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GIS BOOTCAMP. Todd Bacastow. Geography matters!. ‘Geographic Information’ is information which can be related to specific locations. Most human activity depends on geographic information. Topic 1: What is GIS?. Dozens of possible definitions Some emphasise the technology The Hardware

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gis bootcamp


Todd Bacastow

geography matters
Geography matters!
  • ‘Geographic Information’ is information which can be related to specific locations.
  • Most human activity depends on geographic information.
topic 1 what is gis
Topic 1: What is GIS?
  • Dozens of possible definitions
    • Some emphasise the technology
      • The Hardware
      • The Software
    • Others focus on applications
      • Other terms often encountered: LIS, AM/FM, Geo-information systems, etc.
      • May emphasise different roles for the system, e.g. spatial decision support system, spatial database system, etc.
one definition of gis dueker and kjerne 1989
One definition of GIS (Dueker and Kjerne, 1989)
  • “Geographic Information Systems - A system of hardware, software, data, people, organizations and institutional arrangements for collecting, storing, analysing, and disseminating information about areas of the Earth”
geographic information system
Geographic Information System
  • Concepts such as location, direction, distance, proximity, adjacency provide links between different data
    • Geographic information usually broken down into three linked components of
      • Space
      • Time
      • Attribute
geographic information system1
Geographic Information System
  • An Information System is a set of processes, executed on raw data, to produce information which will be useful in decision-making
geographic information system2
Geographic Information System
  • In a system the whole is greater than the sum of its parts (Aristotle, C4th BC)
    • GIS is a convergence of technological fields and traditional disciplines
    • Not just technology: the data, people and institutional context are as much part of GIS as are the computers and software
gis is the convergence of many disciplines


Remote Sensing





Operations Research

Computer Science


Civil Engineering

Business management

Behavioural science


GIS is the convergence of many disciplines:
gis as a tool
GIS as a tool
  • Majority view of GIS
  • Focus is on hardware, software and routines
  • A technocentric perspective
  • The favoured viewpoint of the system vendors
gis as science
GIS as science
  • Emphasis is on data, human uses, contexts
  • A more academic perspective
  • Geographic information science is the “science behind the systems”
  • Includes concepts of spatial reasoning, cognition, human-machine communication, visualisation, data modelling, etc.
gis is a product of a particular culture
GIS is a product of a particular culture
  • Most GIS developed in Europe/N. America
    • USA: Arc/Info, ArcView, Intergraph, Bentley, Autodesk, MAP, GRASS...
    • Canada: Caris, Spans, GeoVision...
    • France: GeoConcept, Carto 2-D...
    • UK: Smallworld, GIMMS, Laserscan...
    • Netherlands: ILWIS, PC Raster...
gis is a commercial product
GIS is a commercial product
  • Developments often driven by commercial considerations, less by scientific ones
  • Vendor’s decisions usually based on questions of profitability
  • Critical evaluation of proprietary GIS is rare
boundaries of gis are being pushed back
Boundaries of GIS are being pushed back
  • GIS techniques and concepts increasingly seen in other areas and applications:
    • “Office” type software
    • In-car navigation and other route-finding systems
    • Multimedia presentations
    • The Internet
    • WAP, SMS, & MMS phone technology
what gis is not
What GIS is not
  • GIS is not simply the technology: it also has a (growing and important) conceptual base
  • GIS can not produce good results from bad data or poor conceptual frameworks
  • GIS is not simply a program to produce maps
  • GIS is not a substitute for thinking!
  • GIS is not the universal answer to all problems!
data input a major bottleneck
Data input - a major bottleneck
  • Costs of input often >80% of project costs
  • Labor intensive, tedious, error-prone
  • Construction of the database may become an end in itself
    • the project may not move on to analysis of the data collected
  • Essential to find ways to reduce costs, maximise accuracy
manual data conversion involves three stages
Manual data conversion involves three stages
  • State 1: Geocoding
    • The conversion of analogue maps to digital form
  • Stage 2: Entering attribute values
    • e.g. the heights to associate with digitised contour lines
  • State 3: Linking attribute data to their own geocoded features
digital map data three possible situations
Digital map data: three possible situations.
  • The data we want already exist
    • Hopefully we can find and buy them (or they may even be free!)
  • Data exist but not in digital form
    • Will require conversion from analogue format
  • Data do not exist at all
    • Will need to collect the data ourselves by remote sensing, field data collection, etc.
data exist in digital form
Data exist in digital form
  • To be useful, have to be in right format, resolution, etc.
  • Metadata can inform us as to fitness for purpose
    • unfortunately such information not always available
    • may lead to misinterpretation, false expectations about accuracy
sources of digital map data
Sources of digital map data
  • National Mapping Organization
  • Other government agencies
  • Commercial data vendors
  • standards may be set to
    • assure uniformity within a single data set or across several data sets
    • ensure the data can be shared across different hardware and software platforms
some popular standards for digital map data include
For Vector data







.E00 (Arc Export) format


For Raster data







Some popular standards for digital map data include
data exist but not in digital form
Data exist but not in digital form
  • Need tools to convert analogue maps or other source documents to digital format
  • Digitizing may be performed manually or through automation
    • Manual methods tedious & error prone
    • Automated techniques may create bigger editing problems later
what if the data do not exist at all
What if the Data do not exist at all?
  • Field data capture
    • May be done manually (e.g. direct survey), automatically (e.g. automatic data loggers, etc.) or a combination of the two
  • Remote sensing
    • Includes satellite imagery, geophysical survey, air photos
    • May be used as alternative source of data
criteria for choosing modes of input
Criteria for choosing modes of input
  • Type of data source
    • images favour scanning
    • maps can be scanned or digitised
  • Database model of the GIS
    • scanning easier for raster, digitising for vector
  • Density of data
    • dense linework makes for difficult digitizing
  • Expected applications of the GIS implementation
integrating different data sources issues
Integrating different data sources: issues
  • Formats
    • many different format standards exist
    • a good GIS can accept and generate datasets in a wide range of standard formats
integrating different data sources issues1
Integrating different data sources: issues
  • Projections
    • Many ways exist to represent curved surface of the earth on a flat map
    • Some projections are very common
    • A good GIS can convert data from one projection to another, or to latitude/longitude
    • Input derived from maps by scanning or digitizing retains the original map's projection
    • With data from different sources, a GIS database often contains information in more than one projection, and must use conversion routines if data are to be integrated or compared
integrating different data sources issues2
Integrating different data sources: issues
  • Scale
    • data may be input at a variety of scales
    • scale is an important indicator of accuracy
    • maps of the same area at different scales will often show the same features
    • variation in scales can be a major problem in integrating data
integrating different data sources issues3
Integrating different data sources: issues
  • Resampling rasters
    • Raster data from different sources may use different pixel sizes, orientations, positions, projections
    • Resampling is the process of interpolating information from one set of pixels to another
    • Resampling to larger pixels is comparatively safe, resampling to smaller pixels is very dangerous
representing spatial entities
Representing Spatial Entities
  • The object-focused approach
    • Based on recognition of discrete objects or entities
    • May be layer-based or object-oriented
    • Usually represented by Vector GIS
two ways of representing space in a gis
Two ways of representing space in a GIS
  • The Tesseral (field-oriented) approach
    • Typically seen in Raster GIS
    • Also in some other models
vector data models
Vector data models
  • Based on the recognition of discrete objects or entities
  • The location/boundaries of these objects defined with respect to some coordinate system
  • Emphasis is on boundaries, space within and between boundaries implied
  • Objects are usually defined in terms of points, lines and areas
  • Complex graphic objects are seen as amalgamations of simpler ones
  • Typical Vector GIS include ARC/INFO, MapInfo Intergraph MGE
separation of locational and attribute data
Separation of Locational and Attribute data
  • In vector GIS, geographic information is represented in terms of
    • Locational / geometric data (“where?”)
    • Attribute information (“what?”)
    • Relationships between objects and attributes
the vector data model
The vector data model
  • Fundamental spatial primitive is a point
    • Defined by a single x,y coordinate pair
  • Points can be used to
    • locate spatial objects
    • represent Vertices (single = “vertex”) defining a line
    • represent Nodes defining start- or end-points on lines, junctions where lines meet, etc.
the vector data model1
The vector data model
  • Sequences of points can be used to define lines
  • Lines themselves can be aggregated to represent
    • Networks
    • Boundaries of polygons and regions
    • Topographic features (contours, breaks of slope, etc.).
  • An essential element of vector GIS
  • A distinct branch of mathematics
  • Defines spatial relationships between objects
    • Adjacency, connectivity, containment, etc.
  • Essential for most vector GIS operations
advantages and disadvantages of the vector approach
Advantages and disadvantages of the vector approach
  • Lower data volumes
  • More adaptable to variations in scale/resolution of phenomena
  • Tends to be more suited to social and economic applications
  • Disadvantages:
    • Less adaptable to uncertainty, fuzziness
    • Often no “lowest common denominator” of aerial unit .
objects versus layers
Objects versus layers
  • Major point of discussion in GIS since mid-1980s
  • Alternative strategies for vector representation of geographic space
    • a “stacked” sequence of layers
    • a collection of discrete objects
  • Difference in how contents of the database represents the real world
  • Echoes wider developments in Computer Science
the object view
The Object view
  • More closely mirrors natural ways of seeing the world
  • Objects usually used in speaking, writing, thinking about the world
  • Objects are fundamental to our understanding of geography
  • Object-oriented approaches may offer data storage and processing advantages
what are these objects
What are these objects?
  • Graphics objects can be points, lines, areas
  • Geographic objects can be roads, houses, hills, etc.
  • A space can be occupied by many, or no, objects
    • A river is an object (has an identity, name, coordinates, properties, etc.)
    • A line is an object (also has an identity, name, coordinates, properties, etc.)
applications of object view
Applications of object view:
  • Utilities and facilities management
    • Concept of empty space littered with objects fits many needs of managing infrastructure
    • Two or more objects may occupy same horizontal position, separated vertically
    • Smaller objects may be part of larger ones (e.g. pipes as part of networks) and vice versa
    • Idea of a variable measured everywhere on Earth has little relevance
the layer view
The Layer view
  • Locations specified by a system of coordinates
  • Geography of real world conceptualised as a series of variables (soils, land use, elevation, etc.)
  • Each layer in the database represents a particular variable
the layer view1
The Layer view
  • Layer view often more compatible with theories of atmospheric, ocean processes
  • Object view is less compatible with concept of continuous change
  • Good for resource management applications
  • Much data for environmental modelling derived from remote sensing
    • Implies a layer view
  • The layer approach usually requires many different files to represent each layer
  • Some files contain the actual data
  • Some contain registration information
  • Some contain topological information to construct complex geometries from more primitive ones
applications of layer view
Applications of layer view
  • Resource management
    • geographic variation can be described by relatively small amount of variables
    • conceptualisation reasonably constant between scales
    • movement of individuals can lead to difficulties of representation and tracking across layers
tesseral geometries
Tesseral geometries
  • From the Greek, tetara or Latintessella = a tile
  • Tessallations are “sets of connected discrete two-dimensional units”
    • thus mosaics or tilings of space
  • May be regular or irregular
  • Focus is on space occupancy
  • Emphasis is on areas, boundaries are implied
conceptual basis creating a tessallation
Conceptual basis: creating a tessallation
  • Define a geographic area of interest
  • Undertake sampling of the entire area
  • Each point is space is assigned a value
  • The data are separated into a set of vertical thematic layers
  • One item of information stored for each location within a single layer
types of tessallation

Types of tessallation

Regular tessalations


Irregular tessalations


Voronoi Tessalations

Triangulated Irregular Networks (TINs)

the raster
The raster
  • “Raster Data are spatial data expressed as a matrix of cells or pixels, with spatial positioning implicit in the ordering of the pixels” (AGI 1994)
  • Raster data structure widely used in GIS
    • e.g. IDRISI, GRASS, Arc/Info’s GRID module
the geometry of a raster
The geometry of a raster
  • Square cell
    • Adjacency defined by edges and corners
    • Connectivity to four neighbouring cells
    • Uniform orientation throughout the matrix
    • Strong self-similarity
    • Easy decomposition into identically-shaped units
    • Very efficient way of packing space
why use rasters
Why use rasters?
  • Raster data from other disciplines
  • Ideal for representing continuous variations in space
  • Common way of structuring digital elevation data
  • Assumes no prior knowledge of the phenomenon
  • Uniform, regular sampling of reality
why use rasters1
Why use rasters?
  • Often used as common data exchange format
  • Raster algorithms often simpler and faster
  • Easy to program, less need for special hardware
  • Raster systems tend to be cheaper than vector
issues and trade offs
Issues and trade-offs
  • May give very large data files
      • typical raster databases may contain > 100 layers
      • each layer typically contains hundreds or thousands of cells
  • Many options exist for storing raster data
      • some are more economical than others in terms of storage space
      • some more efficient in terms of access and processing speed
issues and trade offs1
Issues and trade-offs
  • Maximum resolution determined by the size of grid
  • Less easy to connect tabular (attribute) data to spatial objects
  • Raster data lack topology
  • Regular geometry of raster cells may not accurately reflect the variations of reality
variable resolution tessalations
Variable-resolution tessalations
  • Triangulated Irregular Networks (TINs)
    • Alternative to regular raster for terrain modelling
    • Developed in 1970s
    • Can build surfaces from irregular arrays of point elevation data
    • Many commercial GIS now offer TIN capabilities.

Topic 4: Coordinates,

Datums, and Projections

spherical coordinates

90º N Latitude






0º Latitude

Prime Meridian 0º Longitude





90º S Latitude

90º W Longitude

90º E Longitude

0º Longitude 180º Longitude

Spherical Coordinates

Spherical “grid” is called a graticule

Latitude references north and south

Longitude references east/west

Line of constant latitude is a parallel

Line of constant longitude is a meridian

Meridiansconverge at the poles

Latitude range: 0 to 90 degrees north and south

Longitude range: 0 to 180 degrees east and west

spherical coordinates1
Spherical Coordinates

A spherical coordinate measure is expressed in degrees (º), minutes (‘) and seconds (“)

1º = 60’ = 3,600” ; 1’ = 60”

Expressed as:

ddd mm ss N/S, ddd mm sss E/W

Note the convention is to express latitude (y) before longitude (x), but computer environments use x,y

  • In most digital environments, degrees, minutes and seconds are converted to decimal degrees: degrees + (min/60) + (sec/3600)
  • Harrisburg International Airport is: 40º12’N, 76 º45’W,or
  • 40.20N, 76.75W
spherical coordinates2
Spherical Coordinates

Western and Northern

Hemisphere: -x, +y

Eastern and Northern

Hemisphere: +x, +y

Western and Southern

Hemisphere:-x, -y

Eastern and Southern

Hemisphere:+x, -y

cartesian coordinates

(4.5, 4.5)

1 2 3 4 5 6

Y axis




1 2 3 4 5 6 7 8 9

X axis

Cartesian Coordinates
horizontal datum


GRS80 Spheroid

Meades Ranch




Clarke 1866


Clarke 1866 Spheroid

Horizontal Datum
  • North American Datum of 1927
  • A local datum centered on the Meades Ranch in Kansas. Surface of ellipsoid was tangent to the Meades Ranch
  • 300,000 permanent control network
  • Clarke 1866 spheroid used to define the shape and size of the earth


  • North American Datum of 1983
  • an earth centered datum where the center of the spheroid is the center of the earth
  • based on the Geodetic Reference System of 1980 (GRS80): a better approximation of earth’s true size and shape.
  • twice as accurate as the NAD27: resulted in controls shifted up to 100 meters

Meades Ranch


GRS80 Spheroid



Clarke 1866


Clarke 1866 Spheroid

vertical datum

Land Mass

Sea Level

Sea Floor

Vertical Datum

(mean sea level)

Vertical Datum
  • National Geodetic Vertical Datum of 1929
  • vertical datum based mean sea level as determined by years of observations at tidal gauging stations
  • 585,000 permanently monumented vertical benchmarks interconnected by leveling
  • North American Vertical Datum of 1988
  • 1929 datum adjusted based on more precise measurements of geoid shape and mean sea levels.
  • some bench mark heights changed up to 2 meters, but heights between adjacent benchmarks changed < a few millimeters
  • provides better geoid height definitions in order to convert earth centered GPS derived heights

To represent a spherical model of the earth on a flat plane requires a map projection!


map projections

Z = rotational axis







Map Projections

Spheroid: a three-dimensional geometric surface generated by rotating an ellipse about one of its axes.

It provides an approximate model of the earth’s shape, the first step in constructing a projection

map scale
Map Scale
  • Options to deal with minimum mapping unit size at desired design scale
    • Adopt a larger map scale for the source
      • Increased cost for acquisition
      • Increased storage for larger data volume
    • Convert area features to points or lines
      • Evidence of feature is retained
      • Inconsistency in feature representation
      • May give up desired metrics (area, perimeter)
      • May give up overlay analysis options
    • Eliminate small areas
      • Consistency in feature representation
      • No evidence of omitted features
pennsylvania statewide projection
PennsylvaniaStatewide Projection
  • Projection: Lambert Conformal Conic
  • Spheroid: GRS80
  • Central Meridian: 77º 45’ 00.0” W (-77.75)
  • Standard parallels: 40º 36’ 10.8” N (40.603)
  • 41º 16’ 33.6” N (41.276)
  • Reference latitude: 39º 19’ 59.9’ N (39.333)
  • Considerations for selecting a statewide projection for Pennsylvania:
  • Pennsylvania’s east/west extent is best suited for a conic projection
  • If you need to preserve area, use Alber’s Equal Area Conic
  • If you need to shape and angle, use Lambert Conformal Conic
  • Select two standard parallels that divide the state into approximately even thirds north to south
  • Select a central meridian that divides the state approximately into equal halves

Map Projections

  • Transform spherical geographic space to a 2-D planar surface.
    • If it is a map, it has been projected!
    • Eliminates need to carry a globe around in the pocket!
    • 2-D Cartesian coordinate space is better suited than spherical coordinates when conducting traditional surveys, mapping, and ground measurements.
  • Ensures a known relationship between map location and earth location
map projections1
Map Projections




map projections2
Map Projections
  • A tangent projection results in 1 standard parallel
  • A secant projection results in 2 standard parallels
  • A standard parallel is the mathematical point of intersection between the projection plane and the sphere.
  • Scale distortion.
    • The scale is true (1) along the standard parallel(s).
    • The scale is greater than 1 outside of the standard parallel(s)
    • On secant projections, the scale is less than 1 between the two standard parallels

Map Projections

  • Any representation of the Earth’s 3-D surface on a 2-D plane involves distortion of one or more of the following:
    • shape
    • area
    • distance (scale)
    • direction (angle)

Map Projections

  • There are many map projections
  • Each one is good at representing one or more spatial properties
  • No projection can preserve all four properties
  • The goal is to select a projection that best matches the intended use of the map.
  • Projection distortion significantly affects the properties of a small-scale map
  • Large scale maps are less effected by projection distortion
map projection distortion
Map Projection Distortion
  • Conformal projections
    • Preserve relative angle and shape for small areas, but area is very distorted
    • For any given point, local scale is constant in all directions
    • Used for navigation, meteorological charts
    • Examples: Mercator and Lambert Conformal Conic
  • Equivalent projections
    • Preserve area but shape and angles are very distorted.
    • A coin placed at any location on the map covers the same amount of area
    • Use when area conveys meaning (thematic maps showing density)
    • Examples: Albers Conic Equal Area and Peters Projection
universal transverse mercator utm









84º 30’

500,000 mE

320,000 mE

680,000 mE

0º 00’ 00”

80º 30’




Universal Transverse Mercator (UTM)


  • The cylinder is made secant to the sphere, cutting into the sphere along the lines AB and DE
  • Lines AB and DE are standard meridians 360,000 meters apart. The scale is exact (1) along these lines.
  • The scale for the area between the standard meridians is < 1 (scale too small). Outside these meridians, the scale is too large (> 1)
  • Line CM is the Central Meridian, which starts and stops at the poles
  • The UTM projection is applied every 6º, resulting in 60 UTM zones for the earth (360 / 6 = 60)
  • Good projection if map extent falls within a zone. Should not be used if map extent spans multiple zones
  • Used as State Plane projection system for states that are predominately N-S orientation (e.g. Vermont, Maine, Idaho)

0 mN

10,000,0000 mS

universal transverse mercator utm1
Universal Transverse Mercator (UTM)

72º W

75º W

84º W

78º W

81º W



Standard Meridian

Standard Meridian

Standard Meridian

Central Meridian

Central Meridian



  • Pennsylvania falls between two UTM Zones: Zone 17 and 18
  • Using either zone for a statewide projection causes excessive scale distortion
  • Defining a custom UTM zone with a Central Meridian at 78º W and Standard Meridians at 81º W and 75 º W would be a better customized use of the UTM projection for PA.
pennsylvania state plane coordinate system
Pennsylvania State Plane Coordinate System
  • Based on two different applications of the Lambert Conformal Conic Projection
    • results in two different zones: a North and South Zone
  • Minimizes scale and angle distortions for use by surveyors
  • Local governments are required by State Law to use the PA State Plane Coordinate System
pennsylvania state plane north zone

Scale: 1.000000

Standard Parallel

77º 45’W

41º 57’N

Scale: .9999568

Central Parallel

41º 25’N

Scale: 1.000000

Standard Parallel

40º 53’N

Projection Origin

40º 10’N, 77º 45’W

Central Meridian

Pennsylvania State Plane North Zone
pennsylvania state plane south zone

77º 45’W

Central Meridian

Scale: 1.000000

Standard Parallel

40º 58’N

Scale: .9999595

Central Parallel

40º 27’N

Scale: 1.000000

Standard Parallel

39º 56’N

Projection Origin

39º 20’N, 77º 45’W

Pennsylvania State Plane South Zone
pennsylvania state plane origin offsets for north and south zones

x max  2,805,600’

y max  771,700’

x min  1,204,600’

y min  162,000’


x max  2,813,400’

y max  677,900’

x min  1,188,150’

y min  153,500’


Pennsylvania State Plane Origin Offsets For North and South Zones

X offset: 2,000,000’

y offset: 0’

projection origin for both Zones: 2,000,000’, 0’

map scale1
Map Scale
  • Map scale: the relationship between map distance (or display distance) and actual ground distance
  • Scale Calculations:
    • Scale = map distance / (ground distance x conversion factor)
    • To determine map scale when map and ground distances are known:
      • 2.5” on map = 500 feet on ground

2.5/500*12 = 2.5/6,000 = 1:2,400

    • To determine ground distance when map scale is known:
      • 1:4,800 is same as 1” = 4,800”

1.82” on map: 1 * 1.82 = 4,800*1.82

1.82” = 8,7376” = 728’

map scale2
Map Scale
  • Scale can be expressed as:
    • Linear scale
      • Graphic scale bar
      • Correct if map is enlarged or reduced
    • Verbal scale statement
      • 1 in = 2,000 ft
      • Frequently used by engineers or architects
    • Representative fraction (RF)
      • 1:24,000 (ratio is correct with any units)
      • Usually used by cartographers
map scale3
Map Scale
  • Small Scale Maps
    • Large denominator in RF (1:14,000,000)
    • Maps of continents and world maps
  • Medium Scale Maps
    • Medium denominator in RF (1:24,000)
    • USGS Topographic Quadrangles
  • Large Scale Maps
    • Small denominator in RF (1:2,400)
    • Tax maps, utility maps
  • The smaller the number in the denominator, the larger the map scale
    • ½ is “larger” than ¼ and ¼ is “smaller” than ½
map scale4
Map Scale
  • Considerations for selection of source scale
    • cost
    • required accuracy
    • desired output map scale(s)
    • desired feature representation
    • density of features to be displayed
map scale5
Map Scale
  • In a GIS, scale is a function of:
    • source map scale (compiled scale)
    • desired plot scale(s)
  • Digital data can be plotted at any scale
    • accuracy is only as good as the original source scale
    • resolution of the data will become apparent if plot scale greatly exceeds source scale
map scale6





Map Scale
  • Map scale sets boundary for feature resolution
  • Feature resolution is defined as :
    • The density of features that can be shown at a given scale
    • The amount of detail (density of vertices) that can be used to represent a feature at a given scale
map scale7




f(scale) =

Map Scale
  • Feature resolution is defined as :
    • Minimum mapping unit:the smallest area feature that can be effectively discerned at plot scale
    • Generally around .15” as measured on map
      • 1:24,000 (300 ft. on ground = .15” on map)
      • 1:4,800 (60 ft. on ground = .15” on map)
      • 1:2,400 (30 ft. on ground = .15” on map)
      • 1:1,200 (15 ft. on ground = .15” on map)
map scale8
Map Scale
  • Area features smaller than the minimum mapping unit are:
    • Merged into surrounding data
    • Converted from area to line (drainage)
    • Converted from area to point (cities)
    • Deleted/omitted