View
227
Download
0
Category
Preview:
Citation preview
Copyright © 2016 Spatial Analytix, LLC
GIS for Land Surveyors
Precision. Accuracy. Reliability
Copyright © 2016 Spatial Analytix, LLC
GIS as more than just Software
GeoSpatial Data
Geoprocessing
Metadata
AGENDA
Copyright © 2016 Spatial Analytix, LLC
How is GIS different than CAD?
Its about Data, not Drawings
Points-Lines-Polygons-Symbology
vs
Topology-Attributes-Analysis
Copyright © 2016 Spatial Analytix, LLC
What do these
Colors indicate?
Depth to bedrock
% Sand
Conductivity
% Clay
Hydric Class
Crop Yield
Copyright © 2016 Spatial Analytix, LLC
How is GIS different than CAD?
Its about Data, not Drawings
Points-Lines-Polygons-Symbology
vs
Topology-Attributes-Analysis
Raster Data
Copyright © 2016 Spatial Analytix, LLC
Copyright {c} 2014 by the McGraw-Hill
Companies, Inc. All rights Reserved.
1-10
GIS data models
Vector model Raster model
Copyright © 2016 Spatial Analytix, LLC
Copyright
{c} 2014
by the
McGraw-
Hill
Compani
es, Inc.
All rights
Reserved
.
1-11
Vector model
Vertices
Polygon
Points
Nodes
Line
X
Y
Features
are stored as a series of x-ycoordinates in a rectangular coordinate system.
Features can have one of three geometry types: points, lines, or polygons.
Copyright © 2016 Spatial Analytix, LLC
1-12
GIS data models
Three Pillars of GIS
Imagery (and lots of it)- Raster
Orthos, NAIP, Satellite, UAV
Elevation – Raster and Vector
LiDAR, Contours, DEM, DTM
Vectors- Vector is Vector
Parcels, Planimetrics, NRI
Copyright © 2016 Spatial Analytix, LLC
Copyright
{c} 2014
by the
McGraw-
Hill
Compani
es, Inc.
All rights
Reserved
. 1-13
Storing Elevation Data
Contours
Raster
TIN
Copyright © 2016 Spatial Analytix, LLC
Copyright
{c} 2014
by the
McGraw-
Hill
Compani
es, Inc.
All rights
Reserved
.
1-14
Digital Elevation Model
A DEM has cells or
pixels, each of which
contains a single
elevation.
Regularly spaced array
of elevation values.
Copyright © 2016 Spatial Analytix, LLC
Copyright
{c} 2014
by the
McGraw-
Hill
Compani
es, Inc.
All rights
Reserved
.
1-15
The raster data model
Rows
ColumnsX, Y location
Raster data fileN rows by M columns
X, Y location
Georeferenced to earth’s surface
Copyright © 2016 Spatial Analytix, LLC
Copyright
{c} 2014
by the
McGraw-
Hill
Compani
es, Inc.
All rights
Reserved
. 1-16
Discrete rasters
Discrete rasters essentially store
features—but in raster format
Have relatively few values that
change abruptly from one category
to another
roads
land use
Copyright © 2016 Spatial Analytix, LLC
Copyright
{c} 2014
by the
McGraw-
Hill
Compani
es, Inc.
All rights
Reserved
.
1-17
Continuous rasters
Continuous rasters store surfaces or fields of variables that change continuously over space
Many potential values. Adjacent cells rarely share the same value.
Air photo
DEM
Copyright © 2016 Spatial Analytix, LLC
Copyright
{c} 2014
by the
McGraw-
Hill
Compani
es, Inc.
All rights
Reserved
.1-18
Continuous data
Raster is the best way to store continuously
changing values such as elevation or distance
Analysis faster and more flexible than vectors
for many applications
Vertices slow processing down with vectors
Some analysis only possible using rasters
Copyright © 2016 Spatial Analytix, LLC
The Power of Rasters: Map Algebra
Rasters are essentially arrays of
numbers
Can be added, subtracted, etc
Line up matching cells vertically
Copyright © 2015 by Maribeth H. Price11-19
5 7
2 4
3 2
1 6
8 9
3 10
Ingrid1
+
Ingrid2
=
Outgrid
Map algebra
Copyright © 2016 Spatial Analytix, LLC
Copyright
{c} 2014
by the
McGraw-
Hill
Compani
es, Inc.
All rights
Reserved
. 1-20
Raster analysis functions
Density
Least cost path
Distance Interpolation
Viewshed
Buffers
Copyright © 2016 Spatial Analytix, LLC
GIS Software
Free- QGIS
Affordable- Global Mapper
Industry Standard- ArcGIS
Specialty Extensions…
Copyright © 2016 Spatial Analytix, LLC
GIS DATA TYPES
Imagery
NYS Orthos, local orthos
Hydrography
Stream lines
Water body polygons
Wetlands (NWI)
Planimetrics
Building footprints
Pavement/sidewalks
Land cover
Impervious Surface
Copyright © 2016 Spatial Analytix, LLC
GIS DATA TYPES
Elevation Data
Contours
DEMs
LiDAR Point Clouds
Parcel Data
Boundaries
Centroids
RPS Data Extract (lots of Attribute data)
Local Data
Zoning
NRI
Copyright © 2016 Spatial Analytix, LLC
NYS DATA RESOURCES
NYS GIS Clearinghouse
https://gis.ny.gov/
NYS DEC
http:// www.dec.ny.gov/eafmapper/
http:// www.dec.ny.gov/gis/erm/
NYS DOS- Geographic Information Gateway
http://opdgig.dos.ny.gov/
Counties/municipalities
Westchester, Monroe, Dutchess, Albany, Erie, etc…
Copyright © 2016 Spatial Analytix, LLC
REGIONAL DATA RESOURCES
PA MAP = PASDA
http://www.pasda.psu.edu/
NJ Bureau of GIS
http://www.nj.gov/dep/gis/listall.html
MA Office of Geographic Information
ugly
CT DEEP or UCONN MAGIC
www.ct.gov/deep/gisdata
http://magic.lib.uconn.edu/connecticut_data.html
Copyright © 2016 Spatial Analytix, LLC
Copyright {c} 2014 by the McGraw-Hill Companies, Inc. All
rights Reserved.
GIS Data Compilation
1-37
Old: All data is local and
works in the GIS program
Copyright © 2016 Spatial Analytix, LLC
Copyright {c} 2014 by the McGraw-Hill Companies, Inc. All
rights Reserved.
Web Mapping Services
1-38
New: Servers provide data and services to many uses
on many platforms
Copyright © 2016 Spatial Analytix, LLC
Copyright
{c} 2014
by the
McGraw-
Hill
Compani
es, Inc.
All rights
Reserved
.
Types of services Map services and base maps
Provides maps as static images
Pre-symbolized; attributes not accessible
Feature service
Provides GIS features and attributes
Image service
Provides seamless access to large image datasets from satellites or aerial photography
Geoprocessing service
A tool or analysis that runs on the server and passes the result down to the user
Layer package
A single layer or group layer downloaded to local computer for use in ArcGIS Desktop
Map package
An entire map with many layers downloaded to local computer
1-39
Copyright © 2016 Spatial Analytix, LLC
Applications vs Geoprocessing
Geoprocessing = Tools. Processes that run and give you a result
Line of sight, Slope, flow direction
Buffer, Intersect, Clip, Merge
Applications = What you do with those Tools
Risk Assessment
Site Selection
Multi-Criteria Analyses
Scenario development
Apps- coded interfaces that do specific things
Web apps
Desktop apps
GEOPROCESSING
Copyright © 2016 Spatial Analytix, LLC
Dissolving
Copyright
© 2015
by
Maribeth
H. Price
10-44
Dissolve polygons on habitat or soil class
GEOPROCESSING
Copyright © 2016 Spatial Analytix, LLC
Buffering
Copyright
© 2015
by
Maribeth
H. Price
10-45
Constructs polygon areas within a specified distance of features.
Single buffers around linesMultiple buffers around points
GEOPROCESSING
Copyright © 2016 Spatial Analytix, LLC
Overlay Point-in-polygon overlay
Line-in-polygon overlay
Polygon-on-polygon overlay
Copyright
© 2015
by
Maribeth
H. Price
10-46
GEOPROCESSING
Copyright © 2016 Spatial Analytix, LLC
Habitat analysis with Intersect
Copyright
© 2015
by
Maribeth
H. Price
10-47
1. Use queries to isolate features of interest from input layers.
2. Intersect selection layers to find common areas.
GEOPROCESSING
Copyright © 2016 Spatial Analytix, LLC
Hazards mapping with Intersect
Copyright
© 2015
by
Maribeth
H. Price
10-48
Residential areas Opeche formation Areas of both
GEOPROCESSING
Copyright © 2016 Spatial Analytix, LLC
Map Algebra
Rasters are essentially arrays of
numbers
Can be added, subtracted, etc
Line up matching cells vertically
Copyright
© 2015
by
Maribeth
H. Price
11-49
5 7
2 4
3 2
1 6
8 9
3 10
Ingrid1
+
Ingrid2
=
Outgrid
Map algebra
Copyright © 2016 Spatial Analytix, LLC
Map Algebra: Conversions
Copyright
© 2015
by
Maribeth
H. Price
11-50
[Precip_cm] / 2.54
Precip in cm Precip in inches
Copyright © 2016 Spatial Analytix, LLC
Map Algebra: Cut and fill
Copyright
© 2015
by
Maribeth
H. Price
11-51
[Initial surface] – [final surface]
Cut
Fill
Copyright © 2016 Spatial Analytix, LLC
Map Algebra: model
equations
Copyright
© 2015
by
Maribeth
H. Price
11-52
Complex expressions with multiple inputs to calcuaterisk or hazard index.
Estimated Runoff is Calcuated based on four input grids: precip, slope, soil infiltration, and vegetation cover.
[Precip] * 2 + [Slope] * 4 / ( [Erode] – [Vegcover]
Copyright © 2016 Spatial Analytix, LLC
Map Algebra: logical operators
Copyright
© 2015
by
Maribeth
H. Price
11-53[Elevation] > 1200
Logical operators produce either TRUE (1) or FALSE (0) values in the output grid, based on whether a cell meets the condition.
[Slope] < 10 [crowncov] < 70 AND [crowncov] > 40
Copyright © 2016 Spatial Analytix, LLC
Boolean rasters Boolean rasters represent maps of True/False states for a particular
condition
Copyright
© 2015
by
Maribeth
H. Price
11-54Slope < 10 degrees?
1 = True
0 = False
Copyright © 2016 Spatial Analytix, LLC
Boolean overlay (AND)
Copyright
© 2015
by
Maribeth
H. Price
11-55
0
1
0
1
0
1
AND =
[PrecipGT60] AND [ElevGT1500]
Precip > 60 cm Elevation > 1500 m Lodgepole Pine
Reforestation
habitat
Copyright © 2016 Spatial Analytix, LLC
AND is also multiplication
Copyright
© 2015
by
Maribeth
H. Price
11-56
0
1
0
1
0
1
x =
Boolean AND is equivalent to multiplication. So multiplying layers works just as well.
1 AND 1 = 1
1 AND 0 = 0
0 AND 1 = 0
0 AND 0 = 0
1 × 1 = 1
1 × 0 = 0
0 × 1 = 0
0 × 0 = 0
Precip > 60 cm Elevation > 1500 m Lodgepole habitat
Copyright © 2016 Spatial Analytix, LLC
Additive Boolean overlay
Copyright
© 2015
by
Maribeth
H. Price
11-57
0
1
2
0
1
0
1
+ =
ADD the layers together to create a ranked result.
Precip > 60 cm Elevation > 1500 m Lodgepole habitat
Copyright © 2016 Spatial Analytix, LLC
Copyright
© 2015
by
Maribeth
H. Price
11-58
Copyright © 2016 Spatial Analytix, LLC
QUIZ What are the 3 Pillars of a GIS?
Imagery, Elevation, Vectors
Copyright © 2016 Spatial Analytix, LLC
QUIZ What are the 3 Pillars of a GIS?
Imagery, Elevation, Vectors
What are the two fundamental dataypes of a GIS?
Raster and Vector
Name 3 different ways to represent elevation data
Points, TIN, Raster (DEM), Vector (Contours)
Give 3 examples of point data you might need to display as a continuous surface
Elevation, well data, air quality, precipitation, noise
Copyright © 2016 Spatial Analytix, LLC
Landfill Site Selection
Problem: Find potential locations for a new landfill using these criteria
On flat terrain <= 10 degrees slope
No more than 1 km from an existing road
At least 500 meters from a stream
Meadow or low-density forest
Private Property
Minimum Parcel 10 acres
Zoned Commercial/Industrial
Develop a Boolean raster for each condition with 1 = desirable area, 0 =
not desirable areaCopyright
© 2015
by
Maribeth
H. Price
11-61
Copyright © 2016 Spatial Analytix, LLC
Copyright
© 2015
by
Maribeth
11-62
Slope condition
1. Use slope function on elevation raster.
2. Use map algebra logical operator to produce Boolean map of slope <= 10 degrees.
Copyright © 2016 Spatial Analytix, LLC
Road distance condition
Copyright
© 2015
by
Maribeth
H. Price
11-63
1. Use distance function to create raster of distance from roads.
2. Use logical operator in map algebra to create Boolean raster of areas within 1000 meters of a road.
[Distance] <= 1000
Copyright © 2016 Spatial Analytix, LLC
Stream distance condition
Copyright
© 2015
by
Maribeth
H. Price
11-64
1. Use distance function to create raster of distance from streams.
2. Use a logical operator in map algebra to create a Boolean map of areas more than 500 meters from a stream.
[Distance] > 500
Copyright © 2016 Spatial Analytix, LLC
Vegetation condition
Copyright
© 2015
by
Maribeth
H. Price
11-65
1. Select suitable vegetation density and create a layer from the selected polygons.
2. Convert the selected vegetation layer to a raster using the density attribute.
3. Reclassify the three density values all to 1 and the NoData areas to 0.
Copyright © 2016 Spatial Analytix, LLC
Find areas with Boolean AND
Copyright
© 2015
by
Maribeth
H. Price
11-66
[Slope] AND [Roads] AND [Streams] AND [Vegetation]
Copyright © 2016 Spatial Analytix, LLC
Additive model
Copyright
© 2015
by
Maribeth
H. Price
11-67
[Slope] + [Roads] + [Streams] + [Vegetation]
Copyright © 2016 Spatial Analytix, LLC
Copyright
{c} 2014
by the
McGraw-
Hill
Compani
es, Inc.
All rights
Reserved
.
1-68
Metadata
• Contains information about data that people need to understand the data and evaluate its quality• Should be provided with every data set distributed to the public• Advised for in-house data as well
Copyright © 2016 Spatial Analytix, LLC
GIS for Land Surveyors
Precision. Accuracy. Reliability
Recommended