MN-GL 455 GIS Lecture Slides 2024 PDF

GEOGRAPHIC INFORMATION SYSTEMS GL/MN 455 Prof Bernard Kumi-Boateng Licensed Surveyor February 7, 2025 UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Presentation Outline  Semester’s Activities  Course Schedule  Assessment of Cours...

GEOGRAPHIC INFORMATION SYSTEMS GL/MN 455 Prof Bernard Kumi-Boateng Licensed Surveyor February 7, 2025 UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Presentation Outline  Semester’s Activities  Course Schedule  Assessment of Course  Course Syllabus  Source of Lecture Material  Objectives of Course  Overview of GIS(Day’s Lecture) UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Semester’s Activities  Lectures  Assignment  Quizzes Time! Time!! Time!!! Time!!!! Time!!!!! Time!!!!!! UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Course Schedule/Plan WEEKS DATE Number Month ACTIVITY Overview of GIS I 1 January MN/GL Tutorial on ArcGIS 1 & 2 Overview of GIS II 2 MN/GL Tutorial on ArcGIS 3 & 4 Data Processing Systems I 3 MN/GL Tutorial on ArcGIS 5 & 6 February Data Processing Systems II 4 MN/GL Tutorial on ArcGIS 7 & 8 Data Entry and Presentation I 5 MN/GL Tutorial on ArcGIS 7 & 8 Spatial Data Analysis I 6 MN/GL Tutorial on ArcGIS 10 Spatial Data Analysis II 7 MN/GL Data Visualisation I March Quiz 1 8 MN/GL Data Visualisation II Catch-up 9 MN/GL Data Visualisation III Catch-up 10 MN/GL Catch-up UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Course Schedule/Plan 11 MN/GL Catch-up 12 April MN/GL First Semester Examinations 13 MN/GL 14 MN/GL UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Assessment of Course Assessment of students The student’s assessment will be in two forms: Continuous Assessment [40%] and End of semester examination [60%] Assessment of Lecturer At the end of the course each student will be required to evaluate the course and the lecturer’s performance by answering a questionnaire specifically prepared to obtain the views and opinions of the student about the course and lecturer. This exercise is schedule to take place online. Please be sincere and frank. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Course Syllabus  General Introduction of GIS  Databases in GIS  Introduction to GIS Analytical Tools  Topological Relationship in GIS  Performing Analysis in a GIS Environment UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Source of Lecture Material  This material has been prepared using: ITC text book series on Principles of GIS UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Course Objectives  That the student will among other things:  Design and Use Maps;  Explain the elements of GIS Output;  Work with Spatial Data;  Be able to Digitize Maps ; UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Course Objectives  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. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Today’s Lecture OVERVIEW OF GIS UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Lecture Outline  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 UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Lecture Objectives  To define and state an overview of GIS Concepts  Explain the function of GIS  Describe Spatial Relationships  Be able to organise Spatial Data UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Gentle Introduction to GIS  Everything you experience from day to day happens somewhere in geographic space.  You can represent your experiences by using maps to:  find places, save time while traveling, decide where to locate a new waste site, plan cities, guide the development of wildlife preserves,  the power of a computer can help you make both better and faster decisions UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Example…. Geologist: A geological engineer might want to identify the best localities for constructing buildings in an area with regular earthquakes by looking at rock formation characteristics; Mining: A mining engineer could be interested in determining which prospective gold mines are best fit for future exploration, taking into account parameters such as extent, depth and quality of the ore body, amongst others; UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Geographic Space  All the above professionals work with data that relates to space,  typically involving positional data relative to the Earth’s Surface.  Positional data determines where things are, or perhaps, where they were or will be. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Geographic Space  Positional data of a non-geographic nature is not of interest in this course!  A car driver might want to know the position of the head light switch;  a surgeon must know how to find the appendix to be removed UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Fundamental Problem of GIS  We are facing ‘spatio-temporal’ problems: (a) a geographic dimension: our object of study has different characteristics for different locations (b) a temporal dimension: our object of study has different characteristics for different moments in time. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Stages of Working with Geographic Data  Three important stages of working with geographic data: Data preparation and entry: The early stage in which data about the study phenomenon is collected and prepared to be entered into the system. Data analysis: The middle stage in which collected data is carefully reviewed, and, for instance, attempts are made to discover patterns. Data presentation: The final stage in which the results of earlier analysis are presented in an UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  GIS Defined  A geographic information system (GIS) is a system designed to capture, store, manipulate, analyze, manage, and present all types of geographical data.  In the simplest terms, GIS is the merging of cartography, statistical analysis, and computer science technology.  A GIS can be thought of as a system—it digitally creates and "manipulates" spatial areas. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Purpose of GIS  Understanding of our environment  Land use planning, natural hazards, geology, hydrology, etc.  Geographical space  Position relative to Earth surface Changes  Natural, man-made, or a mix of both  Understanding geographical phenomena  Study, understand, forecast changes, devise action plan  Geographical and temporal dimension  spatio-temporal issues UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Spatial Data and Geo- information By data, we mean representations that can be operated upon by a computer. By spatial data, we mean data that contains positional values. Geo-spatial data as a further refinement, which then means spatial data that is geo-referenced. By information, we mean data that has been interpreted by a human being. Humans work with and act upon information, not data. Geo-information is a specific type of information that involves the interpretation of spatial data. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Geographic Phenomena  We define a geographic phenomenon as a manifestation of an entity or process of interest that:  can be named or described,  can be geo-referenced, and  can be assigned a time (interval) at which it is/was present. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Representation of Phenomena  A first observation is that the representation of a phenomenon in a GIS requires us to state:  what it is, and where it is.  A second fundamental observation is that some phenomena:  manifest themselves essentially everywhere in the study area,  while others only do so in certain localities. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Representation of Phenomena  A (geographic) field is a geographic phenomenon for which, for every point in the study area, a value can be determined.  Example: air temperature, barometric pressure and elevation.  These fields are in fact continuous in nature.  Examples of discrete fields are land use and soil classifications.. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Geographic Fields  Continuous Fields  E.g. elevation, temperature  Discrete fields  E.g. soil type, land cover type, lithology type UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Representation of Phenomena  Many other phenomena do not manifest themselves everywhere in the study area, but only in certain localities.  The array of buoys at the Tema/Takoradi port is a good example of (geographic) objects.  (Geographic) objects populate the study area, and are usually well distinguished, discrete, and bounded entities.  A general rule-of-thumb is that natural geographic phenomena are usually fields, and man-made phenomena are usually objects. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Geographic Objects  Geographic objects are characterized by: Location: Where is it? Shape: What form does it have? Size: How big is it? Orientation: In which direction is it facing? Collection of objects are often viewed as units Object boundaries crisp boundaries (manmade phenomena) fuzzy boundaries (natural phenomena) UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Geographic Phenomena (overview) UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Representation of Real World  When dealing with data and information we are usually trying to represent some part of the real world as it is,  as it was, or perhaps as we think it will be.  A computerized system can help to store such representations.  We restrict ourselves to ‘some part’ of the real world simply because it cannot be represented completely. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Representation of Real World  A representation of some part of the real world can be considered a model of that part.  The advantage of this is that we can ‘play around’ with the model and look at different scenarios, for instance, to answer ‘what if’ questions.  In the GIS environment, the most familiar model is that of a map.  A map is a miniature representation of some part of the real world. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Representation of Real World  Another important class of models are databases.  A database stores usually considerable amount of data, and provides various functions to operate on the stored data.  We are interested in databases that store spatial data. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Representation of Real World  The phenomena for which we want to store representations in a spatial database may have:  point, line, and area or image characteristics  An important choice in the design of a spatial database application is  whether some geographic phenomenon is better represented as a point, as a line, or as an area. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Computer Representations UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Raster Data and Vector Data  vector data model: A representation of the world using points, lines, and polygons. Vector models are useful for storing data that has discrete boundaries, such as country borders, land parcels, and streets.  raster data model: A representation of the world as a surface divided into a regular grid of cells. Raster models are useful for storing data that varies continuously, as in an aerial photograph, a satellite image, a surface of chemical concentrations, or an elevation surface. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Computer Representations UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Organisation of Spatial Data  The main principle of data organization applied in GIS systems is that of a spatial data layer.  A spatial data layer is either a representation of a continuous or discrete field, or a collection of objects of the same kind.  A data layer contains spatial data—of any of the types discussed and  attribute data (tabular form): describes the field or objects in the layer. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Organisation of Spatial Data UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  The Temporal Dimension  Besides having geometric, thematic and topological properties, geographic phenomena change over time;  we say that they have temporal characteristics.  For many applications, it is change over time that is quite often the most interesting aspect of the phenomenon to study.  This area of work is commonly known as change detection UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  The Temporal Dimension  Change detection addresses such questions as:  Where and when did change take place?  What kind of change occurred?  With what speed did change occur?  Can the cause of change be identified? UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Mapping Land use/cover Distribution UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Scale and Resolution  Map scale is the ratio between the distance on a paper map and the distance of the same stretch in the terrain.  A 1:50 000 scale map means that 1 cm on the map represents 50,000 cm, i.e. 500 m, in the terrain.  ‘Large-scale’ means that the ratio is large, so typically it means there is much detail, as in a 1:1 000 paper map.  ‘Small-scale’ in contrast means a small ratio, hence fewer detail, as in a 1:2,500,000 paper map. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Scale and Resolution  When applied to spatial data, the term resolution is commonly associated with the cell width of the tessellation applied.  Digital spatial data, as stored in a GIS, is essentially without scale:  scale is a ratio notion associated with visual output, like a map or on-screen display, not with the data that was used to produce the map. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT GIS Applications Cartography (map making) Emergency management Environmental sciences and security Forest and range management Homeland security Medicine and health care Real estate development and appraisal Social services Transportation Urban planning and development Water resources UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Mapping Augur Data UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Mapping Land use/cover Distribution UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Mapping Major Road & Towns UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Mapping Land Surface Temperature Aboadze Thermal Plant Naval Base Market Circle CBD Takoradi Harbour Takoradi Airport UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Mapping Malaria Incidence UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Mapping Crop Yield UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Mapping Poverty Index in Ghana UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Boundary Dispute Survey UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Spatial distribution of ASM Sites UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Built-up Expansion in Ten Different Zones of One of the Mining Company's Concession UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Aerial View of a Community showing Structures before the RAP UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Aerial View of the Resettled Community UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  THANK YOU Next Lecture UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT DATA PROCESSING SYSTEMS Prof Bernard Kumi- Boateng February 7, 2025 UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Presentation Outline  Objectives of Lecture  Data Processing Systems  GIS Software  GIS Functions  Part 1 and 2 of GIS Cookbook  Database Management Systems UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Objectives and Aims  Understand the Software Architecture and Functionality of a GIS;  Appreciate the Stages of Spatial Data Handling;  Be familiar with Database Management Systems UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Data Processing Systems  Data processing systems are computer systems with hardware and software components.  In combination, these components should be able to  process,  store and  transfer the data. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Geographic Information Systems  Working with spatial data usually involves processes of  data acquisition,  data storage and maintenance,  data analysis,  data dissemination. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Geographic Information Systems  With the help of a GIS, the spatial data can be stored in digital form in world coordinates.  The discipline that deals with all aspects of spatial data handling is called geo-informatics.  It is defined as the scientific field that attempts to integrate different disciplines studying the methods and techniques of handling spatial information. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  GIS Packages  All GIS packages available on the market have their strengths and weaknesses.  Some GISs focus more on support for raster manipulation, others more on (vector-based) spatial objects.  Well-known, full-fledged GIS packages in use are:  Intergraph’s GeoMedia,  ESRI’s ArcGIS, and  MapInfo from MapInfo Corp UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  GIS Packages Open-source desktop GIS application software:  GRASS GIS – Originally developed by the U.S Army Corps of Engineers: a complete GIS.  gvSIG – Written in Java. Runs on Linux, Unix, Mac OS X and Windows.  ILWIS (Integrated Land and Water Information System) – Integrates image, vector and thematic data.  JUMP GIS / OpenJUMP ((Open) Java Unified Mapping Platform) – The desktop GISs OpenJUMP, SkyJUMP, deeJUMP and Kosmo all emerged from JUMP. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  GIS Packages  MapWindow GIS – Free desktop application and programming component.  Quantum GIS (QGIS) – Runs on Linux, Unix, Mac OS X and Windows.  SAGA GIS (System for Automated Geoscientific Analysis) –- A hybrid GIS software. Has a unique Application Programming Interface(API) and a fast growing set of geoscientific methods, bundled in exchangeable Module Libraries.  uDig – API and source code (Java) available. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Characteristics GIS software package  The main characteristics of a GIS software package are its analytical functions that provide means for deriving new geoinformation from existing spatial and attribute data. The four sets of capabilities to handle geo-referenced data:  data capture and preparation,  data management (storage and maintenance),  data manipulation and analysis, and  data presentation. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT GIS Software Architecture & Functionality A geographic information system in the wider sense consists of software, data, people, and an organization in which it functions. In the narrow sense, we consider a GIS as a software system for which we discuss its architecture and functional components. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Stages of Spatial Data Handling  Spatial Data Capture and Preparation  Spatial Data Storage and Maintenance  Spatial Querying and Analysis  Integrated Analysis of Spatial and Attribute Data  Spatial Data Presentation UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Spatial Data Capture & Preparation  The functions for capturing data are closely related to the disciplines of:  surveying,  photogrammetry,  remote sensing, and  the processes of digitizing, i.e. the conversion of analogue data into digital representations. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Spatial Data Input Methods and Devices Used UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Spatial properties of the data  Format transformation functions convert between data formats of different systems or representations, e.g. reading a DXF file into a GIS.  Geometric transformations help to obtain data from an original hard copy source through digitizing the correct world geometry.  Map projections provide means to map geographic coordinates onto a flat surface (for map production), and vice versa.  Edge matching is the process of joining two or more map sheets, for instance, after they have separately been digitized.  Graphic element editing allows the change of digitized features so as to correct errors, and to prepare a clean data set for topology building.  Coordinate thinning is a process that is often applied to remove redundant or excess vertices from line representations, as obtained from digitizing. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Spatial Data Storage and Maintenance  In most of the available systems, spatial data is organized in layers by theme and/or scale.  For instance, the data may be organized in thematic categories, like land use, topography and administrative subdivisions, or according to map scale.  Representation of the real world has to be designed to reflect phenomena and their relationships as naturally as possible. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Spatial Data Storage and Maintenance  In a spatial database, features are represented with their (geometric and non-geometric) attributes and relationships.  The geometry of features is represented with primitives of the respective dimension: a processing plant probably as a point, an agricultural field as a polygon.  The primitives follow either the vector, or the raster approach UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Spatial Data Storage and Maintenance  GIS software packages provide support for both spatial and attribute data, i.e.  they accommodate spatial data storage using a vector approach, and  attribute data using tables [Database Management Systems (DBMSs)]. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Spatial Querying and Analysis  The combination of a database, GIS software, rules, and a reasoning mechanism leads to what is sometimes called a spatial decision support system (SDSS).  In a GIS, data are stored in layers (or themes). Usually, several themes are part of a project.  The analysis functions of a GIS use the spatial and non-spatial attributes of the data in a spatial database to provide answers to user questions. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Integrated Analysis of Spatial and Attribute Data  Analysis of spatial data can be defined as computing from the existing, stored spatial data new information that provides new insight.  It really depends on the application requirements. GIS Functions:  Classification, retrieval, and measurement functions  Overlay functions  Neighbourhood functions  Connectivity functions UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Classification, retrieval, and measurement functions  Classification allows assigning features to a class on the basis of attribute values or attributing ranges (definition of data patterns).  Retrieval functions allow the selective search of data. We might thus retrieve all locations in Ghana where clay deposits are found.  Measurement functions allow the calculation of distances, lengths, or areas. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Overlay functions  We can find intersections, unions, differences and complements of spatial areas.  the potato fields on clay soils:  select the ‘potato’ cover in the crop data layer and the ‘clay’ cover in the soil data layer and perform an intersection of the two areas found UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Overlay functions  the fields where potato or maize is the crop (select both areas of ‘potato’ and ‘maize’ cover in the crop data layer and take their union),  the potato fields not on clay soils (perform a difference operator of areas with ‘potato’ cover with the areas having clay soil),  the fields that do not have potato as crop (take the complement of the potato areas). UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Neighbourhood functions  Search functions allow the retrieval of features that fall within a given search window.  Buffer zone generation (or buffering)- It determines a spatial envelope (buffer) around (a) given feature(s).  Interpolation functions predict unknown values using the known values at nearby locations.  Topographic functions determine characteristics of an area by looking at the immediate neighbourhood. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Connectivity functions  Contiguity functions evaluate a characteristic of a set of connected spatial units.  Network analytic functions are used to compute over connected line features that make up a network. The network may consist of roads, public transport routes, high voltage lines or other forms of transportation infrastructure.  Visibility functions also fit in this list as they are used to compute the points visible from a given location (viewshed modelling or viewshed mapping) using a digital terrain model. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Spatial Data Presentation  The presentation of spatial data, whether in print or on- screen, in maps or in tabular displays, or as ‘raw data’, is closely related to the disciplines of cartography, printing and publishing. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Database Management Systems  A database is a large, computerized collection of structured data.  This format is usually called the database structure.  After its design, data is entered into the database.  It is important to keep the data up-to-date  A Database Management System (DBMS) is a software package that allows the user to set up, use and maintain a database. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Reasons for Using a DBMS  Supports the storage and manipulation of very large data sets.  Can be instructed to guard over data correctness.  Supports the concurrent use of the same data set by many users.  Allows the control of data redundancy UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Reasons for Using a DBMS  Provides a high-level, declarative query language.  A query is a computer program that extracts data from the database that meet the conditions indicated in the query.  A DBMS supports the use of a data model. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  The Relational Data Model  A data model is a language that allows the definition of:  structures that will be used to store the base data,  integrity constraints that the stored data has to obey at all moments in time, and  computer programs used to manipulate the data. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  The Relational Data Model  For the relational data model, the structures used to define the database are:  attributes,  tuples and  relations. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  The Relational Data Model  Computer programs… either:  perform data extraction (queries) from the database without altering it Or  they change the database contents (updates or transactions) UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Simple Example of Database Consists of three tables:  one for storing people’s details  one for storing parcel details and  a third one for storing details concerning title deeds. Various sources of information are kept in the database such as:  a taxation identifier (TaxId) for people,  a parcel identifier (PId) for parcels and  the date of a title deed (DeedDate). UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Relations, Tuples and Attributes In the relational data model,  a database is viewed as a collection of relations (tables)  A table or relation is a collection of tuples (or records).  Tuples have fixed number of named fields (attributes)  Tuples are similarly shaped  All tuples inOFthe UNIVERSITY MINES same relation AND TECHNOLOGY, TARKWA have the same UMaT  Relations, Tuples and Attributes  The PrivatePerson table has three tuples;  the Surname attribute value for the first tuple is ‘Garcia.’  An attribute is a named field of a tuple, with which each tuple associates a value, the tuple’s attribute value. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Relations, Tuples and Attributes  An attribute’s domain is a set of atomic values such as:  the set of integer number values,  the set of real number values etc. In our example  the domain of the Surname attribute is string, so any surname is represented as a sequence of text characters UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Relations, Tuples and Attributes  When a relation is created, we need to indicate what type of tuples it will store i.e.:  provide a name for the relation,  indicate which attributes it will have, and  set the domain of each attribute. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Finding tuples and building links  To find a specific tuple in a large table is impossible through a visual check.  The DBMS must support quick searches amongst many tuples.  This is why the relational data model uses the notion of key. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Keys  Keys are a way to associate tuples in different relations  Keys are one form of integrity constraint Enrolled Students sid cid grade 53666 Carnatic101 C sid name login age gpa 53666 Reggae203 B 53666 Jones jones@cs 18 3.4 53650 Topology112 A 53688 Smith smith@eecs 18 3.2 53666 History105 B 53650 Smith smith@math 19 3.8 FORIEGN Key PRIMARY Key  Keys  A primary key is a unique identifier for a database record.  When a table is created, one of the fields is typically assigned as the primary key.  Foreign key : Set of fields in one relation that is used to `refer’ to a tuple in another relation.  Must correspond to the primary key of the other relation. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Table with a Foreign Key UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Querying a Relational Database  Tuple selection works like a filter:  it allows tuples that meet the selection condition to pass, and disallows tuples that do not meet the condition.  Attribute projection works like a tuple formatter:  it passes through all tuples of the input, but reshapes each of them in the same way.  The most common way of defining queries in a relational database is through the SQL language. SQL stands for Structured Query Language. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Tuple selection UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Attribute projection UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Join operator  The join operator takes two input relations and produces one output relation, gluing two tuples,  one from each input relation, to form a bigger tuple, if they meet a specified condition. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Join operator UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Join operator Suppose we really wanted to obtain combined TitleDeed/Parcel information, but only for parcels with a size over 1000, and we only wanted to see the owner identifier and deed date of such title deeds. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Raster and a Related Table UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  THANK YOU Next Lecture UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT DATA ENTRY AND PREPARATION Prof Bernard Kumi- Boateng February 7, 2025 UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Presentation Outline  Objectives of the Lecture  Spatial Data Input  Data Preparation  Data Checks and Repairs  Elements of Data Quality  Validation of Data UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Objectives and Aims  Know how Spatial Data is Acquired  Digitise Paper Maps  Understand Data Checks and Repairs  Deal with Spatial Data Quality Issues UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Spatial Data Input Direct spatial data acquisition techniques  direct observation of the relevant geographic phenomena  Done through ground-based field surveys, or  Using remote sensors in satellites or airplanes. Indirect spatial acquisition (Digitizing Paper Maps)  Two forms of: on-tablet and on-screen.  In on-tablet digitizing, the original map is fitted on a special surface (the tablet).  In on-screen digitizing, a scanned image of the is shown on the computer screen. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Spatial Data Input Spatial Data Elsewhere (National mapping agency or Private Company) :  Topographic base data  elevation data  natural resource data  census data  Are available at important price factors:  The nature,  scale, and  date of production. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Data Preparation  Aims to make the acquired spatial data fit for use  Images may require enhancements and corrections  Vector data also may require editing, such as:  the trimming of overshoots of lines at intersections,  deleting duplicate lines,  closing gaps in lines, and  generating polygons.  Data may need to be converted to either:  vector format or  raster format. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Data Checks and Repairs  Acquired data sets must be checked for:  consistency and completeness.  Applies to geometric and topological quality  Clean-up operations are performed in a standard sequence. For example:  crossing lines are split before dangling lines are erased  nodes are created at intersections before polygons are generated UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Data Checks and Repairs UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Data Checks and Repairs UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Determining & Mapping Position  The first step of using a GIS is to provide it with data.  The acquisition and preprocessing of spatial data is:  an expensive and  time-consuming process.  The success of a GIS project depends on:  the quality of the data UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Data Quality  Quality is considered to be the totality of characteristics of a product that bear on its ability to satisfy a stated and implied need. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Elements of Data Quality Data quality elements as defined in ISO 19113:  Completeness – this is a measure of the presence and absence of features, their attributes and relationships.  Over-completeness are called errors of commission;  incompleteness are called errors of omission.  Crucial to the detection of these errors is to know what does and what does not belong to the complete set that the producer intended to include in the data set.  Logical consistency – this is the degree of adherence to logical rules of data structure, attribution and relationships. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Elements of Data Quality  Positional accuracy – this is the accuracy of coordinate values.  Relative positional accuracy is the accuracy relative to other data in the same test data set.  Absolute positional accuracy is the accuracy of test coordinate values relative to matching reference coordinate values on the same coordinate reference system.  Temporal Accuracy – is the accuracy of the temporal attributes and temporal relationships of features.  Attribute accuracy – this is the accuracy of all attributes other than the positional and temporal attributes of a spatial data set.  Attributes can be measured on four scales: ratio, interval, ordinal and nominal. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Validation of Data Quality  Validating the accuracy of spatial data is essential to data sharing and integration.  For example, is the road represented digitally truly a road or is it a stream?  Graphic and non-graphic (tabular) data need to be validated to ensure data quality in a spatial database. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  THANK YOU Next Lecture UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  SPATIAL DATA ANALYSIS Prof Bernard Kumi- Boateng UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Objectives and Aims Understand the Classification of Analytical GIS Capabilities UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Spatial Problem  The solution to a (spatial) problem always depends on a (large) number of parameters.  Typical problems may be in:  planning  e.g. what are the most suitable locations for a new dam?  prediction  e.g. what will be the size of the lake behind the dam?  The context may be the construction of a dam, and its environmental, societal, and economic impacts. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Analytical Capabilities of GIS  Measurement, retrieval, and classification functions allow the exploration of the data without making fundamental changes, and therefore they are often used at the beginning of data analysis.  Overlay functions This group forms the core computational activity of many GIS applications. Data layers are combined and new information is derived, usually by creating features in a new layer.  Neighbourhood functions Whereas overlays combine features at the same location, neighbourhood functions evaluate the characteristics of an area surrounding a feature’s location. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Retrieval, Classification and Measurement Measurements on vector data  The primitives of vector data sets are point, (poly) line and polygon.  Related geometric measurements are location, length, distance and area size.  A common use of area size measurements is when one wants to sum up the area sizes of all polygons belonging to some class.  This class could be grade type  What is the size of the area covered by grades > 2.5? UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Retrieval, Classification and Measurement Measurements on raster data  Measurements on raster data layers are simpler because of the regularity of the cells.  The area size of a cell is constant, and is determined by the cell resolution.  The area size of a selected part of the raster is calculated as the number of cells multiplied with the cell area size. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Spatial Selection Queries  When exploring a spatial data set, one usually want to select certain features, to restrict the exploration.  Such selections can be made on:  geometric/spatial grounds, or  attribute data associated with the spatial features. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Interactive spatial selection  In interactive spatial selection,  one defines the selection condition by pointing at or drawing spatial objects on the screen display,  after having indicated the spatial data layer(s) from which to select features.  The interactively defined objects are called the selection objects such as:  points,  lines, or  polygons. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Interactive spatial selection  the selection object is a circle  the selected objects are the red polygons;  they overlap with the selection object. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Spatial selection by attribute conditions  One can also select features by stating selection conditions on the features’ attributes.  These conditions are formulated in SQL  This type of selection answers questions like;  “where are the features with... ?” UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Spatial selection by attribute conditions  The query expression is Area < 400 000,  which is: “select all the land use areas of which the size is less than 400 000.” UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Spatial selection by attribute conditions  if we are interested in land use areas of size less than 400 000 that are of land use type 80,  the selected features of previous figure are subjected to a further condition, LandUse = 80. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Combining attribute conditions  When multiple criteria have to be used for selection,  we need to carefully express all of these in a single composite condition.  The tools for this come from a field of mathematical logic, known as propositional calculus.  we have seen simple, atomic conditions such as:  Area < 400000 and  LandUse = 80. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Combining attribute conditions  Atomic conditions use a predicate symbol such as:  < (less than)/> (greater than), or  = (equals).  Other possibilities are:  = (greater than or equal)  (does not equal).  Any of these symbols is combined with an expression on the left and one on the right. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Combining attribute conditions For instance,  LandUse 80 can be used to select all areas with a land use class different from 80.  arithmetic expressions like 0.15×Area will compute 15% of the area size.  Atomic conditions can be combined into composite conditions using logical connectives.  The most important ones are:  AND, OR, NOT and the bracket pair (· · ·) UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Combining attribute conditions  Area < 400000 AND LandUse = 80,  select areas for which both atomic conditions hold true.  Area < 400000 OR LandUse = 80  select areas for which either condition holds,  The NOT connective can be used to negate a condition.  NOT (LandUse = 80)  select all areas with a different land use class than 80. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Combining attribute conditions  Brackets can be applied to force grouping amongst atomic parts of a composite condition.  (Area < 30 000 AND LandUse = 70) OR (Area < 400 000 AND LandUse = 80)  select areas of class 70 less than 30 000 in size, as well as class 80 areas less than 400 000 in size. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Spatial selection (topological relationships) Selecting features that are inside selection objects  This type of query uses the containment relationship between spatial objects.  Polygons can contain polygons, lines or points, and  lines can contain lines or points,  but no other containment relationships are possible. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  we are interested in finding the location of medical clinics in the area of kakai District.  We first selected all areas of kakai District, using the technique of selection by attribute condition  District = “kakai”.  Then, these selected areas are used as selection objects to determine which medical clinics (as point objects) were within them. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Classification  Classification is a technique of purposefully removing detail from an input data set, in the hope of revealing important patterns (of spatial distribution).  The pattern that we look for may be the distribution of household income in a city.  we could define five different categories (or: classes):  ‘low’, ‘below average’, ‘average’, ‘above average’ and ‘high’, UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Classification  If these five categories are mapped in a sensible colour scheme, this may reveal interesting information.  The input data set may have itself been the result of a classification,  and in such a case we call it a reclassification.  post-processing function such as:  spatial merging,  aggregation or  dissolving.  Two kinds of classification: user-controlled and automatic. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Classification  In user-controlled classification, we indicate which attribute is, or which ones are, the classification parameter(s) and we define the classification method.  Automatic Classification, GIS software can also perform automatic classification, in which a user only specifies the number of classes in the output data set.  Two techniques of determining break points are in use.  Equal interval technique  Equal frequency technique UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Classification Equal interval technique  The minimum (vmin) and maximum values (vmax) of the classification parameter are determined and  the (constant) interval size for each category is calculated as: (vmax − vmin)/n  where n is the number of classes chosen by the user. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Classification Equal frequency technique  This technique is also known as quantile classification.  The objective is to create categories with roughly equal numbers of features per category.  The total number of features is determined  the number of features per category is calculated.  The class break points are then determined by counting UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Overlay Functions  The principle of spatial overlay is to compare the characteristics of the same location in both data layers,  and to produce a new characteristic for each location in the output data layer.  Standard overlay operators take two input data layers, and assume they are geo-referenced in the same system, and overlap in study area. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Overlay Functions  Vector Overlay Operators  spatial join  polygon clipping operator  polygon overwrite UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Overlay Functions UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Overlay Functions UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Raster Overlay Operators  Vector overlay operators are useful, but geometrically complicated, and  this sometimes results in poor operator performance.  Raster overlays do not suffer from this disadvantage, as most of them perform their computations cell by cell  Language to express operations on raster are called raster calculus UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Neighbourhood Functions To perform neighbourhood analysis, we must:  state which target locations are of interest to us, and define their spatial extent,  determine the neighbourhood for each target,  define which characteristic(s) must be computed for each neighbourhood. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Neighbourhood Functions  For instance, our target can be a medical clinic. Its neighbourhood can be defined as:  an area within 2 km travel distance, or  all roads within 500 m travel distance, or  all other clinics within 10 minutes travel time, or  all residential areas, for which the clinic is the closest clinic. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Neighbourhood Functions  Two types of Computations:  Proximity  Spread UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Proximity Computation  In proximity computations, we use geometric distance to define  the neighbourhood of one or more target locations.  The most common and useful technique is buffer zone generation UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Proximity Computation  The principle of buffer zone generation:  we select one or more target locations,  then determine the area around them,  within a certain distance. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Proximity Computation UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Spread Computation  The determination of neighbourhood of one or more target locations may depend on direction and differences in the terrain.  A typical case is when the target location contains a ‘source material’ that spreads over time.  This ‘source material’ may be: air, water or soil pollution, a water spring uphill, or the radio waves emitted from a radio relay station. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Spread Computation  In all these cases, one will not expect the spread to occur evenly in all directions.  There will be local terrain factors that influence the spread, making it easier or more difficult.  Spread computation involves:  source locations They are the locations of the source of whatever spreads.  Local resistance provides a value that indicates how difficult it is for the ‘source material’ to pass by that cell. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Spread Computation  From the source location(s) and the local resistance raster,  the GIS will be able to compute a new raster…  that indicates how much minimal total resistance…  the spread has witnessed for reaching a raster cell. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Seek Computation  There are cases where a phenomenon does not spread in all directions, but only along a chosen, least-cost path,  A typical case arises when we want to determine the drainage patterns in a catchment:  the rainfall water ‘chooses’ a way to leave the area.  This is when we use seek computations. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Network Analysis  A network is a connected set of lines, representing some geographic phenomenon, typically of the transportation type  Network analysis can be done using either raster or vector data layers  Classical spatial analysis functions on networks are supported by GIS software packages. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Network Analysis  Optimal path finding which generates a least cost-path on a network between a pair of predefined locations using both geometric and attribute data.  Network partitioning which assigns network elements (nodes or line segments) to different locations using predefined criteria. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  THANK YOU Next Lecture UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  SPATIAL DATA ANALYSIS Prof Bernard Kumi- Boateng UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Objectives and Aims Understand the Classification of Analytical GIS Capabilities UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Spatial Problem  The solution to a (spatial) problem always depends on a (large) number of parameters.  Typical problems may be in:  planning  e.g. what are the most suitable locations for a new dam?  prediction  e.g. what will be the size of the lake behind the dam?  The context may be the construction of a dam, and its environmental, societal, and economic impacts. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Analytical Capabilities of GIS  Measurement, retrieval, and classification functions allow the exploration of the data without making fundamental changes, and therefore they are often used at the beginning of data analysis.  Overlay functions This group forms the core computational activity of many GIS applications. Data layers are combined and new information is derived, usually by creating features in a new layer.  Neighbourhood functions Whereas overlays combine features at the same location, neighbourhood functions evaluate the characteristics of an area surrounding a feature’s location. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Retrieval, Classification and Measurement Measurements on vector data  The primitives of vector data sets are point, (poly) line and polygon.  Related geometric measurements are location, length, distance and area size.  A common use of area size measurements is when one wants to sum up the area sizes of all polygons belonging to some class.  This class could be grade type  What is the size of the area covered by grades > 2.5? UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Retrieval, Classification and Measurement Measurements on raster data  Measurements on raster data layers are simpler because of the regularity of the cells.  The area size of a cell is constant, and is determined by the cell resolution.  The area size of a selected part of the raster is calculated as the number of cells multiplied with the cell area size. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Spatial Selection Queries  When exploring a spatial data set, one usually want to select certain features, to restrict the exploration.  Such selections can be made on:  geometric/spatial grounds, or  attribute data associated with the spatial features. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Interactive spatial selection  In interactive spatial selection,  one defines the selection condition by pointing at or drawing spatial objects on the screen display,  after having indicated the spatial data layer(s) from which to select features.  The interactively defined objects are called the selection objects such as:  points,  lines, or  polygons. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Interactive spatial selection  the selection object is a circle  the selected objects are the red polygons;  they overlap with the selection object. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Spatial selection by attribute conditions  One can also select features by stating selection conditions on the features’ attributes.  These conditions are formulated in SQL  This type of selection answers questions like;  “where are the features with... ?” UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Spatial selection by attribute conditions  The query expression is Area < 400 000,  which is: “select all the land use areas of which the size is less than 400 000.” UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Spatial selection by attribute conditions  if we are interested in land use areas of size less than 400 000 that are of land use type 80,  the selected features of previous figure are subjected to a further condition, LandUse = 80. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Combining attribute conditions  When multiple criteria have to be used for selection,  we need to carefully express all of these in a single composite condition.  The tools for this come from a field of mathematical logic, known as propositional calculus.  we have seen simple, atomic conditions such as:  Area < 400000 and  LandUse = 80. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Combining attribute conditions  Atomic conditions use a predicate symbol such as:  < (less than)/> (greater than), or  = (equals).  Other possibilities are:  = (greater than or equal)  (does not equal).  Any of these symbols is combined with an expression on the left and one on the right. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Combining attribute conditions For instance,  LandUse 80 can be used to select all areas with a land use class different from 80.  arithmetic expressions like 0.15×Area will compute 15% of the area size.  Atomic conditions can be combined into composite conditions using logical connectives.  The most important ones are:  AND, OR, NOT and the bracket pair (· · ·) UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Combining attribute conditions  Area < 400000 AND LandUse = 80,  select areas for which both atomic conditions hold true.  Area < 400000 OR LandUse = 80  select areas for which either condition holds,  The NOT connective can be used to negate a condition.  NOT (LandUse = 80)  select all areas with a different land use class than 80. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Combining attribute conditions  Brackets can be applied to force grouping amongst atomic parts of a composite condition.  (Area < 30 000 AND LandUse = 70) OR (Area < 400 000 AND LandUse = 80)  select areas of class 70 less than 30 000 in size, as well as class 80 areas less than 400 000 in size. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Spatial selection (topological relationships) Selecting features that are inside selection objects  This type of query uses the containment relationship between spatial objects.  Polygons can contain polygons, lines or points, and  lines can contain lines or points,  but no other containment relationships are possible. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  we are interested in finding the location of medical clinics in the area of kakai District.  We first selected all areas of kakai District, using the technique of selection by attribute condition  District = “kakai”.  Then, these selected areas are used as selection objects to determine which medical clinics (as point objects) were within them. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Classification  Classification is a technique of purposefully removing detail from an input data set, in the hope of revealing important patterns (of spatial distribution).  The pattern that we look for may be the distribution of household income in a city.  we could define five different categories (or: classes):  ‘low’, ‘below average’, ‘average’, ‘above average’ and ‘high’, UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Classification  If these five categories are mapped in a sensible colour scheme, this may reveal interesting information.  The input data set may have itself been the result of a classification,  and in such a case we call it a reclassification.  post-processing function such as:  spatial merging,  aggregation or  dissolving.  Two kinds of classification: user-controlled and automatic. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Classification  In user-controlled classification, we indicate which attribute is, or which ones are, the classification parameter(s) and we define the classification method.  Automatic Classification, GIS software can also perform automatic classification, in which a user only specifies the number of classes in the output data set.  Two techniques of determining break points are in use.  Equal interval technique  Equal frequency technique UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Classification Equal interval technique  The minimum (vmin) and maximum values (vmax) of the classification parameter are determined and  the (constant) interval size for each category is calculated as: (vmax − vmin)/n  where n is the number of classes chosen by the user. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Classification Equal frequency technique  This technique is also known as quantile classification.  The objective is to create categories with roughly equal numbers of features per category.  The total number of features is determined  the number of features per category is calculated.  The class break points are then determined by counting UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Overlay Functions  The principle of spatial overlay is to compare the characteristics of the same location in both data layers,  and to produce a new characteristic for each location in the output data layer.  Standard overlay operators take two input data layers, and assume they are geo-referenced in the same system, and overlap in study area. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Overlay Functions  Vector Overlay Operators  spatial join  polygon clipping operator  polygon overwrite UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Overlay Functions UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Overlay Functions UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Raster Overlay Operators  Vector overlay operators are useful, but geometrically complicated, and  this sometimes results in poor operator performance.  Raster overlays do not suffer from this disadvantage, as most of them perform their computations cell by cell  Language to express operations on raster are called raster calculus UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Neighbourhood Functions To perform neighbourhood analysis, we must:  state which target locations are of interest to us, and define their spatial extent,  determine the neighbourhood for each target,  define which characteristic(s) must be computed for each neighbourhood. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Neighbourhood Functions  For instance, our target can be a medical clinic. Its neighbourhood can be defined as:  an area within 2 km travel distance, or  all roads within 500 m travel distance, or  all other clinics within 10 minutes travel time, or  all residential areas, for which the clinic is the closest clinic. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Neighbourhood Functions  Two types of Computations:  Proximity  Spread UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Proximity Computation  In proximity computations, we use geometric distance to define  the neighbourhood of one or more target locations.  The most common and useful technique is buffer zone generation UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Proximity Computation  The principle of buffer zone generation:  we select one or more target locations,  then determine the area around them,  within a certain distance. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT Proximity Computation UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Spread Computation  The determination of neighbourhood of one or more target locations may depend on direction and differences in the terrain.  A typical case is when the target location contains a ‘source material’ that spreads over time.  This ‘source material’ may be: air, water or soil pollution, a water spring uphill, or the radio waves emitted from a radio relay station. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Spread Computation  In all these cases, one will not expect the spread to occur evenly in all directions.  There will be local terrain factors that influence the spread, making it easier or more difficult.  Spread computation involves:  source locations They are the locations of the source of whatever spreads.  Local resistance provides a value that indicates how difficult it is for the ‘source material’ to pass by that cell. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Spread Computation  From the source location(s) and the local resistance raster,  the GIS will be able to compute a new raster…  that indicates how much minimal total resistance…  the spread has witnessed for reaching a raster cell. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Seek Computation  There are cases where a phenomenon does not spread in all directions, but only along a chosen, least-cost path,  A typical case arises when we want to determine the drainage patterns in a catchment:  the rainfall water ‘chooses’ a way to leave the area.  This is when we use seek computations. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Network Analysis  A network is a connected set of lines, representing some geographic phenomenon, typically of the transportation type  Network analysis can be done using either raster or vector data layers  Classical spatial analysis functions on networks are supported by GIS software packages. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  Network Analysis  Optimal path finding which generates a least cost-path on a network between a pair of predefined locations using both geometric and attribute data.  Network partitioning which assigns network elements (nodes or line segments) to different locations using predefined criteria. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT  THANK YOU Next Lecture UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT