GIS Syllabus & Introduction Faculty of Applied Engineering and Urban Planning Civil Engineering Department GIS 2nd Semester 2008/2009 Syllabus & Introduction Lecture - Week 1

Content Lecturer Information Course Outline Text Book and Recommended Readings Activities Assessment

MSc. Infrastructre Engineering Lecturer Information Eng. Maha A. Muhaisen MSc. Infrastructre Engineering 2004 Office: BK210 Tel.: Ext. 1127 O. Hours: Will be provided later

Course Outline Introduction Fundamentals of GIS Data models Hardware and software Sources, collection and integration of data Database / building geodata, metadata Georeferance and map projection GIS tools GIS analysis GIS applications Initiating GIS

Principle of Geographic information systems Text Book Principle of Geographic information systems Second Edition 2001 Rolf A. de By, Richard A. , Knippers Yuxian Sunm Martin C. Ellis Menno-Jan Kraak Michael J. C. Weir Yola Georgiadou Mostafa M. Radwan Cees J. vanWesten Wolfgang Kainz Edmund J. Sides

Activities Lectures Lab Theory and Principles Examples Group Work and Discussion Quizzes Lab application on ArcGIS Projects

Grade Policy Mid-term Exam 30% Final Exam 40% Assignments & Quizzes 10% Lab Work/ project 20% 100%

Definitions of GIS Broad definitions: “…geographic information systems are tools that allow for the processing of spatial data into information, generally information tied explicitly to, and used to make decisions about, some portion of the earth.” (DeMers, 1997: 7).

Definitions of GIS Broad definitions: “A geographic information system is a facility for preparing, presenting, and interpreting facts that pertain to the surface of the earth. ” (Tomlin, 1990: xi).

Definitions of GIS Perspective-oriented definitions (information technology): “A geographic information system (GIS) is an information system that is designed to work with data referenced by spatial or geographic coordinates. ...GIS is both a database system with specific capabilities for spatially-reference data, as well [as] a set of operations for working with data. ” (Star and Estes, 1990: 2-3).

Definitions of GIS Perspective-oriented definitions (management): “[GIS is] an institutional entity, reflecting an organizational structure that integrates technology with a database, expertise and continuing financial support over time.” (Carter, 1989: 3)

Definitions of GIS Perspective-oriented definitions (decision support): “[GIS is] a decision support system involving the integration of spatially referenced data in a problem solving environment.” (Cowen, 1988)

Definitions of GIS Three ‘role-based’ views on GIS Cartographic view Database view Spatial Analysis View

Definitions of GIS Geographic Information System “GIS” is a computer system for capturing, managing, integrating, manipulating, analyzing and displaying data which is spatially referenced to the Earth.

What is GIS? An integration of five basic components Procedures A What is a GIS? Definitions of a geographic information system can vary considerably. The definition provided here combines both the components and functions of a GIS. The components needed to perform GIS tasks include those listed below: People This is the most important component in a GIS. People must develop the procedures and define the tasks of the GIS. People can often overcome shortfalls in other components of the GIS, but the opposite is not true. The best software and computers in the world cannot compensate for incompetence. Data The availability and accuracy of data can affect the results of any query or analysis. Hardware Hardware capabilities affect processing speed, ease of use and the type of output available. Software This includes not only the actual GIS software, but also various database, drawing, statistical, imaging or other software. Procedures Analysis requires well defined, consistent methods to produce correct and reproducible results. Procedures A Introduction to ArcGIS for ArcView and ArcInfo (I)

What is GIS? Component-based : hardware software data people organizational/institutional context

What is GIS? Hardware computer monitor printer network infrastructure

Definitions of GIS Hardware computer monitor printer network infrastructure

Definitions of GIS Software the GIS application may be modular many component pieces for different purposes i.e. for output, types of analysis, editing, etc. different software for different purposes

Definitions of GIS Data spatial attribute metadata

Definitions of GIS People application authors administrators who maintain system expert users casual users consumers of GIS products management

Definitions of GIS Organizational/Institutional Context mission of organization organizational atmosphere encourage innovation entrenched bureaucracy resistant to change morale management hierarchy

Definitions of GIS Definition based on what a GIS does: the basic functions of a GIS (GIS as toolbox) Data input Data management (storage/retrieval) Data manipulation/analysis Data output

Definitions of GIS Data input getting the ‘real world’ into the database data already in digital format data in other formats data transformation

Definitions of GIS Data management (storage/retrieval) store data in a particular format data is organized in a way that facilitates fast and easy retrieval data can be created, deleted, and edited

Definitions of GIS Data manipulation/analysis transform data representations (i.e. interpolation) model geographic distributions determine spatial relationships extract information from data

Definitions of GIS Data output: communication Maps Tables Diagrams Animations Etc.

Definitions of GIS Does a GIS have to be computer based? is a (paper) map creation, storage, retrieval, and analysis organization (i.e. a cartographic analysis lab) a GIS?

Definitions of GIS Is a pre-computer-based library catalog system (card catalog) a GIS? Spatial or geographic information? Data input? Data management? Data analysis? Data output?

Definitions of GIS Works with geographic information Performs data input, management, manipulation/analysis, and output functions Composed of hardware, software, people, and institutional context

Definitions of GIS Many names for related systems and systems that overlap with GIS: spatial information system (SIS) geo-information system (GIS) spatial decision support system (SDSS) land information system (LIS) computer aided design (CAD) computer aided mapping (CAM) remote sensing (image processing) (RSS)

Definitions of GIS Primary related technologies computer cartography database management (information technology) computer graphics remote sensing computer aided design (CAD)

Origins of GIS 1950s in Geography quantitative revolution, spatial analysis, computer cartography 1960s: individual development efforts Harvard Lab for Computer Graphics (and 70s) early GISs: SYMAP, GRID, ODYSSEY Canada GIS Roger Tomlinson coined the term ‘GIS’

Origins of GIS 1970s: the federal government USGS automated mapping of quad maps, available GIS products US Census DIME (1970) and TIGER (1990) 1980s and 1990s: a GIS industry commercial vendors ESRI and Intergraph

Why use GIS? Until 1960s geographic information was represented primarily using paper maps convenient storage and retrieval suits a wide variety of purposes people are familiar with them

Some issues with paper maps Why use GIS? Some issues with paper maps difficult to update/edit many types of analysis are problematic/complex e.g. overlay, proximity analysis data integration is difficult large volumes of data

Why use GIS? GIS provides data integration and standardization methods for managing large volumes of data advanced spatial analysis capabilities automation of repetitive tasks sophisticated means of communication: distribution and output

Data Integration and Standardization Why use GIS? Data Integration and Standardization data of different types, origins, scales can be registered to a common georeferencing system and integrated for mapping or analysis problems: data of different resolutions and accuracies may be integrated introducing error

Managing large volumes of data Why use GIS? Managing large volumes of data databases are designed for the purpose of managing large amounts of data DBs have certain mechanisms that provide the means for speedy data retrieval and creation, deletion, and update operations disk space (memory) is cheap

Why use GIS? Spatial analysis automated basic and advanced spatial analysis functions some automate traditional map analyses that were previously cumbersome (e.g. overlay) other spatial analysis functions have only been made widely available through GIS (e.g. least cost path)

Automation of repetitive tasks Why use GIS? Automation of repetitive tasks can create scripts to batch process customize GIS interface for specific purposes mapmaking tabular reporting common data retrieval certain type of analysis

Distribution and output Why use GIS? Distribution and output not restricted to maps diagrams, reports, tables, animation, multi-media easy and cheap to make multiple copies easy to distribute data in digital format available global and local distribution networks: Internet, intranets, …etc

Why use GIS? Issues with GIS expensive requires expertise to maintain and learning curve specific systems for specific types of applications

Four basic categories: Why use GIS? Four basic categories: environmental/natural resource management urban and regional management infrastructure management commercial

environmental/natural resource management Applications of GIS environmental/natural resource management vegetation, habitat (ecologic) hydrology minerals agriculture: planning and prediction

Urban and regional management Applications of GIS Urban and regional management land use planning environmental impact emergency response (fire, police) crime analysis maintenance of legal records

infrastructure management Applications of GIS infrastructure management am/fm public works utilities transportation (relationship with CAD/CAM) commercial market area analysis site selection routing

GIS Functions Capture Store Query Analyze Display Output

Storing Data Vector Formats discrete representations of reality Raster Formats use square cells to model reality

Data Collection & Data Model GIS

Data Collection

Data in GIS – Acquisition Overview Real world  digital environment Data Input Digitizing Scanning Aerial Photographs Remote Sensing Surveying Global Positioning System

Data Input Major need in order to use a GIS  Data input costs often consume 60-90% of project costs data input is labor intensive, tedious, error prone reducing costs and maximizing accuracy important Automation appealing automated input can create future problems redrafting source documents (maps) often needed Input involves encoding locational information attribute data Locational data  Cartesian coordinate system source maps may have different projections, scales common coordinate system for multiple source integration Attribute data  tables, Digital data sharing

Sources of Data Digital Non-digital graphics Document files Existing vector/raster information Databases/spreadsheet tables Satellites Non-digital graphics Maps Photographs Sketches Diagrams Document files Scientific reports Survey measurements

Creating Digital Information Digitize existing maps Scan existing maps Digital photogrammetric map production Manual entry of computed coordinates from field measurements Transfer data from existing digital sources

Digitizing Maps Map-based information exists Vector representation Preparation Identify relevant features to digitize Organization Digitizing Internal coordinate systems Coordinate referencing (control points) Point and stream digitizing

Issues to Consider Paper maps Cartographic license Map sheets Humidity Errors Spills, blots, etc. Cartographic license Moving boundaries to include other map elements Map sheets Temporal changes Different creator Accuracy and precision Human error Experience Over and under shoots Cleaning process planar enforcement

Scanning Map-based information exists Automated digital data creation Background need  support other map information Automated digital data creation Raster representation Preparation Map needs to be of high cartographic quality Map needs to be clean Line width of at least 0.1 mm Scanning process Binary encoding process Regular grid produced

Processing Scan Information Scale and Resolution Issues Vectorization Convert raster data to vector format Errors Pattern recognition Labels Features

Digitizing vs. Scanning Advantages of digitizing Performed on inexpensive equipment Requires little training Does not require high quality map Disadvantages of digitizing Tedious Time consuming Advantages of scanning Easily performed Rapid creation of digital information Disadvantages of scanning Raster data files can be large and memory intensive Attribute data cannot be directly linked to line or polygon features Improving data quality is labor intensive Lines scaling not easy to deal with Clean maps with well defined lines necessary Equipment/software can be expensive Expert personnel required

Aerial Photographs Taken from above the surface of the earth Balloons, planes, helicopters Vertical angles Need for interpretation Roads, lakes, rivers, buildings, fields, forests Vegetation, soils and geological formations Shapes of objects Size, pattern, shadows, color, texture Resolution 0.025 mm + Distortion Need for projecting to a ground coordinate system Georeferencing

Remote Sensing Satellite measurements of solar energy on the surface of the earth (electromagnetic radiation) Passive optical (reflected sunlight and reemitted thermal radiation) Microwave (transmission and reflection of energy using microwave portion of the radio frequency spectrum) Landsat (US) Polar orbit and solar synchronous Altitude of 705 km (4-5,7) 16 day cycles SPOT (French) Polar orbit Altitude of 832 km 26 day cycle Two sensors (Panchromatic and Multispectral) Three-dimensional image capabilities

Surveying Field data collection Method to determine horizontal and vertical position of objects Reference marks needed Coordinates used to supplement and update Measurements: Distance Direction Positional accuracy 1-10 cm Typically two people needed Time consuming task Attribute information needs to be attached to locational measurements

Global Positioning System Satellite navigation system US Department of Defense Designed for military use Nominal operational constellation 24 satellites orbit the earth (12 hour circular paths) – 20,000 km often more than 24 operational satellites (new ones launched to replace older ones)

GPS Signals Atomic clocks (highly accurate radio signals) Coded satellite signals processed by a GPS receiver (compute position, velocity and time) Use of four satellites enables position and time on surface to be determined  (x,y,z) Six orbital planes equally spaced (60° apart), and inclined at about 55° with respect to the equatorial plane (5-8 satellites visible from any point on earth)

Differential GPS Error corrections GPS readings adjusted using Ground stations Another GPS receiver Accuracy ± 2 cm to ± 10 m

Understanding Spatial Data Structures Lecture 3a Understanding Spatial Data Structures

Perception, and Space How do we deal with representing constructions of spatial objects, like “mountain,” “river,” “street,” “city,” How about representing more conceptual constructions like “temperature,” “migration pattern,” “traditional homeland,” “habitat,” “geographic range,” etc? Answer: we have various data models which use different abstractions of reality

There are two general approaches for representing things in space: Entities and Fields There are two general approaches for representing things in space: Entities/ Objects: precise location and dimensions and discrete boundaries (remember, points are abstractions). Fields, or phenomena: a Cartesian coordinate system where values vary continuously and smoothly; these values exist everywhere but change over space

Two major data models Entity approach roughly corresponds with the vector model Field approach roughly corresponds with raster model Any geographic phenomenon can be represented with both, but one approach is usually better for a particular circumstance

Raster Spatial features modeled with grids, or pixels Cartesian grid whose cell size is constant Grids identified by row and column number Grid cells are usually square in shape Area of each cell defines the resolution Raster files store only one attribute, in the form of a “z” value, or grid code. Consider the contrary….

Vector Vector layers either represent: Points (no dimensions) Lines, or “arcs” (1 dimension) or Areas, or “polygons” (2 or 3 dimensions) Points are used to define lines and lines are used to scribe polygons Each point line or polygon is a “feature,” with its own record and its own attributes

Vector and Raster Models in GIS Representation of Lines Raster Vector

Raster and Vector representations of the same terrain Raster: great for surfaces Vector: limited with surfaces

Raster and Vector representations of the same land use

Raster and Vector representations of the same land use: closer in

Vector vs. Raster: bounding Raster: bad with bounding Vector: boundary precision

Vector vs. Raster: Sample points Cancer rates across space

Moving between vector and raster In Arc View and Arc GIS, we can covert vector layers to grids, based on an attribute, or grids to vector layers The disadvantage of vector to raster is that boundaries can be imprecise because of cell shape Each time you convert, you introduce more error too

WHEN TO USE RASTER OR VECTOR???

Raster data analysis is better for representing phenomena: where boundaries are not precise that occur everywhere within a frame and can be expressed as continuous numeric values where change is gradual across space where the attribute of a cell is a function of the attributes of surrounding cells

Raster technical advantages : Simple file structure Simple overlay operations Small, uniform unit of analysis Raster technical disadvantages : Big file size, especially for fine-grained data Difficult and error-prone reprojections Square pixels are unrealistic

Vector analysis is better : Where there are definable regions Where the relative position of objects is important Where precise boundary definition is needed Where multiple attributes are being analyzed for a given spatial object For modeling of routes and networks For modeling regions where multiple overlapping attributes are involved units with man-made boundaries (cities, zip codes, blocks), roads, rivers

Vector technical advantages : Smaller file size (in general) More graphically interpretable Allows for topology (see further on) Vector technical disadvantages : Complicated file structure Minimum mapping units are inconsistent between overlapping layers

Specific Vector Usages All legal and administrative boundaries (zip codes, states, property lines, land ownership) Building footprints and 3-D models Roads Bedrock geology Pipelines, power lines, sewer lines Flight paths and transportation routes Coastlines

Specific Raster Usages Terrain modeling where micro-locational variability is present and matters Groundwater modeling, where surface flow outside of channels is important Representation of slope and aspect Representations of distance and proximity to features Spatial representation of probabilities Modeling phenomena in nature with continuous spatial variability and numeric attributes, like soil moisture, depth to bedrock, percent canopy cover, vegetative greenness index, species richness index

In many cases, though, the choice between raster and vector may not be so clear. Often it depends on the application The following are some examples where you could go either way:

Terrain Vector-based models used for terrain, including contours and TIN (The Triangulated Irregular Network) Problem: creates distinct terrain entities that distort reality: terraces and triangular facets Raster based grids are more commonly used They are optimal for showing spatial micro-variation in elevation although still have the problem of being like miniature “steps” Lattices deal with this through interpolation

Soil Soil type: Vector Soil pH: raster Soil types are meant to represent discrete and homogeneous areas and are qualitative. There is no “slight gradation” between soil types like with pH Soil pH: raster pH is numeric, not categorical, and that number may vary slightly within a single soil type polygon If pH were turned into categories, like High, Medium and Low, vector might be better

Weather Weather station data: Vector, coded with points Average precipitation surface: Raster interpolation of points Average precipitation contours: vector lines Both are interpolations, but one may be more accurate in a given situation Downside of contours: terrace effect, fewer intervals, more categorical.

Rivers Most people think of a river as a discretely bounded entity, hence vector What about where the river size fluctuates seasonally, e.g. desert rivers? Or where the location of the river bed changes slowly and gradually over the years Or where the river becomes delta, and the distinction between “river” and “swamp” becomes fuzzy? Or where the river has a certain probability of flowing or being dry at any given location and time

Rivers Depends on the type of analysis being done With vector can do network modeling of stream and river system, but only in the arcs Vector stream model can take advantage of topologically enabled analysis tools With raster, can do surface flow modeling More realistic, because when it rains water flows everywhere, not just in channels, shows accumulation Think of every piece of land as mini stream channel

Metropolitan Areas No official administrative boundary for this Where does one metro area begin and another end? Look at the New York New Jersey area. For a precise bounding, say for administrative purposes, use vector Can also include “fuzzy boundaries” To represent a gradual change from one urban area to another, use raster

Vegetation Mapping Vector works well for modeling vegetation stand type where categories are broad, e.g. mixed conifer, deciduous hardwood Raster works better where there is micro-locational heterogeneity in species distribution The more specific and variable the classification, the more likely the raster approach will be needed

Introduction to the Architecture of ArcGIS

What is ArcGIS? ArcGIS Desktop is a software product from ESRI It comes in three versions: ArcView – basic desktop package, $ ArcEditor – ArcView +Advanced editing, $$ ArcInfo – ArcEditor + Comprehensive Geoprocessing, $$$ We are using ArcInfo

Components of Arc GIS All installation levels of Arc GIS come with ArcMap (now includes toolbox in Arc 9.0) ArcCatalog ArcInfo installation ArcInfo Workstation ArcReader ArcObjects Developer Tools ArcScene

Components of Arc GIS How do you access these? Go to the start menu>>programs>>ArcGIS

ArcCatalog for organizing and managing spatial and tabular data Main menu Standard Toolbar Catalog tree Catalog display

This button allows you to connect to a local folder or a network place In this course, all data and work will be stored on your Zoo account. In lab we’ll learn to “map a network” drive to your Zoo account, so it looks no different from saving to your local machine. This will be your Z drive.

Arc Catalog Once you’ve mapped your network drive, you can open an Arc Catalog connection to it, or to a subfolder in it

Arc Catalog For instance, here are the contents of my nr143 folder on my Zoo account, which I made a connection to:

Arc Catalog There are five types of data shown here Geodatabase Coverage Tabular data Arc Map Project file Shapefile

Data types Shapefile: native file format for the ArcView 3.x; Coverage: native file format for old ArcInfo 7.x Is not a file, but a complex directory structure Dbase/tabular data: non-spatial data table that can be used in a GIS, spreadsheet or database--.dbf Map document: a project file—it stores your map view and preferences in ArcGIS

Data types Geodatabase: This is ArcGIS’s new data model for storing geographic information It can contain any number of layers within it, known as “feature classes.” Shapefile have only one feature class As an example, here is a geodatabase with three layers, as seen in Arc Catalog

Feature classes In a geodatabase there are features classes for points, lines and areas (polygons) as well as for rasters (surfaces), annotation (labels) and dimensions Polygon, or area feature class Line, or arc feature class Annotation class: stores info about displaying labels on a map layer; important for roads and utilities

Feature dataset Feature dataset: a thematic grouping of feature classes Feature classes can be stand alone Feature data sets Feature classes

Geodatabase Geodatabases offer numerous advantages: Multiple spatial layers and non-spatial data sources can be stored in a single file and organized thematically Rules can be easily defined that can apply to all classes These rules can include relationships between layers For instance if you have one subclass with water lines and one with water valves, you can ensure that if you move a water line, the water valve that connects that line will move as well, or it ensures that, say, if the material attribute for a water line is set to copper, the water valves that connect to it will also be copper

Geodatabase Other advantages: Spatial layers maintain the same measurement and geographic reference systems, because new layers added to the geodatabase can “inherent” properties of existing ones Labeling “behavior” can be stored as an annotation feature class, making it easier to keep labels consistent “Domains” can be established, which specify the valid ranges of values for attributes, which reduces input error.

Geodatabase Types: Personal or multiple user We will use personal geodatabases. These are actually Microsoft Access files with the .MDB file extension. A multi-user (SDE) geodatabase is for organizations that wish to have many people accessing the same database. These are used with enterprise database systems, like Oracle, Informix or SQL Server

Personal Geodatabase With Access you can edit and query attribute tables and manage relationships between feature classes But it can only be viewed spatially in ArcGIS

Arc Catalog: data management Arc Catalog is where you can create and modify geodatabases. You can create new feature classes, tables or relationship classes within a geodatabase You can also import existing shapefiles or coverages into a geodatabase or export a feature class to a shapefile

Arc Catalog: data management Through right clicking, you can access the properties of a feature subclass and make changes to fields for the feature class’ attribute table

Arc Catalog: Online data Arc Catalog also allows you to access some data directly from the Internet It comes preloaded with a connection to the Geography Network, where many layers are available through the Arc Internet Map Server (IMS) service ; other Internet connections can be added

Arc Catalog: Online data Geography network data

Arc Catalog:Functionality Arc Catalog allows you to preview geographic data, including geodatabase feature classes Use these buttons to zoom and pan

Arc Catalog:Functionality You can also preview online data sets, like those from the geography network

Arc Catalog:Functionality …and you can preview and browse tabular attribute data

Arc Catalog: Functionality Preview, create and modify the “metadata,” or data about the data Arc Catalog standardizes this information and allows you to import and export metadata between layers

Arc Map Arc Map is where you’ll do most of your mapping and visualization of spatial data This is the default upper tool and menu bar Change map’s display scale (zoom factor) Zoom to full extent Find feature Zoom tools Add new layer measure Select feature

Arc Map: overlaying data Using the add data button, you can overlay any number of layers for which geographic reference information exists. Display Window TOC

Arc Map: overlaying data The window on the left side of Arc Map is the Table of Contents, where loaded layers are listed There are 2 modes for the TOC: display and source Display: note that it gives a symbol for the feature type Source mode: can see that the first 3 layers are from 1 geodatabase and the fourth is from a stand alone file TOC

Arc Map: properties You can access many functions by right clicking on the layer in the TOC. You can access, edit and query attribute tables from a special interface

Arc Map: Layer properties You can access layer properties by double clicking on that layer: much functionality is found there

Arc Map: Layouts Arc Map can view in two modes: data view: for viewing, analyzing and manipulating data layout view: for laying out data for presentation

Arc Map: Measurement Units Map and Display units. With reference info Map units are already set; Display units are still changeable Without reference info Define map units (This turns into guesswork)

Arc Map: Units Once you’ve set display units, future geographic measurement functions should be in those units, even if they’re different from the Map units; however, this does not apply to non-spatial attribute values If you use the ruler tool for instance, measurements will be in the selected Display units at the bottom of the page

Arc Map: Scale Translation factor between one unit on the map and same unit in the real world map units: ground units (ratio) 1:10,000:1 inch equals 10,000 inches in real world Scale is a dimensionless number

Large vs. Small Scale Large scale Lots of detail Shows small features No much detail Shows large features

Large vs. Small Scale USA 1:21,197,708 1:3,125,078 Washington State 1:21,197,708 1:3,125,078 Clark County, Washington highway interchange Clark County, Washington 1:30,137 1:5,269

ArcGIS: Extensions Specialized applications 3D Analyst Spatial Analyst Geostatistical Analyst Tracking Analyst, X Tools… Others…

ArcGIS: Extensions Activate by going to Tools>>extensions

Arc Map: toolbars When you load Arc Map it defaults to a state with a minimum of toolbars. You can add more toolbars representing more functionality, including extensions from the view menu.

Arc Toolbox Used to be a separate interface, now part of ArcMap

Arc Toolbox Advanced Analyses Extract Overlay Proximity Statistics

Arc Toolbox Data Conversions Change formats from one file type to another …

Arc Toolbox Data Management A collection of tools for data management…

Arc Scene Primarily for viewing 3D data

An Introduction to the Vector Data Model and Map Layout Techniques Lecture 3b An Introduction to the Vector Data Model and Map Layout Techniques

Reviewing Vector Data Types Three basic “feature” or “object” types Point Arc Polygon A layer holds a single feature type

Reviewing Vector Data Types Attribute table Attribute types Nominal attributes: descriptive information Ordinal attributes: rank order or scale Interval/ratio attributes: numeric items, order, magnitude of difference

Point Feature ID X,Y Coordinates A point layer: a collection of records with (x,y) coordinates 6 2 ID X,Y Coordinates 1 2,2 2 3,6 3 5,5 4 6,3 … 10 4,1 3,6 5 3 5,5 4 3 4 6,3 2 1 2,2 1 10 4,1 1 2 3 4 5 6 Image modified from ESRI Arc Info electronic help

Image modified from ESRI Arc Info electronic help Line (Arc) Feature Feature has length but not area Straight or curved Lines connect a set of vertices and nodes Feature is the ARC, not the line segments Arcs meet at nodes Vertices Line segment Node Node Image modified from ESRI Arc Info electronic help

Line (Arc) Feature Each point has a unique location

Polygon Feature Area of homogenous phenomena In a polygon layer, lines (arcs) define areas Closed region – first and last coordinate pairs are in the same location Line segments bound the polygon Lines (Arcs) Points

TOPOLOGY

TOPOLOGY (for vector data) What is topology? Why is important? Three types of topological models in GIS Spatial operations of topology Contiguity Connectivity Trade-offs of topological structure Application model Triangular Irregular Network (TIN):Vector-based GIS

Spatial features and spatial relationships Spatial features in maps Points, lines and polygons Human being interprets additional information from maps about the spatial relationships between features A route trace from an airport to a house Land contiguity adjacent to streets along which the lands are located

The definition of Topology The spatial relationships can be interpreted identification of connecting lines along a path definition of the areas enclosed within these lines identification of contiguous areas In digital maps, these relationships are depicted using ‘Topology’ Topology = A mathematical procedure for explicitly defining spatial relationship Topology is the description of how the spatial objects are related with spatial meaning

Topology Definition1: Explicit encoding of spatial relationships between objects: the spatial location of each point, line and polygon is defined in relation to each other Definition2: Topology is a collection of rules and relationships that enables the geodatabase to more accurately model geometric relationships found in the world.

Why Topology Two major purposes Allows for powerful analysis tools Quality control mechanism

Types of Vector Topology Arc-node topology Polygon topology Route topology Region topology

Arc-node & Node Topology Connectivity analysis Arc-node Topology Arc-node List Direction Image source: ESRI Arc Info electronic help

Image source: ESRI Arc Info electronic help Polygon-arc Topology Polygon-arc Topology Polygon-arc List The order does matter! Image source: ESRI Arc Info electronic help

Polygon-arc Topology Adjacency External polygon

Define paths based on series of arcs Route Topology Define paths based on series of arcs

Quality control and topology Ensuring “logical consistency” Define complex and nuanced rules governing spatial relationships of features Data quality Single layer quality control Mutli-Layer quality control

Rules for spatial relationships Say we have the following layers: parcels, sidewalks, right of way boundaries, building footprints, zoning Rules for spatial relationships Lots must be enclosed polygons Buildings must be entirely within a lot Sidewalks must be outside a parcel polygon and entirely within the public right of way Lots must fall entirely within a single zoning class All lots must have access to a right of way

Quality control and topology Single layer quality control undershoot Dangles polygon overshoot does not share a border

Quality control and topology Mutli-Layer quality control: Defining spatial rules between layers Polygon rules: e.g. Must Be Covered by Feature Class of Line rules: e.g. Must not Self Intersect Point rules: e.g. Must be Properly Inside Polygons ArcCatalog includes new tools for defining and validating topology rules

Spaghetti Data Model Non-topological data model Lecture 3b Spaghetti Data Model Non-topological data model Collections of line segments and points Only stores features coordinates No real connection, topology or relationships Not for spatial analysis Generally come from CAD files or digitizing Can “clean” these data, using user-defined tolerances

Map Layouts and Cartographic Representation Lecture 3b Map Layouts and Cartographic Representation

Lecture 3b Map Compilation X Map? Map Elements?

Map Compilation Title Legend Neatline Data frame North arrow Scale bar Lecture 3b Map Compilation Title Legend Neatline Data frame North arrow Scale bar Notes

Lecture 3b Layouts Create a map for layout in ArcMap Layout view View>>Layout view.

Map Compilation Legend Geographic features Other map elements Title Lecture 3b Map Compilation Geographic features Other map elements Legend Title North arrow Scale bar Author Neatline Source of data Other objects…

Lecture 3b Layouts Legends are edited in the Legends property window: Accessed by double clicking the legends. Give a title, e.g. land use Symbol editing

Legends editing: Items Lecture 3b Layouts Legends editing: Items Item Style Symbol

Legends editing: Frame Lecture 3b Layouts Legends editing: Frame Frame

Legends editing: Size and position Lecture 3b Layouts Legends editing: Size and position Size&Position

More than one frames can be shown in layout view ArcMap: data frame More than one frames can be shown in layout view Frame 1 Frame 2

Create a new view or “data frame” in ArcMap Layouts: data frame Create a new view or “data frame” in ArcMap

Access and edit data frame properties Layouts: data frame Access and edit data frame properties

Mxd files are project files Lecture 3b MXD Files Mxd files are project files Save your layout All other preferences Data is not included With an extension .mxd File >> Save (As)

Layer Files Save symbology and setting Lecture 3b Layer Files Save symbology and setting Primarily for saving legend setting Opening a layer file will open the data layer with all the preferences saved With an extension .lyr

Use layer files when you have lots of non-numeric categories Lecture 3b Layer Files Use layer files when you have lots of non-numeric categories

Create a layer file in ArcCatalog Lecture 3b Layer Files Create a layer file in ArcCatalog

Create a layer file in ArcMap Lecture 3b Layer Files Create a layer file in ArcMap

Import a layer file’s symbology in properties Lecture 3b Layer Files Import a layer file’s symbology in properties

Topological data models Three types of topological concepts Arc, Node and polygon topologies Arc Arcs have directions and left and right polygons (=contiguity) Node Nodes link arcs with start and end nodes (=connectivity) Polygon Arcs that connect to surround an area define a polygon (=area definition)

Terms and concepts Connectivity - from and to nodes Contiguity - Polygon Enclosure Adjacency - from Direction To Node Arc Right Polygon Left Polygon From Node

5. The Example of Topological Relationships

Spatial operations of topology Connectivity and contiguity A basic, but core spatial analysis operations in GIS Contiguity A city planner might be interested in zoning conflicts such as industrial zones bordering recreation areas Connectivity Transportation network, telecommunication systems, river systems To find optimum routings or most efficient delivery routes or the fastest travel route To estimate water flow at a bridge crossing that will result from heavy flood

Trade-offs of topology Advantages Spatial data is stored more efficiently Analysis process faster and efficient for large data sets By topological relationships, we can perform spatial analysis functions, Modelling flow through the connection of lines in a network (i.e. buffering) Combining adjacent polygons with similar characteristics (i.e. spatial merge) Overlaying geographical features (i.e. spatial overlay)

Disadvantages Extra cost and time Additional batch job working creating topological structure does impose a cost Topology should be always updated when a new map or existing map is updated Additional batch job working To avoid the extra efforts, GIS systems need to run a batch job (i.e. a process that can be run without user interactions); 70% of total GIS costs Autoexec.bat in DOS Macro languages such as AML (Arc/Info), Avenue (ArcView), MapBasic (MapInfo) and etc

Conclusions of topology When topology is created, we can identify Know its positions of spatial features Know what is around it Understand its geographical characteristics by virtue of recognising its surroundings Know how to get from A to B

Metadata Metadata is ‘data about data’. It should include such information as:- The origin of the data source When the dataset was created Who created / modified it Data coverage and scale Accuracy and precision Ownership, copyright and restrictions on use Dataset file location Critical to maintaining an effective GIS