REVIEW LECTURE By Austin Troy

REVIEW LECTURE By Austin Troy
------Using GIS-- Introduction to GIS REVIEW LECTURE By Austin Troy

Introduction to GIS I. What is GIS?

WHAT IS GIS? Some definitions:
Introduction to GIS WHAT IS GIS? Some definitions: The complete sequence of components for acquiring, processing, storing and managing data (Star and Estes, 1990) It is a configuration of computer hardware and software specifically designed for the acquisition, maintenance and use of cartographic data, (Tomlin, 1990) It is a set of computer tools for collecting, storing, retrieving at will, transforming and displaying spatial data from the real for a particular set of purposes, (Borrough et al. 1998) A system of hardware, software, data, people, organizations and institutional arrangements for collecting, storing, analyzing and disseminating information about areas of the earth. Source: adapted from UC Berkeley GIS Center

Limitations of GIS: Simple geometry does not describe nature
Introduction to GIS Limitations of GIS: Simple geometry does not describe nature Nature is impossible to quantify and measure accurately When we represent nature on paper or on a computer we abstract and simplify GIS is fundamentally an exercise in abstraction and approximation; It is not like physics or math It is complicated by scale It is complicated by time

Introduction to GIS II. Data Structures

Introduction to GIS Entities and Fields There are two general approaches for representing things in space: Entities: objects, with precise location and dimensions and discrete boundaries (remember, points are abstractions). Fields, or phenomena: a Cartesian coordinate system where values vary continuously and smoothly

Spatial data structures- Raster
Introduction to GIS Spatial data structures- 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….

Spatial data structures-Vector
Introduction to GIS Spatial data structures-Vector Each point has a unique location Points can create a line Points and lines can scribe a polygon, whose angle points are given by “nodes” Polygons are closed regions whose boundaries are made up of line segments connected at many angles. Polygons generally define an area of homogenous phenomena These phenomena can be described by one or more stored attributes

Spatial data structures-Vector
Introduction to GIS Spatial data structures-Vector Vector files in ARC INFO are topologically encoded. These are true vector files. that is, each point, line and node is defined in relation to every other point, line and node Polygon and line topology is encoded using arcs Arcs are vertices and nodes connected by lines Topology allows for powerful analysis tools

Importance of Topology
Introduction to GIS Importance of Topology The computer needs topology to be able to address many types of spatial problems With topology, data is stored very efficiently, allowing spatial data sets to be processed quickly Topology facilitates analyses, such as: modeling flow through networks combining adjacent polygons with similar traits determining distances between and among objects surface modeling

Introduction to GIS Spaghetti Data Model Just because feature looks like a point, line or polygon does not mean it’s topological Spaghetti Model is: Non-topological data model that looks like vector collections of line segments and points with no real connection or topology Only stores features coordinates; there are no relative relationships encoded in this model each feature has no knowledge of other features that it intersects, is adjacent to, contiguous with or near

TIN: Triangulated Irregular Network
------Using GIS-- Introduction to GIS TIN: Triangulated Irregular Network What is a TIN? Alternative model for representing terrain (from lecture 16) A TIN is a data structure that defines geographic space as a set of contiguous, non-overlapping triangles, which vary in size and angular proportion. Like grids, TINs are used to represent elevation surfaces, and can be created directly from files of elevation sample points, but with TINs these sample points are irregularly distributed.

Note the triangular facets defined by points
------Using GIS-- Introduction to GIS Three Dimensional data — TIN Note the triangular facets defined by points

TINs How are the sample points that define the triangles determined?
------Using GIS-- Introduction to GIS TINs TIN triangles vary in size with the complexity of the terrain; How are the sample points that define the triangles determined? Several algorithms exist for choosing significant sample points from a DEM to make a TIN: Very Important Points method (VIP) Maximum z tolerance method

------Using GIS-- Introduction to GIS TINs Maximim z tolerance: like VIP, an offset is determined, but this uses a specified maximum z-tolerance to decide which points are selected. The z-tolerance is selected through an iterative process. First it constructs a candidate TIN, and for each triangle in the TIN it calculates the elevation difference of the grid cells bounded by it. If the cell with the largest difference to triangle is greater than the z tolerance, that point is flagged for addition to the TIN model. This is what AV asks you for when you make a TIN

Introduction to GIS III. Database Models

Three Classic Database Models
Introduction to GIS Three Classic Database Models Hierarchical Network Relational -Arc View and Arc Info use this model

Introduction to GIS Data Types An individual data point, or datum (singular of data), can be of any number of types, including: number (must be continuous); below are subclasses: Currency (has specific decimal behaviors) Byte (0 to 255) Date Integer Long (integers, with a greater range) Single precision (with decimals) Double precision (like single, with a greater range) string (text: numbers can be represented as text, but they have no numerical properties) Boolean (yes, no) Object (holds data objects)

Hierarchical Database Model
Introduction to GIS Hierarchical Database Model A one-to-many method for storing data in a database that looks like a family tree with one root and a number of branches or subdivisions. Problem: linkages in the tables must be known before Groovy 70s TV Action shows Drama Sitcoms Three’s company Love Boat Dukes of Hazzard Dallas Fantasy Island CHIPs Gavin McLeod Larry Hagman John Ritter Tom Wopat Eric Estrada Larry Wilcox Ricardo Montalban Suzanne Somers

Networked Database Model
Introduction to GIS Networked Database Model A database design for storing information by linking all records that are related with a list of “pointers.” Problem: linkages in the tables must be known before. Not adaptable to change. Action shows Drama Sitcoms Three’s company Love Boat Dukes of Hazzard Dallas Fantasy Island CHIPs NBC CBS ABC

Network and Hierarchical Examples
Introduction to GIS Network and Hierarchical Examples Think about file storage in MS Windows—analogous to a hierarchical database Shortcuts turn this into the equivalent of a network database

Relational (Tabular) Database Model
Introduction to GIS Relational (Tabular) Database Model A design used in database systems in which relationships are created between one or more flat files or tables based on the idea that each pair of tables has a field in common, or “key”. In a relational database, the records are generally different in each table The advantages: each table can be prepared and maintained separately, tables can remain separate until a query requires connecting, or relating them, relationships can be one to one, one to many or many to one

Data Tables (flat files)
Introduction to GIS Data Tables (flat files) Records are the unit that the data are specific to Fields, or columns, are attribute categories Cells are where individual values of a record for a field are stored fields Name Phone Address Student ID *** Headings: are the labels for the columns records cells

Introduction to GIS Data key Is a field that is common to two or more flat files; allows a query to be done across multiple tables or allows two tables to be joined Flat file: professor info Flat file: course info Name Phone Address faculty ID *** Course name Course number enrollment faculty ID ***

Relational database: one to many relationship
Introduction to GIS Relational database: one to many relationship Parcel ID Street address zoning 11 15 Maple St. Residential-1 12 85 Brooks Ave Commercial-2 13 74 Windam Ct. Residential 4 One-to-many relationship Owner Parcel ID occupation J. Smith 13 lawyer R. Jones 11 dentist J. McCann 12 financier T. Flores Real estate developer In this case, several people co-own the same lot, so no longer one lot, one person

One-to-many relationship
Introduction to GIS Assuming each owner owned several parcels, we would structure the database differently One-to-many relationship Parcel ID Street address zoning 11 15 Maple St. Residential-1 12 85 Brooks Ave Commercial-2 13 74 Windam Ct. Residential 4 Properties owned by T. Flores Owner Parcel ID Date of transaction Flores 13 15 19 Owner occupation # properties owned J. Smith lawyer 2 R. Jones dentist 5 J. McCann financier T. Flores Real estate developer 3 Note: this table includes data pertinent only to Flores’ ownership of these properties

Introduction to GIS IV. Queries

Queries in Arc View Arc View queries only select (highlight) records
Introduction to GIS Queries in Arc View Arc View queries only select (highlight) records When a record is selected, so is its corresponding feature To summarize selected values, must use the “statistics” function or “summarize” button To create new values based on a query, must use the “calculate” tool.

So what can Arc View do with queries?
Introduction to GIS So what can Arc View do with queries? A query selects records; once selected you can: Look at the selection Requery the selection Do stats on the selection Create new fields that recategorize the selection by an an attribute field Create new fields by doing calculations across several fields Create a shapefile from the selection

Introduction to GIS Reclassing: say we wanted to prioritize inner city areas for urban redevelopment projects: Let’s query based on unemployment and home value Based on these we’ll make High, Medium and Low priority areas; everything else is not classified Tracts with median home value < $100,000 and un-employment > 12% are “High”

Introduction to GIS

Introduction to GIS Multi-layer queries Let’s say we want to select all the houses in our sample database that overlay fire hazard zones and then run some statistics

Introduction to GIS Now with “sample houses” active, we click select by theme and tell it to choose features that intersect the features of fire hazard zone

Those that overlay a hazard zone are selected
Introduction to GIS Those that overlay a hazard zone are selected

Now we can run statistics on the selection
Introduction to GIS Now we can run statistics on the selection This tells us that 2011 houses overlay fire zones Since we ran stats on the Price field, it also tells us that the mean price for these properties is $460,127!

Introduction to GIS Now, say we want to select features from layer A that are within a distance of features in layer B

Introduction to GIS V. Legend editing

Legend Editor Review: there are two ways to classify your legend:
Introduction to GIS Legend Editor Review: there are two ways to classify your legend: Graduated color: number data type Unique value: primarily for string data (categories) Class. method Class. field

Introduction to GIS Legend Editor Often categories must be aggregated and redefined: this land use map had over 110 categories that were condensed to 30

Introduction to GIS Legend Editor To do this I had to type in what code ranges corresponded with my land use designations (e.g. Single family residential, commercial) and assign each a color. I decided on the classes The colors had to be chosen strategically so that important classes stood out and so that similar classes (e.g. SF and MF residential)were in the same color family

Legend Editor With graduated color (numeric) legends, we must decide:
Introduction to GIS Legend Editor With graduated color (numeric) legends, we must decide: How many classes By what method will the data be broken up Natural breaks, quantile, equal area, standard deviation The default is natural breaks

Introduction to GIS Legend Editor Here we have houses graduated by sales price. We have accepted arc view’s default of 5 classes and natural breaks.

Introduction to GIS Legend Editor Notice how it changes when we classify by equal interval—most are in the lowest category now because outliers make the intervals meaningless

Introduction to GIS Legend Editor Another way to display point data is by the graduated symbol method—the larger the value, the larger the symbol

VI. Vector geoprocessing
Introduction to GIS VI. Vector geoprocessing

Using the Geoprocessing Wizard
Introduction to GIS Using the Geoprocessing Wizard The GP wizard allows you to dissolve, clip, merge, intersect, union and spatial join

Introduction to GIS Spatial Join Assigns data from one location in one layer to same location in another Can assign polygon data to a point that overlays Can assign point to point and point to line distances between two layers Simple adds attributes to the DBF table

Buffering: A GIS Classic
Introduction to GIS Buffering: A GIS Classic Arc View 3.2 allows for buffer operations Allows for variable width based on attribute Allow multiple buffers

VII. Vector representation
Introduction to GIS VII. Vector representation

Vector Representation:Points
Introduction to GIS Vector Representation:Points Simple points Nodes: topological junction between line features Vertices: define “kinks” All zero dimensional: points don’t exist in reality

Vector Representation:points
Introduction to GIS Vector Representation:points Arcview allows the user to represent points as symbols of numerous types and at various sizes. Remember “graduated symbol” from last week: where point symbol size varies with an attribute. Arcview comes with a bunch of symbol pallettes that can be loaded for specific sectors and industries

Vector Representation:lines
Introduction to GIS Vector Representation:lines String: a sequence of line segments Chain: a directed sequence of non-intersecting line segments with nodes at each end Arc: some consider any chain or string as an arc; others consider it a chain or string in which segments are smooth curves defined by mathematical formulae

Vector Representation:lines
Introduction to GIS Vector Representation:lines Ring: this is a series of line segments (a string) that close upon each other It is NOT a polygon!!

Vector Representation:area
Introduction to GIS Vector Representation:area Simple Polygon: an area defined by an outer ring that may have no inside features or holes Complex polygons: like a simple polygon with internal features

Vector representation:Scale
Introduction to GIS Vector representation:Scale Scale is the ratio of the map distance to the ground distance Hence, 1:200,000 means 1 cm on the map = 200,000 cm in the real world The smaller the ratio, the LARGER the scale and the smaller the area depicted That area is known as the map extent.

Vector representation:Scale
Introduction to GIS Vector representation:Scale Scale and Vector representation are closely tied up On a small scale map (e.g. 1:2,000,000) a city is represented as point, without dimension, while on a large scale map (1:24,000), a city would likely be represented as an area with dimensions Think of other examples: rivers, roads, buildings: these The smaller the scale, the more we abstract, and the more we use points and lines

Source: UC Berkeley GIS CENTER
Introduction to GIS What is Geocoding? Geocoding is the mechanism that allows you to use addresses to identify locations on a map. Addresses are the most common form of storing geographic data. With geocoding can display the tabular data containing addresses as points on a map. An address specifies a location in the same way that a geographic coordinate does. But since an address is merely a text string containing the information of house number, street name, direction, and/or zip codes, an address needs a mechanism to calculate the geographic coordinate for the address and then display the location on a map based on the assigned coordinate. Source: UC Berkeley GIS CENTER

Geocoding with TIGER: Address segments
Introduction to GIS Geocoding with TIGER: Address segments Match R-F-ADDR R-T-ADDR 1000 Main St 1100 1001 1101 L-F-ADDR L-T-ADDR Address Style 1060 Main St Source: UC Berkeley GIS CENTER

The Problems with Address Geocoding
Introduction to GIS The Problems with Address Geocoding Interpolation opens geocoding to error An urban road segment: smaller, more precise A rural area with a long road segment: very imprecise

VIII. Advanced Raster Analysis
Introduction to GIS VIII. Advanced Raster Analysis

Neighborhood Statistics
Introduction to GIS Neighborhood Statistics In Arcview, neighborhood statistics command allows you to specify statistic: Min, max, mean, standard deviation, range, sum, variety

Neighborhood Statistics
Introduction to GIS Neighborhood Statistics Neighborhood statistics creates a new grid layer with the neighborhood values This can be used to: Simplify or “filter down” the features represented Emphasize areas of sudden change in values Look at rates of change Look at these at different spatial scales

Introduction to GIS Neighborhood Filters Generating neighborhood means is similar to RS technique called low pass filtering: Low pass filtering: takes “tonally rough” surfaces, with abrupt changes in cell values, and makes those values vary more smoothly. The opposite is called a high-pass filter. High pass filtering: emphasizes detailed, abrupt changes in cell values, deemphasizes areas of gradual change.

Introduction to GIS Tabulate Areas This is a command for analysis between two vector layers, but it does so by temporarily rasterizing these layers, overlaying and then determining areas that are coincident, in the measurement units specified for the view It summarizes the area for each feature in the row theme by each category that exists in the column theme So, if I have three categories for the attribute field that I’m tabulating by, it results in three new attributes, which give the area of features in layer 1 that overlay features in layer 2 with that category value

Raster Data Structuring
Introduction to GIS Raster Data Structuring Run-length encoding (RLE) Chain Code Block Code Quad Tree

Introduction to GIS IX. Public Data

Introduction to GIS DLG Summary

DLG Layer Availability in CA-1:24,000
Introduction to GIS DLG Layer Availability in CA-1:24,000 Listings available at the same web site Browse by state and by layer

Introduction to GIS DEM Summary

Introduction to GIS Other USGS Data National Elevation Data (NED): seamless, fewer terrain artifacts National Land Cover Data DOQs-digital ortho quarter quadrangle DRG-digital raster graphic GNIS- Geographic Names information system

GNIS Includes location, names and category of features such as:
Introduction to GIS GNIS Includes location, names and category of features such as: Schools/universities Churches/cemeteries Airports/ports Parks/recreation centers Shopping centers Stadiums/arenas Theaters/auditoriums/cultural facilities Country clubs/golf courses Marinas/yacht clubs Trailheads (some) Rural fire stations (some) Dams/reservoirs Cities/incorporated areas (as points)

US Census TIGER line files Line Features
Introduction to GIS US Census TIGER line files Line Features 1.Roads 2.Railroads 3.Hydrography 4.Transportation and Utility Lines Boundary Features 1.Statistical boundaries, such as census tracts and blocks 2.Local government boundaries, such as places and counties 3.Administrative boundaries, such as congressional and school districts Landmark Features 1.Point landmarks, such as schools and churches 2.Area landmarks, such as parks and cemeteries 3.Key geographic locations, such as apartment buildings and factories Can be integrated with Census data

Transfer Formats: SDTS
Introduction to GIS Transfer Formats: SDTS Spatial Data Transfer Standard Newer Standard for USGS data Large scale DLGs only available in this format The Federal Geographic Data Committee has mandated that all federal digital geographic data go to this standard The Standard allows the exchange of digital spatial data between different computer systems. It provides a solution to the problem of spatial data transfer from the conceptual level to the details of physical file encoding. Several software tools have been developed for the importing SDTS data, but each data product requires a different software tool

Introduction to GIS X. Remote Sensing

Introduction to GIS The Physics of RS An RS sensor can detect spectral responses from objects in various wavelength ranges. Each class of objects has a different spectral responses across wavelength Spectral reflectance values of an object can be plotted on a graph as a function of wavelength, known as a spectral reflectance curve.

Introduction to GIS The Physics of RS Each object feature class on the earth has a spectral reflectance curve that helps us to identify it remotely. This is why we can use RS to tell the difference between types of objects A spectral response pattern delivers much more information than a single pixel value

Introduction to GIS The Physics of RS A Spectral reflectance curve for two classes of similar object: conifers and deciduous trees Note how visible band is similar, but near IR band is very different: means eye could not pick this up The shape of an objects curves will determine what bands we use to ID it Source :Lillesand and Kiefer 2000

The Physics of RS Spatial resolution—size of pixel
Introduction to GIS The Physics of RS Spatial resolution—size of pixel Radiometric resolution—number of possible z values; sensors with high radiometric resolution can perceive subtler gradations in brightness

Introduction to GIS Panchromatic imaging With panchromatic imaging, the sensor is a single channel detector sensitive to radiation in a broad wavelength range. Where the wavelength range coincides with the visible range, the resulting image resembles a "black-and-white" photograph. The physical quantity being measured is the apparent brightness of the targets. The spectral information or "colour" of the targets is lost.

Multispectral Imaging
Introduction to GIS Multispectral Imaging With multispectral, or multiband data, there are several layers, or values for each pixel, each representing a different “channel” or reflectance in a wavelength spectrum Each “band” or “channel” is sensitive to radiation within a different band of wavelength, through use of different filters These bands can be combined to make “composite” images, can be looked at separately, or can be analyzed using overlay analysis methods

Multispectral Imaging
Introduction to GIS Multispectral Imaging True color composite: where bands are assigned to color channels in such a way that colors in the image roughly correspond with the colors in the real world. Often assigned red to red, green to green and blue to blue can result in this Another is a false color composite, which shows colors that don’t really exist in that location. An example is color infrared composite, where green band is assigned to blue display channel, red is assigned to green and Near IR is assigned to red

RS Sources LANDSAT TM LANDSAT MSS SPOT IKONOS
Introduction to GIS RS Sources LANDSAT TM LANDSAT MSS SPOT IKONOS Scanner Types: pushbroom vs. whiskbroom Orbit: sun-synchronous

Introduction to GIS Preprocessing Geometric correction: corrects distortion due to parallax, altitude, velocity, curvature of earth, atmosphere, terrain, etc These consist of systematic and random sources of distortion Radiometric correction: distortion in radiance measure due to atmosphere, angle of sun, angle of view, etc. E.g. haze compensation “Noise correction”- removal of random “noise” in signal-e.g. destripping

Image enhancement Three types of manipulation are:
Introduction to GIS Image enhancement Three types of manipulation are: Contrast manipulation: methods include gray level thresholding, level slicing and contrast stretching Spatial feature manipulation: methods include spatial filtering, edge enhancement and Fourrier analysis Multi-image manipulation: methods include multispectral band ratioing and differencing, principal components, canonical components, vegetative components, decorrelation stretching, others

Introduction to GIS Contrast enhancement Most images start with low contrast; these improve it Level slicing reclasses DNs into fewer classes, so differences can be more easily seen; colors or grayscale values can be assigned. Like resampling down radiometric resolution. Often used where histogram shows bimodal distribution of reflectance values Contrast Streching is the opposite, where a smaller number of values are stretched out over full DN range

Introduction to GIS Contrast enhancement Gray level thresholing: all pixel values below a lower threshold are mapped to zero and those above an upper threshold are mapped to 255. All other pixel values are linearly interpolated to lie between 0 and 255 Grey-Level Transformation Table for performing linear grey level stretching of the three bands of the image. Red line: XS3 band; Green line: XS2 band; Blue line: XS1 band. Source:

Spatial Feature Enhancement
Introduction to GIS Spatial Feature Enhancement Spatial filtering: neighborhood operations (like we reviewed for raster analysis), that they calculate a new value for the center pixel based on the values of its neighbors within a window (see lecture 9 for more); includes low-pass (emphasizes regional spatial trends, demphasizes local variability ) and high-pass (emphasizes local spatial variability) filters Convolution: a filtering operation where where pixels within the window, or “kernel” are weighted, so each pixel coefficient is multiplied by DN and they are all summed and assigned to the pixel at the middl

Spatial Feature Enhancement
Introduction to GIS Spatial Feature Enhancement Edge Enhancement: This is a convolution method that combines elements of both low and high-pass filtering in a way that accentuates linear and local contrast features without losing the regional patterns First, a high-pass image is made with local detail Next, all or some of the gray level of the original scene is added back Finally, the composite image is contrast stretched

Spectral Classification
Introduction to GIS Spectral Classification Two types of classification: Supervised: the analyst designates on-screen “training areas” known land cover type from which an interpretation key is created, describing the spectral attributes of each cover class . Statistical techniques are then used to assign pixel data to a cover class, based on what class its spectral pattern resembles. Unsupervised:automated algorithms produce spectral classes based on natural groupings of multi-band reflectance values (rather than through designation of training areas), and the analyst uses references data, such as field measurements, DOQs or GIS data layers to assign areas to the given classes

Introduction to GIS Spectral Classification Unsupervised: Computer groups all pixels according to their spectral relationships and looks for natural spectral groupings of pixels, called spectral classes Assumes that data in different cover class will not belong to same grouping Once created, the analyst assesses their utility Spectral class 1 Spectral class 2 Source: F.F. Sabins, Jr., 1987, Remote Sensing: Principles and Interpretation.

Introduction to GIS Spectral Classification Unsupervised: After comparing the reclassified image (based on spectral classes) to ground reference data, the analyst can determine which land cover type the spectral class corresponds to Has advantage over supervised classification: the “classifier” identifies the distinct spectral classes, many of which would not have been apparent in supervised classification and, if there were many classes, would have been difficult to train all of them. Not required to make assumptions of what all the cover classes are before classification.

Introduction to GIS Spectral Classification Supervised: Better for cases where validity of classification depends on a priori knowledge of the technician Conventional cover classes are recognized in the scene from prior knowledge or other GIS/ imagery layers Therefore selection of classes is pre-determined and supervised Training sites are chosen for each of those classes Each training site “class” results in a cloud of points in n dimensional “measurement space,” representing variability of different pixels spectral signatures in that class

Introduction to GIS Spectral Classification Supervised: The next step is for the computer to assign each pixel to the spectral class is appears to belong to, based on the DN’s of its constituent bands There are numerous algorithms the computer uses

Introduction to GIS Spectral Classification Supervised: These algorithms look at “clouds” of pixels in spectral “measurement space” from training areas, and try to determine which “cloud” a given non-training pixel falls in. The simplest method is “minimum distance” in which a theoretical center point of point cloud is plotted, based on mean values, and an unknown point is assigned to the nearest of these. That point is then assigned that cover class. They get much more complex from there.

Land Cover/ Land Use Mapping
Introduction to GIS Land Cover/ Land Use Mapping Anderson land use and land cover classification system for use with remote sensor data (levels 3 and 4 not shown, because vary locally) Level I Level II 1 Urban or Built-up Land 11 Residential 12 Commercial and Services 13 Industrial 14 Transportation, Communications, and Utilities 15 Industrial and Commercial Complexes 16 Mixed Urban or Built-up Land 17 Other Urban or Built-up Land

Land Cover/ Land Use Mapping
Introduction to GIS Land Cover/ Land Use Mapping Level I Level II 2 Agricultural Land 21 Cropland and Pasture 22 Orchards, Groves, Vineyards, Nurseries, and Ornamental Horticultural Areas Confined Feeding Operations 24 Other Agricultural Land 3 Rangeland 31 Herbaceous Rangeland 32 Shrub and Brush Rangeland 33 Mixed Rangeland 4 Forest Land 41 Deciduous Forest Land 42 Evergreen Forest Land 43 Mixed Forest Land 5 Water Streams and Canals 52 Lakes 53 Reservoirs 54 Bays and Estuaries 6 Wetland Forested Wetland 62 Nonforested Wetland

Introduction to GIS XI. Data Input

Introduction to GIS Data input We’ve already talked about remote sensing imagery, one of the most important sources of input data Today we’ll talk about three other sources of input: GPS Scanning Digitizing

Introduction to GIS How does GPS work? We need at least 3 satellites as reference points to “triangulate” our position. Based on the principle that where we know our exact distance from a satellite in space, we know we are somewhere on the surface of an imaginary sphere with radius equal to the distance to the satellite. With two satellites we know we are in the plane where the two intersect. With three or more, we can get two possible points, and one of those is usually impossible from a practical standpoint and can be discarded

How does GPS work? Here’s how the sphere concept works
Introduction to GIS How does GPS work? Here’s how the sphere concept works A fourth satellite narrows it from 2 possible points to 1 point Source: Trimble Navigation Ltd.

Introduction to GIS How does GPS work? This method assumes we can find exact distance from our GPS receiver to a satellite. How does that work? Simple answer: see how long it takes for a radio signal to get from the satellite to the receiver. Since we know speed of light, we can answer this This gets complicated when you think about the need to perfectly synchronize satellite and receiver. We use pseudo random code to synchronize

Introduction to GIS How does GPS work? 4th Satellite helps make up for fact that ground receiver does not have perfect atomic clock. While 3 perfect satellite signals can give a perfect location, 3 imperfect signals can’t, but 4 can Imagine time to receiver as distance, with each distance from each satellite defining a circle around each satellite of that radius If receiver clock is correct, 4 circles should meet at one point. If they don’t meet, the computer knows there is an error in the clock: “ They don’t add up”

Introduction to GIS How does GPS work? So now we know how far we are from the satellites, but how do we know where the satellites are?? We can’t use them as a reference otherwise. Because the satellites are 11,000 ft up, they operate according to the well understood laws of physics, and are subject to few random, unknown forces. This allows us to know where a satellite should be at any given moment.

Selective availability
Introduction to GIS Selective availability Until May of 2000, the DoD intentionally introduced a small amount of error into the signal for all civilian users, calling it “selective availability,” so non- US military users would not have the same positional accuracy as the US military. SA resulted in about 100 m error most of the time Turning off SA reduced error to about 30 m radius

Introduction to GIS Differential GPS This is a way to dramatically increase the accuracy of GPS positioning to a matter of a few meters, using basic concepts of geometry This was used in the past to overcome SA, but with that gone, is now used for reducing the 30m error DGPS uses one stationary and one moving receiver to help overcome the various errors in the signal By using two receivers that are nearby each other, within a few dozen km, they are getting essentially the same errors (except receiver errors)

Introduction to GIS How does DGPS work? The stationary receiver must be located on a control point whose position has been accurately surveyed: eg. USGS benchmarks The stationary unit works backwards—instead of using timing to calculate position, it uses its position to calculate timing It determines what the GPS signal travel time should be and compares it with what it actually is Can do this because, precise location of stationary receiver is known, and hence, so is location of satellite

Introduction to GIS GPS Uses Trimble Navigation Ltd., breaks GPS uses into five categories: Location – positioning things in space Navigation – getting from point a to point b Tracking - monitoring movements Mapping – creating maps based on those positions Timing – precision global timing You can learn about all these applications at these web links, but we mainly care about mapping

Scanning Encodes a digital raster image of some surface
Introduction to GIS Scanning Encodes a digital raster image of some surface Three types of scanner: Flat bed: Drum Continuous feed

Introduction to GIS Digitizing Head’s up—on screen Tablet digitizing

XII. Network analysis: Won’t be on the test
Introduction to GIS XII. Network analysis: Won’t be on the test

XIII. Projections and coordinate systems
Introduction to GIS XIII. Projections and coordinate systems

So, what shape IS the earth?
Introduction to GIS So, what shape IS the earth? Earth is not a sphere, but an ellipsoid, because the centrifugal force of the earth’s rotation “flattens it out”. Source: ESRI This was finally proven by the French in 1753 The earth rotates about its shortest axis, or minor axis, and is therefore described as an oblate ellipsoid But also called “spheroid” because so close to sphere

Introduction to GIS Spheroids We must use a different spheroid for different regions to account for irregularities, or we get positional errors The International 1924 and the Bessel 1841 spheroids are used in Europe while in North America the GRS80, and decreasingly, the Clarke 1866 Spheroid, are used

The Geographic Graticule/Grid
Introduction to GIS The Geographic Graticule/Grid This is a location reference system for the earth’s surface, consisting of: Medians: lines of longitude and Parallels: lines of latitude Prime meridian is at Greenwich, England (that is 0º longitude) Equator is at 0º latitude Source: ESRI

The Geographic Graticule/Grid
Introduction to GIS The Geographic Graticule/Grid This is like a planar coordinate system, with an origin at the point where the equator meets the prime meridian The difference is that it is not a Grid because grid lines must meet at right angles; this is why it’s called a graticule instead Each degree of latitude represents about 110 km, although, that varies slightly because the earth is not a perfect sphere Can use degrees minutes seconds or “decimal degrees”

Map Projection-distortion
Introduction to GIS Map Projection-distortion The problem with map projection is that it distorts one or several of these properties of a surface: Shape Area Distance Direction Some projections specialize in preserving one or several of these features, but none preserve all

Introduction to GIS Map Projection-distortion Hence, when choosing a projection, one must take into account what it is that matters in your analysis and what properties you need to preserve Conformal and equal area properties are mutually exclusive but some map projections can have more than one preserved property. For instance a map can be conformal and azimuthal Conformal and equal area properties are global (apply to whole map) while equidistant and azimuthal properties are local and may be true only from or to the center of map

Introduction to GIS Map Projection-distortion robinson Mercator—goes on forever sinusoidal

Map Projection-General Types
Introduction to GIS Map Projection-General Types Cylindrical projection: created by wrapping a cylinder around a globe and, in theory, projecting light out of that globe; the meridians in cylindrical projections are equally spaced, while the spacing between parallel lines of latitude increases toward the poles; meridians never converge so poles can’t be shown Source: ESRI

Introduction to GIS Map Projection-General Types Conic Projections: projects a globe onto a cone In simplest case, globe touches cone along a single latitude line, or tangent, called standard parallel Other latitude lines are projected onto cone To flatten the cone, it must be cut along a line of longitude (see image) The opposite line of longitude is called the central meridian Source: ESRI

Introduction to GIS Map Projection-General Types Conic Projections: Projection is most accurate where globe and cone meet—at the standard parallel Conic projections are typically used for mid-latitude zones with east-to-west orientation.

Introduction to GIS Map Projection-General Types Planar or Azimuthal Projections: simply project a globe onto a flat plane The simplest form is only tangent at one point Any point of contact may be used but the poles are most commonly used When another location is used, it is generally to make a small map of a specific area When the poles are used, longitude lines look like hub and spokes Source: ESRI

Map Projection-Specific Types
Introduction to GIS Map Projection-Specific Types Some of the most important Mercator Transverse Mercator Lambert Conformal Conic Albers Equal Area

Introduction to GIS Datums A datum is realized by a set of referenced survey monuments, with known position. These monuments are connected by a network of precise measurements that enable the computation of a position. Datum changes when different spheroid used Datum closely approximates the mean sea-level surface for an area of interest Provides surface to which ground control measurements can be references

Introduction to GIS Datums Datum closely approximates the mean sea-level surface for an area of interest—based on spheroid Provides surface to which ground control measurements can be references The North American Datum of 1927 (NAD27) is based on Clarke 1866, and has its center in Kansas. A newer, satellite measured spheroid is the World Geodetic System 1984 (WGS84) spheroid, which is more or less identical to Geodetic Reference System 1980 (GRS80)- measured from center of spheroid

Introduction to GIS Coordinate Systems Map projections, like we discussed in last lecture provide the means for viewing small-scale maps, such as maps of the world or a continent or country (1:1,000,000 or smaller) Plane coordinate systems are typically used for much larger-scale mapping (1:100,000 or bigger)

Introduction to GIS Coordinate Systems Projections are designed to minimize distortions of the four properties we talked about, because as scale decreases, error increases Coordinate systems are more about accurate positioning (relative and absolute positioning) To maintain their accuracy, coordinate systems are generally divided into zones where each zone is based on a separate map projection

Introduction to GIS Coordinate Systems The four most commonly used coordinate systems in the US: Universal Transverse Mercator (UTM) grid system The Universal Polar Stereographic (UPS) grid system State Plane Coordinate System (SPC) And the Public Land Survey System (PLSS)

XIV. Data quality and error
Introduction to GIS XIV. Data quality and error

Data quality Components of data quality: Accuracy Precision
Introduction to GIS Data quality Components of data quality: Accuracy Precision Accuracy and Precision apply to : Position attributes

Other measures of data quality
Introduction to GIS Other measures of data quality Logical consistency Completeness Data currency/timeliness Accessibility These apply to both attribute and positional data

Random and Systematic error
Introduction to GIS Random and Systematic error Error can be systematic or random Systematic error can be rectified if discovered, because its source is understood Random error cannot be controlled for because its source is not understood. Random errors are often introduced in little bits at each stage of data collection and processing

Random and Systematic error
Introduction to GIS Random and Systematic error Systematic errors affect accuracy, but are usually independent of precision; data can use highly precise methods but still be inaccurate due to systematic error Accurate and precise: no systematic , little random error Accurate and imprecise: no systematic , but considerable random error inaccurate and precise: little random error but significant systematic error inaccurate and imprecise: both types of error

Sources of error Obvious sources: old maps, lack of cover, etc
Introduction to GIS Sources of error Obvious sources: old maps, lack of cover, etc Measurement error: GPS, RS, manual attribute classification, temporal measurement, etc Can be systematic or random Processing errors: Numerical (rounding off), geocoding, digitizing, automated classification Errors can propagate and cascade

Documentation and Metadata
Introduction to GIS Documentation and Metadata To avoid many of these errors, good documentation of source data is needed Metadata is data documentation, or “data about data” Ideally, the metadata should descrive the data according to FGDC metadata standards

Documentation and Metadata
Introduction to GIS Documentation and Metadata Critical components usually break down into: Dataset identification Data definition Dataset overview Data quality Spatial reference information Administrative information

XV. Spatial Interpolation
Introduction to GIS XV. Spatial Interpolation

Introduction to GIS What is interpolation? Process of creating a surface based on values at isolated sample points. Sample points are locations where we collect data on some phenomenon and record the spatial coordinates We use mathematical estimation to “guess at” what the values are “in between” those points We can create either a raster or vector interpolated surface Interpolation is used because field data are expensive to collect, and can’t be collected everywhere

How does it work Let say we have our ground water pollution samples
Introduction to GIS How does it work Let say we have our ground water pollution samples This gives us

What isn’t interpolation?
Introduction to GIS What isn’t interpolation? Interpolation only works where values are spatially dependant, or spatially autocorrelated, that is, where the value depends on location Where values across a landscape are geographically independent, interpolation does not work because value of (x,y) cannot be used to predict value of (x+1, y+1).

Where interpolation does not work
Introduction to GIS Where interpolation does not work Cannot use interpolation where values are not spatially autocorrelated Say looking at household income—in an income-segregated city, you could take a small sample of households for income and probably interpolate However, in a highly income-integrated city, where a given block has rich and poor, this would not work

Introduction to GIS Sampling As you can see, the density and spacing of samples depends on many things A key component of any study with spatially referenced field data is the sampling strategy If the values in your interpolation surface (layer A) depend on some factor in layer B, then we can design our sample of A based on layer B We can do this by conducting a stratified random sample

Introduction to GIS Sampling The number of samples we want within each zone depends on the statistical certainty with which we want to generate our surface Do we want to be 95% certain that a given pixel is classified right, or 90% or 80%? Our desired confidence level will determine the number of samples we need per strata This is a tradeoff between cost and statistical certainty Think of other examples where you could stratify….

How does the math work? There are many many methods for interpolation
Introduction to GIS How does the math work? There are many many methods for interpolation These are the methods used by Arc View: IDW, or inverse distance weighted interpolation: also known as “natural neighbor interpolation” SPLINE: Also known as thin-plate splines

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