GIS Fundamentals A First Text on Geographic Information Systems 5th edition by Paul Bolstad 1506695876 9781506695877

Figure 1-1 : A map of northern South America and southern Central America made by Diego Gutiérrez i

What is a GIS?

Figure 1-2 : GIS allow us to analyze important geographic features. The satellite image at the cente

Why We Need GIS

Figure 1-3 : The environmental impacts wrought by humans have accelerated in many parts of the world

Figure 1-4 : GIS may aid in disaster assessment and recovery. This satellite image shows the Camp fi

Figure 1-5 : Portable computing is one example of the technological pull driving GIS adoption (court

Figure 1-6 : Images taken from aircraft and satellites (left) provide a rich source of data, which m

GIS in Action

Figure 1-7 : Search and rescue operations, such as the one for Marvin Matsumoto (upper left, inset),

Figure 1-8 : Parcel information entered in a GIS may substantially improve government services. Here

Figure 1-9 : GIS may be used to streamline government function. Here, septic systems not compliant w

Figure 1-10 : A gray wolf, one of a few successfully recovered endangered species, restored with the

Figure 1-11 : Wolf recovery involves tranquilizing and fitting wolves with tracking collars. These p

Figure 1-12 : Spatial data, such as the recorded pack locations and home ranges (circles and polygon

Figure 1-13 : Air pollution from power plants and vehicles is still a significant health hazard (cou

Figure 1-14 : Small air pollution particles (dark spots, above) lodge in lungs and cause life-long d

Figure 1-15 : Scientists at NASA have developed methods to map air pollution across continents on a

Figure 1-16 : Towable samplers help measure air pollution for individual streets, at various traffic

Figure 1-17 : Fine-detailed spatial estimates of particulate air pollutants (courtesy J. Marshall).

GIS Components

Hardware for GIS

Figure 1-18 : GIS are used in a range of computing environments and network configurations, from sta

GIS Software

Figure 1-19 : Functions commonly provided by GIS software.

Open Geospatial Consortium

ArcGIS

QGIS

GeoMedia

Idrisi

AUTOCAD MAP 3D

GRASS

Bentley Map

Spatial R, Python, and GDAL

GIS in Organizations

Figure 1-20 : GIS exist in an institutional context. Effective use of GIS depends on a set of protoc

Summary

The Structure of This Book

Suggested Reading

Study Questions

Data Models

Geodesy, Datums, Map Projections, and Coordinate Systems

Maps, Data Entry, Editing, and Output

Global Navigation Satellite Systems and Coordinate Surveying

Introduction

Figure 5-1 : A GNSS and data collection package fit for long-term songbird tracking (courtesy Alex J

GNSS Basics

Figure 5-2 : The three segments that comprise a GNSS.

Figure 5-3 : A handheld GNSS receiver (left) and a GNSS receiver in use (right, courtesy Juniper Sys

GNSS Signals

Table 5-1 : GPS Signals

Figure 5-4 : Existing and proposed GNSS broadcast signals, frequencies, and positioning services. Si

Range Distances

Figure 5-5 : A single satellite range measurement.

Range = speed of light * travel time (5.1)

Figure 5-6 : A decoded C/A satellite signal provides a range measurement.

Figure 5-7 : Range measurements from multiple GNSS satellites. Range measurements are combined to na

Positional Uncertainty

Figure 5-8 : Uncertainty in range measurements leads to positional errors in GNSS measurements.

Sources of Range Error

Figure 5-9 : GNSS receivers often discard signals from satellites near the horizon. As this image sh

Satellite Geometry and Dilution of Precision

Figure 5-10 : Relative GPS satellite position affects positional accuracy. Range uncertainties are a

Figure 5-11 : GPS satellite distribution affects positional accuracy. Closely spaced satellites resu

Figure 5-12 : Observed GPS error distributions for various receivers under open sky conditions (a th

Differential Correction

Figure 5-13 : Differential GNSS positioning.

Figure 5-14 : Differential correction is based on measuring GNSS timing and range errors at a base s

Figure 5-15 : Post-processed and real-time differential GNSS correction.

Real-time Differential Positioning

Figure 5-16 : The location of radio beacon stations (dots) in the central U.S., in April, 2015. Dist

Real-Time Kinematic and Virtual Reference Stations

Figure 5-17 : The distribution of stations in the Minnesota Department of Transportation VRS network

WAAS, Augmentation, and Satellite-based Corrections

Precise Point Positioning

A Caution on Datums

GNSS Applications

Figure 5-18 : A GPS receiver developed for marine (left) and aerial navigation (right, courtesy Garm

Field Digitiziting

Figure 5-19 : Line features may be field digitized via GPS, as in this example of a GIS/GPS system m

Figure 5-20 : Ruggedized tablet computers may be carried in the field and used for data entry. Large

Figure 5-21 : Features may be entered and edited in the field using a GPS receiver and appropriate s

Single-Fix vs Averaged Accuracy

Figure 5-22 : An example of several field-digitized lines, using a professional-grade GNSS receiver

Field Digitizing Accuracy and Efficiency

Figure 5-23 : GPS satellite signals may be blocked by terrain or built structures. This reduces the

Figure 5-24 : A range pole in use with a GNSS receiver.

Figure 5-25 : The percentage of GPS position fixes that are successfully collected decreases in vall

Rangefinder Integration

Figure 5-26 : A laser rangefinder may substantially improve the efficiency of field data collection

GNSS Height Measurement

(5.2)

Figure 5-27 : Calculation of orthometric height from ellipsoidal height. Most GNSS systems report el

GNSS Tracking

Figure 5-28 : A wildebeest fit with a GNSS tracking collar. The antenna is visible as the white patc

Figure 5-29 : GNSS tracking data for wildebeest in the Serengeti and Ngorongoro Crater regions of Ta

Figure 5-30 : Real-time tracking via GPS substantially improves vehicle fleet management, particular

Optical and Laser Coordinate Surveying

Figure 5-31 : Surveying establishes the coordinates for most property lines. Field measurements of d

Figure 5-32 : A surveying instrument for collecting coordinate geometry data.

Figure 5-33 : Angles in surveys may be reported as azimuth angles, measured clockwise relative to No

Figure 5-34 : Directions may be specified as bearings, with a North or South reference, and an East

Figure 5-35 : Conversion formulas from bearing to azimuth, by quadrant.

Figure 5-36 : Coordinate geometry (COGO) allows the calculation of coordinate locations from open (s

Figure 5-37 : Example calculations of dx and dy values, given a survey distance (L) and an azimuth a

Figure 5-38 :Three-dimensional scanning lasers use coordinate geometry to record comprehensive x, y,

Summary

Suggested Reading

Study Questions

Aerial and Satellite Images

Digital Data

Attribute Data and Tables

Basic Spatial Analyses

Topics in Raster Analysis

Terrain Analysis

Spatial Estimation: Interpolation, Prediction, and Core Area Delineation

Introduction

Figure 12-1 : Air quality varies across space and time, and is only measured in a few locations. Spa

Sampling

Sampling Patterns

Figure 12-2 : Examples of a) systematic, b) random, c) cluster, and d) adaptive sampling patterns. S

Spatial Interpolation Methods

Figure 12-3 : Measurement points and values for indicator ozone concentration for 2014. Higher conce

Nearest Neighbor Interpolation

Figure 12-4 : A three-dimensional perspective representation of Thiessen (nearest neighbor) polygons

Figure 12-5 : Sample points and estimated ozone concentrations by Thiessen polygons.

Fixed Radius – Local Averaging

Figure 12-6 : A diagram and example of a fixed radius interpolation. Values within each sampling cir

Figure 12-7 : A perspective diagram of fixed radius sampling. A circle is centered on each raster ce

Figure 12-8 : Original data and sample points, and a fixed radius interpolation. Note that this meth

Inverse Distance Weighted Interpolation

(12.1)

Figure 12-9 : An example calculation for a linear inverse distance weighted interpolator. The values

(12.2)

Figure 12-10 : Inverse distance weighted interpolation typically results in a smooth, gap-free surfa

Splines

Figure 12-11 : Diagram of a two-dimensional (line) spline. Segments are fit to portions of a line. S

Figure 12-12 : A spline-fit surface.

Spatial Prediction

Figure 12-13 : Part a shows spatially autocorrelated (Layer 1) and spatially uncorrelated (Layer 2)

(12.3)

Figure 12-14 : Moran’s I is a measure of the correlation among nearby observations. This example s

(12.4)

Spatial Regression

(12.5)

Trend Surface and Simple Spatial Regression

Z = a0 + a1x + a2y + a3x2 + a4y2 + a5 xy (12.6)

Figure 12-15 : A third-order trend surface fit to the sample points.

Z = ao + a1x + a2y + a3Q + a4W (12.7)

Kriging and Co-Kriging

Figure 12-16 : Lag distances, used in calculating semivariances for kriging.

Figure 12-17 : A lag tolerance defines a range for grouping samples. The grouping aids estimation of

g(h) = 1/2n * S (Za – Zb)2 (12.8)

Figure 12-18 : An idealized variogram, with the nugget, sill, and range identified.

Figure 12-19 : A variogram, a plot of calculated and fit semivariance vs. lag distance.

(12.9)

Figure 12-20 : Sample points and predicted values from an application of kriging to the ozone data.

Prediction Accuracy

(12.10)

(12.11)

(12.12)

(12.13)

(12.14)

(12.15)

(12.16)

Core Area Mapping

Mean Center and Mean Circle

Figure 12-21 : An example of a mean center and corresponding mean circles for a set of sample points

Convex Hulls

Figure 12-22 : A set of points (a) may be enclosed by connecting the “outermost” points in the s

Figure 12-23 : The convex hull for a set of points (a) may be calculated from a sweep algorithm. Sta

Characteristic Hull Polygons

Figure 12-24 : An example of a core area defined by a convex hull (a), and characteristic hull polyg

Kernel Mapping

Figure 12-25 : Kernel mapping is based on the concept of a distribution of observations in space. Ea

(12.17)

Figure 12-26 : A density distribution is assumed, and plotted for each observation. Here an observat

Figure 12-27 : Individual density distributions may be plotted for each sample observation, as shown

(12.18)

Figure 12-28 : We choose both the general form of the density distribution, and a bandwidth paramete

(12.19)

Figure 12-29 : Kernels may be used to map density distributions across a two-dimensional surface. De

(12.20)

Figure 12-30 : Kernel mapping may be used to identify core areas, although the areas that will be de

Time-Geographic Density Estimation

Figure 12-31 : A sequence of observations on a moving object, from an initial time (t0) to a final t

(12.21)

(12.22)

Figure 12-32 : An object is estimated to have been contained within an ellipse, given two subsequent

Figure 12-33 : The process of calculating a geoellipse density between two sequential points, repres

Figure 12-34 : An example of the overlapping set of geoellipses used to create a composite density f

(12.23)

Figure 12-35 : An example time-geographic density function for a sequential set of control points. T

Summary

Suggested Reading

Study Questions

Spatial Models and Modeling

Data Standards and Data Quality

New Developments in GIS

Appendix A: Glossary

Appendix B: Useful Conversions and Information

Appendix C: Answers to Selected Study Questions