maps.
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GIS cannot be used to map land surface temperature.
GIS cannot be used to map land surface temperature.
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The lecture on Data Processing Systems was scheduled for February 7, 2024.
The lecture on Data Processing Systems was scheduled for February 7, 2024.
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Understanding the functionality of a GIS is not an objective of the Data Processing Systems lecture.
Understanding the functionality of a GIS is not an objective of the Data Processing Systems lecture.
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Data processing systems consist of hardware components only.
Data processing systems consist of hardware components only.
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GIS cannot be applied to boundary dispute survey.
GIS cannot be applied to boundary dispute survey.
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Overlay functions are used at the beginning of data analysis.
Overlay functions are used at the beginning of data analysis.
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Neighbourhood functions combine features at the same location.
Neighbourhood functions combine features at the same location.
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Primitives of vector data include points, lines, and polygons.
Primitives of vector data include points, lines, and polygons.
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Location, length, distance and area size are geometric measurements related to raster data.
Location, length, distance and area size are geometric measurements related to raster data.
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In raster data, the area size of a cell is variable.
In raster data, the area size of a cell is variable.
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The area size of a selected raster section is determined by multiplying the number of cells by the vector area size.
The area size of a selected raster section is determined by multiplying the number of cells by the vector area size.
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Spatial selections can only be made based on geometric grounds.
Spatial selections can only be made based on geometric grounds.
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Selection objects in interactive spatial selection can be points, lines, or polygons.
Selection objects in interactive spatial selection can be points, lines, or polygons.
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The most important logical connectives are AND, OR, NOT, and the square bracket pair $[, ,]$
The most important logical connectives are AND, OR, NOT, and the square bracket pair $[, ,]$
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The statement Area < 400000 NOR LandUse = 80 will select areas for which either condition holds.
The statement Area < 400000 NOR LandUse = 80 will select areas for which either condition holds.
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The NOT connective can be applied to negate a condition.
The NOT connective can be applied to negate a condition.
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Brackets can be applied to discourage grouping amongst atomic parts of a composite condition.
Brackets can be applied to discourage grouping amongst atomic parts of a composite condition.
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Lines can contain polygons, lines, or points.
Lines can contain polygons, lines, or points.
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Selecting features that are inside selection objects makes use of the containment relationship between spatial objects.
Selecting features that are inside selection objects makes use of the containment relationship between spatial objects.
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Classification is a technique of purposefully revealing detail from an input data set.
Classification is a technique of purposefully revealing detail from an input data set.
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If a classification is performed on a dataset that was itself the result of a classification, it is called a regression.
If a classification is performed on a dataset that was itself the result of a classification, it is called a regression.
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Raster overlays suffer from computational disadvantages when performing cell by cell analysis.
Raster overlays suffer from computational disadvantages when performing cell by cell analysis.
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The language used to express operations on raster is referred to as raster calculus.
The language used to express operations on raster is referred to as raster calculus.
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Proximity computations rely solely on the characteristics of the target locations.
Proximity computations rely solely on the characteristics of the target locations.
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Buffer zone generation is a method used in proximity computations.
Buffer zone generation is a method used in proximity computations.
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Spread computation can expect the material to disperse evenly in all directions.
Spread computation can expect the material to disperse evenly in all directions.
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A medical clinic's neighborhood can be defined by various travel distances.
A medical clinic's neighborhood can be defined by various travel distances.
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Neighbourhood analysis requires identifying target locations and defining their spatial extent.
Neighbourhood analysis requires identifying target locations and defining their spatial extent.
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Spread computation may consider external factors like pollution and terrain differences.
Spread computation may consider external factors like pollution and terrain differences.
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Overlay functions in GIS are used to combine data layers and derive new information.
Overlay functions in GIS are used to combine data layers and derive new information.
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The primitives of vector data sets include point, (poly)line, and rectangle.
The primitives of vector data sets include point, (poly)line, and rectangle.
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Neighbourhood functions evaluate properties solely based on a feature's location.
Neighbourhood functions evaluate properties solely based on a feature's location.
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Measurements on raster data layers are complex due to the irregularity of the cells.
Measurements on raster data layers are complex due to the irregularity of the cells.
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Interactive spatial selection involves defining selection conditions by drawing spatial objects on a screen.
Interactive spatial selection involves defining selection conditions by drawing spatial objects on a screen.
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A common use of area size measurements in vector data is to sum up areas of all polygons belonging to a specific grade type.
A common use of area size measurements in vector data is to sum up areas of all polygons belonging to a specific grade type.
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The area size of a selected part of the raster is calculated by dividing the number of cells by the cell area size.
The area size of a selected part of the raster is calculated by dividing the number of cells by the cell area size.
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Geometric selection grounds are the only criteria for selecting features in spatial data sets.
Geometric selection grounds are the only criteria for selecting features in spatial data sets.
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Flashcards
Analytical Capabilities of GIS
Analytical Capabilities of GIS
Functions that allow data exploration without major changes, used initially in data analysis.
Overlay Functions
Overlay Functions
Core functions in GIS that combine data layers to derive new information, creating features in a new layer.
Neighbourhood Functions
Neighbourhood Functions
Evaluate characteristics of an area surrounding a feature’s location, not just the feature itself.
Vector Data Measurements
Vector Data Measurements
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Raster Data Measurements
Raster Data Measurements
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Spatial Selection Queries
Spatial Selection Queries
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Interactive Spatial Selection
Interactive Spatial Selection
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Selection Objects
Selection Objects
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Map Scale
Map Scale
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Large-Scale Map
Large-Scale Map
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Small-Scale Map
Small-Scale Map
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Resolution in GIS
Resolution in GIS
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GIS Applications
GIS Applications
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Mapping Malaria Incidence
Mapping Malaria Incidence
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Crop Yield Mapping
Crop Yield Mapping
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Boundary Dispute Survey
Boundary Dispute Survey
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Data Processing Systems
Data Processing Systems
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Database Management Systems
Database Management Systems
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AND Operator
AND Operator
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OR Operator
OR Operator
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NOT Operator
NOT Operator
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Brackets in conditions
Brackets in conditions
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Containment Relationship
Containment Relationship
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Selection by Attribute
Selection by Attribute
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Classification
Classification
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Reclassification
Reclassification
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Raster calculus
Raster calculus
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Neighbourhood analysis
Neighbourhood analysis
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Proximity computation
Proximity computation
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Buffer zone generation
Buffer zone generation
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Spread computation
Spread computation
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Target locations
Target locations
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Geometric distance
Geometric distance
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Source material
Source material
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GIS Functions
GIS Functions
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Geo-informatics
Geo-informatics
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GIS Packages
GIS Packages
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Intergraph’s GeoMedia
Intergraph’s GeoMedia
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GRASS GIS
GRASS GIS
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Quantum GIS (QGIS)
Quantum GIS (QGIS)
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SAGA GIS
SAGA GIS
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Analytical Functions in GIS
Analytical Functions in GIS
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Classification Functions
Classification Functions
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Exploration of Data
Exploration of Data
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Cell Resolution in Raster Data
Cell Resolution in Raster Data
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Area Size Measurement in Vector Data
Area Size Measurement in Vector Data
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Geometric Grounds for Selection
Geometric Grounds for Selection
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Attribute Data in Spatial Selection
Attribute Data in Spatial Selection
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Defined Selection Conditions
Defined Selection Conditions
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Derived Information from Overlays
Derived Information from Overlays
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Study Notes
Course Information
- Course Title: Geographic Information Systems
- Course Code: GL/MN 455
- Instructor: Prof Bernard Kumi-Boateng
- Date of Presentation: February 7, 2025
- Institution: University of Mines and Technology, Tarkwa, Ghana
Presentation Outline (Page 2)
- Semester's Activities
- Course Schedule
- Assessment of Course
- Course Syllabus
- Source of Lecture Material
- Objectives of Course
- Overview of GIS(Day's Lecture)
Semester's Activities (Page 3)
- Lectures
- Assignment
- Quizzes
Course Schedule/Plan (Pages 4 & 5)
- Schedule details include weeks, dates, months, and activities
- January: Overview of GIS I, Tutorial on ArcGIS 1 & 2, Overview of GIS II, Tutorial on ArcGIS 3 & 4, Data Processing Systems I, Tutorial on ArcGIS 5 & 6
- February: Data Processing Systems II, Tutorial on ArcGIS 7 & 8, Data Entry and Presentation I, Tutorial on ArcGIS 7 & 8, Spatial Data Analysis I, Tutorial on ArcGIS 10
- March: Spatial Data Analysis II, Data Visualisation I, Quiz 1, Data Visualisation II, Catch-up, Data Visualisation III, Catch-up
- April: Catch-up, First Semester Examinations
Assessment of Course (Page 6)
- Student assessments are in two forms:
- Continuous Assessment (40%)
- End of semester examination (60%)
- Assessment of the Lecturer:
- Online questionnaire for student feedback on the course and lecturer performance
Course Syllabus (Page 7)
- General Introduction of GIS
- Databases in GIS
- Introduction to GIS Analytical Tools
- Topological Relationship in GIS
- Performing Analysis in a GIS Environment
Source of Lecture Material (Page 8)
- ITC text book series on Principles of GIS
Course Objectives (Pages 9 & 10)
- Student will:
- Design and Use Maps
- Explain the elements of GIS Output
- Work with Spatial Data
- Be able to Digitize Maps
- Apply Geo-processing techniques
- Be able to use ArcGIS 3D Analyst
- Be proficient in the use of ArcGIS Software
- Be able to Design a GIS Project
Today's Lecture (Page 11)
- Overview of GIS
Lecture Outline (Page 12)
- Lecture Objectives
- Gentle Introduction to GIS
- Fundamental Problem of GIS
- Stages of Working with Geographic Data
- Definition and Purpose of GIS
- Representation of the Real World
- GIS Application
Lecture Objectives (Page 13)
- Define and state an overview of GIS Concepts
- Explain the function of GIS
- Describe Spatial Relationships
- Be able to organise Spatial Data
Gentle Introduction to GIS (Page 14)
- Everyday experiences occur in geographic space
- Maps can be used to:
- Find places
- Save time while travelling
- Decide where to locate new waste sites
- Plan cities
- Guide the development of wildlife preserves
- Help make better and faster decisions with computer power
Example Applications (Page 15)
- Geologist: Identify best construction locations in earthquake-prone areas based on rock formations
- Mining Engineer: Determine suitable gold mines considering ore body extent, depth, and quality
Geographic Space (Pages 16 & 17)
- Professionals work with data related to space, typically positional data relative to Earth's surface
Fundamental Problem of GIS (Page 18)
- Spatio-temporal problems:
- Geographic dimension: objects of study vary in characteristics based on location
- Temporal dimension: objects of study vary in characteristics across different time periods
Stages of Working with Geographic Data (Page 19)
- Data preparation and entry
- Data analysis
- Data presentation
GIS Defined (Page 20)
- GIS is a system for capturing, storing, manipulating, analyzing, managing, and presenting geographic data
- Combination of cartography, statistics, and computer science technology
- Digitally creates and manipulates spatial areas
Purpose of GIS (Page 21)
- Understanding the environment (e.g., land use, natural hazards, geology, hydrology)
- Representing geographical space (natural, man-made or a mix)
- Understanding geographical phenomena (studying, understanding changes, forecasting, devising action plans)
- Spatio-temporal issues
Spatial Data and Geo-information (Page 22)
- Data: representations for computer operation
- Spatial data: data with positional values
- Geo-spatial data: geo-referenced spatial data
- Information: data interpreted by humans
Geographic Phenomena (Pages 23 & 24)
- Manifestation of an entity or process of interest;
- Can be named or described
- Geo-referenced
- Assigned a time (interval) of presence/occurrence
Representation of Phenomena (Pages 24 & 25)
- Representing phenomena in GIS involves stating what it is and where it is
- Some phenomena occur everywhere, while others are localized phenomena
- (Geographic) fields: variables like temperature, pressure and elevation
- (Geographic) discrete fields: land use, soil classifications
Geographic Fields (Page 26)
- Continuous Fields: Elevation, temperature
- Discrete Fields: soil type, land cover type, lithology type
Representation of Phenomena (Page 27)
- Many other phenomena don't manifest everywhere, but only in certain localities
- Geographic examples of objects: buoys at a port
- General rule: Natural phenomena are usually fields, while man-made phenomena are usually objects.
Geographic Objects (Page 28)
- Characteristics of Geographic Objects:
- Location
- Shape
- Size
- Orientation
- Object Boundaries: Crisp (man-made) or fuzzy (natural)
Representation of Real World (Pages 29-32)
- Representations of real-world GIS comprise models and databases
- GIS models are map representations (miniature of real world parts.)
- Databases store the real-world details, including spatial data
Computer Representations (Pages 33 & 34)
- Raster data model: Regular, Irregular tessellations
- Vector data model: Topological (structured) and unstructured (spaghetti)
Raster Data and Vector Data (Page 35)
- Vector: Data using points, lines, polygons for discrete boundaries (e.g., country borders)
- Raster: Data as a grid of cells for continuous-variable data (e.g., satellite images)
Computer Representations (Page 36)
- Visual representations of raster and vector data models
Organization of Spatial Data (Page 37)
- Data is organized in layers representing either a field (continuous or discrete) or a collection of objects
- A data layer includes spatial and attribute data
The Temporal Dimension (Pages 39 & 40)
- Geographic phenomena often change over time
- Change detection addresses questions about when and where changes occur, the nature of those changes, speed of changes, and the causes of change
Mapping Land use/cover Distribution (Page 41)
- Detailed representations of land use over time. Maps show Closed Canopy, Open Canopy, Shrubs, Mining Areas, and Settlements.
- Maps are labeled with the approximate dates of each land cover distribution map. (e.g.,
a) Land use/cover map of 1986)
Scale and Resolution (Pages 42 & 43)
- Map scale (ratio on map to real distance) varies, affecting detail (e.g., 1:50,000; 1:2,500,000)
- Resolution is associated with spatial cell width
GIS Applications (Page 44)
- Types of applications such as cartography, emergency management, environmental sciences, forest management, homeland security, medicine, real estate, social services, transportation, urban planning, and water resources.
Mapping Augur Data (Pages 45 & 46)
- Map showing samples of augur data with different grades.
- Data are plotted in a diagram with location/coordinate and values for each sample.
Mapping Land use/cover Distribution (Page 48)
- Detailed representations of land use over time are shown. Maps show Closed Canopy, Open Canopy, Shrubs, Mining Areas, and Settlements.
- Maps are labeled with the approximate dates of each land cover distribution map. (e.g.,
a) Land use/cover map of 1986)
Mapping Major Road & Towns (Page 49)
- Map showing major roads and towns in a particular region of interest
- Includes geographical location. Labeling helps show towns (e.g., Mampong, Aboadze, etc.)
Mapping Land Surface Temperature (Page 50)
- Map showing temperature distribution across a region.
- Temperature data is color-coded according to gradations.
Mapping Malaria Incidence (Page 51)
- Map showing malaria incidence density across a region
- Different areas/regions have corresponding density values displayed based on color.
Mapping Crop Yield (Page 52)
- Map showing crop yield distribution across a region labeled as block ID
- Different blocks/regions have corresponding yield estimates displayed based on color intensity.
Mapping Poverty Index in Ghana (Page 53)
- Map of Ghana showing Multidimensional Poverty Index (MPI)
- Color-coded map showing MPI values across regions in Ghana
Boundary Dispute Survey (Page 54)
- Map showing boundary markers and disputed areas
- Detailed geographical markers are shown in the map.
Spatial distribution of ASM Sites (Page 55)
- Map showing the spatial distribution of Small-scale Mining (ASM) sites.
Built-up Expansion In Different Zones Of One of The Mining (Page 56)
- Map demonstrating various zones showing built-up expansion with buffer zones of various distances.
Aerial View of a Community Showing Structures Before the RAP (Page 57)
- Aerial photo of a community, showing building structures.
Aerial View of the Resettled Community (Page 58)
- Aerial photo of a resettled community, showing building structures.
Geographic Data Processing Systems (Pages 60 & 61)
- Objectives of the lecture
- Data processing systems
- GIS software
- GIS functions
- Part 1 and 2 of GIS Cookbook
- Database management systems
Objectives and Aims (Page 62)
- Understand software architecture and functionality of a GIS
- Appreciate the stages of spatial data handling
- Be familiar with database management systems
Data Processing Systems (Page 63)
- Computer systems with hardware and software components that process, store, and transfer data.
- GIS processes of spatial data handling involve acquisition, storage/maintenance, analysis, and dissemination.
Geographic Information Systems (Page 64)
- GIS stores spatial data electronically using world coordinates
- Geoinformatics is the study of methods and techniques used to handle spatial information
- Incorporates concepts from different disciplines (cartography, statistics, computer science)
GIS Packages (Pages 65 & 66)
- Various GIS packages available on the market with different strengths and weaknesses, and emphasis on raster or vector data.
- Intergraph's GeoMedia
- ESRI's ArcGIS
- MapInfo from MapInfo Corp
GIS Packages (Page 67)
- Open-source GIS packages
- GRASS GIS
- gvSIG
- ILWIS
- JUMP/OpenJUMP
GIS Packages (Page 68)
- Free desktop GIS applications and programming components
- MapWindow GIS
- Quantum GIS (QGIS)
- SAGA GIS
- uDig
Characteristics GIS software package (Page 69)
- Analytical functions for extracting information from spatial and attribute data
- Data capture & preparation, data management, data manipulation & analysis, data presentation
GIS Software Architecture & Functionality (Page 70)
- Components of a GIS: Data Capture & Preparation, Storage and Maintenance, Manipulation and Analysis, Data Presentation
Stages of Spatial Data Handling (Page 71)
- Spatial Data Capture and Preparation
- Spatial Data Storage and Maintenance
- Spatial Querying and Analysis
- Integrated Analysis of Spatial and Attribute Data
- Spatial Data Presentation
Spatial Data Capture & Preparation (Page 72)
- Data acquisition methods related to surveying, photogrammetry, remote sensing and digitization.
Spatial Data Input Methods and Devices Used (Page 73)
- Methods and devices for spatial data input
- manual digitizing: keyboard, digitizing tablet
- automatic digitizing: scanner
- semi-automatic digitizing: line-following software
- available digital data: magnetic tape, CD-ROM, computer network
Spatial properties of the data (Page 74)
- Format transformation
- Geometric transformations
- Map projections
- Edge matching
- Graphic element editing
- Coordinate thinning
Spatial Data Storage and Maintenance (Page 75)
- Spatial data organized in thematic layers by theme and/or scale for efficient organization
- Data design reflects real-world phenomena as precisely as possible
Spatial Data Storage and Maintenance (Pages 76 to 77)
- Features in spatial databases represented with geometric and non-geometric attributes, relationships
- Geometry of features represented with primitives (e.g., points, polygons).
Spatial Querying and Analysis (Page 78)
- GIS combines databases, software, rules, and reasoning to produce spatial decision systems
- Data stored in layers (themes), usually with multiple themes in a project
Integrated Analysis of Spatial and Attribute Data (Page 79)
- Spatial data analysis involves extracting new insights from existing data
- GIS functions for this include classification, retrieval, measurement, overlay, neighborhood, and connectivity functions.
Classification, retrieval, and measurement functions (Page 80)
- Classification: assigning features to classes based on attribute values.
- Retrieval: finding specific data meeting conditions.
- Measurements: calculating distances, lengths and areas
Overlay functions (Pages 81 & 82)
- Combine spatial data layers to produce new data (e.g., intersecting features)
Neighbourhood functions (Page 83)
- Find features within a defined window (e.g., a buffer zone) and compute characteristics of the area around a feature
- Search: features within a window
- Buffer: zones around features
- Interpolation: predicts unknown values based on nearby known values.
- Topographic: characteristics of areas based on neighbourhood assessment
Connectivity functions (Page 84)
- Evaluate characteristics of spatial units that are connected. This can involve analysis of networks (roads, transportation routes) and visibility functions (visual area from a point)
Spatial Data Presentation (Page 85)
- Presenting spatial data in print, on screen, or as 'raw data' is related to cartography, printing, and publishing
- Hard copy devices (printer, plotter, film writer)
- Software copy devices (computer screen, magnetic tape, CD-ROM, Internet)
Database Management Systems (Page 86)
- Database (DBMS) is a software package used for setting up, using and maintaining a large collection of structured data (database structure).
Reasons for Using a DBMS (Pages 87, 88)
- Supports large datasets
- Guarantees data correctness
- Allows concurrent use by many users
- Controls data redundancy
- Provides a high-level, declarative query language
The Relational Data Model (Pages 89 to 91)
- A data model defines database structures, integrity constraints, and manipulation programs
- Attributes (fields)
- Tuples (records)
- Relations (tables)
Simple Example of Database (Page 92)
- Databases include tables for:
- Individuals (Private Person)
- Parcels (Parcel)
- Titles (Title Deed)
- Examples include tax identifiers (Taxld), parcel numbers (PId), and deed dates (DeedDate).
Relations, Tuples, and Attributes (Pages 93 & 94)
- Database represented by relations (tables) with tuples (records) that have attributes (fields).
- Tuples share the same structure/attributes
Relations, Tuples, and Attributes (Page 95)
- Attributes have a domain of atomic data types (integer, real, string, date)
Relations, Tuples, and Attributes (Page 96)
- Creating a relation involves specifying the relation's name, attributes, and attribute domains.
Finding tuples and building links (Page 97)
- DBMSs require mechanisms for efficient searching and retrieving data within large datasets. This is accomplished using keys
Keys (Pages 98 & 99)
- Primary Key: Unique identifier for each record/tuple in a table.
- Foreign Key: A field in one table that refers to the primary key in another table, creating a relationship between the tables
Table with a Foreign Key (Page 100)
- Graphical example depicting a table with a foreign key relationship between two tables (TitleDeed and Parcel)
Querying a Relational Database (Page 101)
- Tuple Selection: Extracts tuples meeting a given condition (filter) using a WHERE clause
- Attribute Projection: Extracts specific attributes from all tuples (formatter) using a SELECT clause.
- SQL is commonly used for relational database queries
Tuple selection (Page 102)
- Example using SQL to select tuples based on the condition AreaSize > 1000 (filter)
Attribute projection (Page 103)
- Example using SQL to select specific attributes from all tuples (formatter).
Join operator (Pages 104 to 106)
- Combines data from multiple tables using matching values (WHERE) based on a shared field to create a combined data set.
Raster and a Related Table (Page 107)
- Example of a raster data grid/table and related table showing values for each category.
Data Entry and Preparation (Page 109)
- Lecture Objectives
- Spatial Data Input
- Data Preparation
- Data Checks and Repairs
- Elements of Data Quality
- Validation of Data
Objectives and Aims (Page 111)
- How spatial data is acquired
- Digitising paper maps
- Understanding data checks and repairs
- Dealing with spatial data quality issues
Spatial Data Input (Pages 112 & 113)
- Direct spatial acquisition methods: field surveys, remote sensing
- Indirect spatial acquisition (digitizing paper maps): on-tablet, on-screen
- Other sources (National mapping agency or private companies) data is available (e.g., topographic base, elevation, natural resource, census). These have important influencing factors, including the nature of the data, the scale, and the date of production.
Data Preparation (Page 114)
- Preparing acquired spatial data for use(e.g., enhancing or correcting images; trimming, editing, deleting vector data like lines; converting to vector or raster format)
- Adjusting vector data to remove overshoots, duplicate lines, gaps in lines, generate polygons. Images may need to be enhanced or corrected.
Data Checks and Repairs (Page 115)
- Assessing data for completeness and consistency
- Clean-up data using standard sequences: split crossing lines, create nodes at intersections for polygon generation
Data Checks and Repairs (Page 116)
- Examples of data clean-up operations (erase duplicates, extend undershoots, erase short objects, break crossing objects, dissolve polygons)
Data Checks and Repairs (Page 117)
- Showing examples of
spaghetti dataversuscleanedandpolygonversustopologyaspects of spatial data
Determining & Mapping Position (Page 118)
- Data acquisition and preprocessing is expensive and time consuming.
- Project success depends on data quality
Data Quality (Page 119)
- Quality is the totality of characteristics of a product (in this case, spatial data) that meet the stated and implied needs for its use.
Elements of Data Quality (Page 120)
- Completeness: Assessing if needed data elements exist; this includes commission (over-completeness) and omission (incompleteness) errors
- Logical consistency: Determining if the data structure, relationships, and attribute values conform to logical rules
Elements of Data Quality (Page 121)
- Positional accuracy: Assessing the accuracy of coordinate values relative to other data, including absolute (compared to a standard) and relative (comparisons among data points/features) aspects.
- Temporal accuracy: Determining the accuracy of temporal attributes and relationships
- Attribute accuracy: Assessing accuracy of all other attributes besides positional and temporal attributes; these can be measured by ratio, interval, ordinal, or nominal scales
Validation of Data Quality (Page 122)
- Validating data accuracy is key for integration and sharing. This includes verifying physical representations (e.g., a road vs. a stream) and other non-graphic (e.g., tabular) data.
Spatial Data Analysis (Page 124)
- Course Title: Spatial Data Analysis
- Instructor: Prof Bernard Kumi-Boateng
- Institution: University of Mines and Technology, Tarkwa, Ghana
Objectives and Aims (Page 125)
- Understanding the classification of analytical GIS capabilities
Spatial Problem (Pages 126 to 126-b)
- Solving spatial problems (e.g., dam location, lake size estimation) involves numerous parameters and considerations (e.g., environmental, societal, and economic impacts).
Analytical Capabilities of GIS (Page 127)
- Measurement, retrieval, classification: exploring data without major changes, often at the start of data analysis
- Overlay functions: combines data layers to calculate new data in new layers
- Neighbourhood functions: evaluates characteristics around a feature, often computing distances or relationships.
Retrieval, Classification and Measurement on Vector data (Page 128)
- Measurements on points, lines, and polygons for vector data
- Location, length, distance, and area size
Retrieval, Classification and Measurement on Raster Data (Page 129)
- Measurements on raster data (simpler due to grid structure); area size of a cell determined by resolution; area of a selected raster part determined using cell number and area size.
Spatial Selection Queries (Pages 130 & 172)
- Querying procedures based on geometric/spatial conditions or attributes associated with spatial features
- Interactively selecting areas (e.g., using drawn shapes)
- Using queries to select features meeting specific conditions. This allows researchers to filter data to focus on relevant features rather than extensive datasets.
Interactive Spatial Selection (Pages 131 & 173)
- Using graphical selection tools (e.g., circles) to specify features in a GIS spatial display, based on interactive selections from data layers.
Interactive Spatial Selection (Page 174)
- Interactive selection of features using graphical/screen selection tools (e.g., a circle,) and selecting features in specific layers.
Spatial Selection by Attribute Conditions (Pages 133, 175 & 176)
- Specifying feature attributes using SQL-based queries; an example in this segment is area size determination using a query for features less than 400,000
Combining Attribute Conditions (Pages 136 to 182)
- Combining multiple criteria for selection by defining logical conditions (AND, OR, NOT) based on values or relationships with the attributes.
Spatial selection (topological relationships) (Pages 141 & 183)
- Selecting features based on containment relationships (e.g., polygons containing polygons, lines, or points.)
Classification (Pages 143, 185 & 186)
- Classification methods (user-controlled and automatic).
- Transforming data sets by classifying by attribute parameter(s) or using equal interval, equal frequency techniques.
- Applying reclassification tools (spatial merging, aggregation, or dissolving operations.)
Classification (Pages 145 & 187)
Methods for Determining Break Points
- Equal Interval: Determining class intervals based on the minimum, maximum values, and desired number of classes.
- Equal Frequency/Quantile: Dividing data into classes with roughly equal numbers of attributes. Using the total number of features to determine the number of features per category.
Overlay Functions (Pages 148 & 190, also 191 & 192)
- Vector Overlay Operators: Spatial join, polygon clipping, polygon overwrite.
- Raster Overlay Operators: Raster computations on a cell-by-cell basis; raster calculus
Neighbourhood Functions (Pages 153, 195 & 196)
- Determining neighbourhood: Defining target locations (e.g., a clinic, a road) and their spatial extent (e.g., 2 km radius).
- Finding features within a defined neighborhood.
- Calculating characteristics within neighborhoods (e.g., proximity, spread using buffer zones)
Proximity Computation (Pages 156 to 200)
- Proximity computations used for calculating neighbourhood aspects using geometric distances
- Important technique used is buffer zone generation for determining areas within specific distances of a location or feature.
- Distance-based methods (e.g., buffer zone) for identifying locations or features that are within a certain distance of a reference point (or multiple reference points).
Spread Computation (Pages 159 & 201 to 203)
- Determining the spread of a phenomenon and its effect by determining direction and/or differences in the terrain
- Source locations: locations of the phenomenon (e.g., pollution sources, water springs)
- Local resistance: factor influencing how the phenomenon spreads (e.g., terrain)
- The GIS evaluates spread using a raster dataset
Seek Computation (Page 162 & 204)
- Identifying least-cost paths and the directions for phenomena to spread by cost/path analysis.
- Example: Determining drainage patterns in a geographic area to assess how rainfall water flows.
Network Analysis (Pages 163 & 205)
- Network analysis using raster or vector data (e.g., identifying transportation patterns)
- Optimal path finding: Determining least-cost paths between locations on a network. This involves leveraging geometric and attributed data.
- Network partitioning: Categorizing network elements (nodes, line segments) using predefined rules.
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Test your knowledge of the key concepts and functions of Geographic Information Systems (GIS). This quiz covers topics such as spatial data handling, map scales, and the applications of GIS technology. Perfect for students and professionals looking to refresh their understanding of GIS principles.
