GEOLOGICAL SURVEY CIRCULAR 712 

Earth Science Data in Urban and Regional 

Information Systems-A Review 

Work done in cooperation with the 

U.S. Department of Housing and Urban Development, 

Office of Policy Department and Research 

Earth Science Data in Urban and Regional 

Information Systems-A Review 

By Victor W. Adams 

GEOLOGICAL SURVEY 

Work done in cooperation with the 

U.S. Department of Housing and Urban Development, 

Office of Policy Department and Research 

197!) 

CIRCULAR 712 
United States Department of the Interior 

Geological Survey 

V. E. McKelvey, Director 

Library of Congress Cataloging in Publication Data 

Adams, Victor W. 

Earth science data in urban and regional information systems-A review. 

(Geological Survey Circular 712) 

Bibliography: p. 28-29. 

1. Information storage and retrieval systems-Earth sciences. I. Title. II. Series: United States. 

Geological Survey Circular 712. 

QE75.C5 no. 712[QE48.8] 557.3'08s[550'.28'5] 75-33226 

free on application to the U.S. Geological Survey, Notional Center, Reston, Vo. 22092 
CONTENTS 

Page 

~bstract -------------------------------------------- 1 

Page 

Urban and regional information systems-Continued 

Data base organization for geographical information 

systems-Continued 

Introduction ______________________________ -- ____ ____ 1 

Computer information systems ______________________ 2 

Computer systems ________ ______________________ 2 Incorporating line and point data ____________ 13 

Management information systems ________________ 3 Data structures for information systems __________ 13 

Geographical information systems ________________ 3 Some selected systems -------------------------- 14 

Data base organization ---------------------- 14 

Software capability-------------------------- 16 

Earth science data and the San Francisco Bay region __ 3 

Methods of earth science data entry __________________ 5 

Map digitization and image digitizers ------------ 5 

Optical scanners -------------------------------- 5 

Utilization of earth science data __________________ 17 

Natural resource information systems ________ 17 

Problems associated with map digitization ________ 8 Program inventory and geologic modeling ____ 18 

Direct entry of field data ________________________ 9 Earth science data for information and predictive 

Urban and regional information systems______________ 9 

Data base organization for geographical information 

systems ----------------------------------- 10 

land use ------------------------------ 19 

Evaluation and recommendations -------------------- 20 

Data base organization -------------------------- 24 

Map digitization -------------------------------- 26 

FIGURE 

~real type data ______________________________ 10 

1. 

2. 

3. 

4. 

5. 

6. 

7. 

8. 

9. 

10. 

11. 

12. 

13. 

14. 

15. 

16. 

17. 

18. 

19. 

Polygon or area boundary ________________ 10 Summary ____ _ ___ __ _______ __ ___ _ _ ____ _ _ ___ _ _ _ ___ ____ 27 

Grid structure -------------------------- 11 References cited ------------------------------------ 28 

ILLUSTRATIONS 

Page 

Sample geologic map showing types of land-use data____________________________________________________ 6 

Sketch showing approximation of a curved line by a series of straight line segments______________________ 7 

Photograph of image digitizer _____________________ --------------------------------------------------- 8 

Computer printouts showing steps in digitizing a curved line____________________________________________ 9 

Photograph of IBM optical scanner __________________________________________________________ ---------- 10 

Sample station sheet for encoding field data to be keypunched into a computer -------------------------- 11 

Sample map of study region containing eight digitized land-use zones ____________________________________ 11 

Digitized maps of land-use zones and soil types -------------------------------------------------------- 12 

Sketches showing overlay, union, and intersection of digitized soil-type and land-use data ---------------- 12 

Sample map showing grid overlay to be used in digitizing data------------------------------------------ 13 

Sample map on which grid size is small relative to polygon size ---------------------------------------- 13 

Diagram of modeling system used by Bay ~rea Transportation Study Commission ________________________ 14 

Map showing tract zones used in models of the nine-county San Francisco Bay region -------------------- 15 

Sample map to be digitized by an information system that stores coordinates of arcs of area polygons ______ 18 

Example of an interactive computer language, part 1 -------------------------------------------------- 22 

Example of an interactive computer language, part 2 -------------------------------------------------- 23 

Graph showing error tolerance in different types of planning-------------------------------------------- 24 

Part of the slope map of the Mount Sizer area---------------------------------------------------------- 25 

Map zones for the Universal Transverse Mercator projection in the United States ------------------------ 26 

TABLES 

Page 

TABLE 1. Characteristics of four generations of computers ________________________ -------------------------------- 3 

2. Program inventory ---------------------------------------------------------------------------------- 18 

3. Landslide failure record for rock units in San Mateo County -------------------------------------------- 21 

III 

Earth Science Data in Urban and Regional 

Information Systems-A Review 

By Victor W. Adams 

ABSTRACT 

Computerized information systems for urban land use and 

regional planning are surveyed in terms of their capability for 

handling geographically related data, especially earth science 

data. The need for planners to consider these data stems from 

the growing necessity to consider ( 1) the environmental impact 

of urbanization of open areas and (2) environmental hazards in 

the development of long-range plans and subsequent zoning 

and construction ordinances. This study involves earth science 

data for the nine-county San Francisco Bay region. 

Computer information systems are described briefly, and 

terms used by computer technologists are defined. 

Studies made by the U.S. Geological Survey for the benefit of 

urban planners in the San Francisco Bay region have involved 

a wide variety of geologic data. Many of the techniques used in 

deriving the end result can be duplicated in the computer. 

Urban and regional information systems that must handle 

relatively large (multicounty) areas may require a wide range 

of capabilities. Factors to be considered include the following: 

(a) interactive and batch mode capability, (b) large capability 

for data manipulation and computation, (c) multiple type input 

and output devices, (d) multiterminal support, and (e) support 

of multiple data bases. The extent to which a particular system 

incorporates these capabilities depends on regional needs. The 

specifications of any particular system should be determined by 

its users for the natural environment of the region. The users, 

primarily planners, should be aided by environmental scientists and computer specialists. Until such specifications are 

made, it is impossible to estimate the cost of a system, but the 

initial cost of the data base will be high. 

INTRODUCTION 

The San Francisco Bay Region Environment 

and Resources Planning Study (SFBRS) is a 

cooperative effort that began in January 1970 between the U.S. Geological Survey of the Department of Interior and the Office of Policy Development and Research of the Department of Housing 

and Urban Development. It is a study of physical 

environmental factors, particularly geologic 

hazards, and their relation to urban and regional 

planning. The area of study is the nine-county San 

Francisco Bay region. The need for such a study 

grew from the recognized need for incorporating 

1 

physical environmental data in the regional planning effort. The products of the study are maps and 

reports divided into three series: basic data contributions, the technical series (derived from the 

data for a technical audience), and the interpretive 

report series (a final derivation for a nontechnical 

audience such as planners and government officials). The program elements are grouped into four 

categories: topographic, geologic and geophysical, 

hydrologic, and planning program elements. The 

U.S. Geological Survey and Department of Housing and Urban Development (1971) have published a detailed description of the program elements and products of the bay region study. This 

report is one of the planning program elements. 

Environmental factors are becoming increasingly important to planners in the bay region. 

Hazards associated with these factors, such as 

earthquake fault zones and slopes that are potentially unstable, should be recognized and dealt 

with in the planning phase. The consideration of 

environmental factors also entails the need toestimate the impact on the environment of various 

land-use alternatives associated with rapid urbanization. Many environmental factors in the 

area under study are regional in nature. Earthquake fault patterns are very extensive. Depletion 

of an underground aquifer has caused land subsidence in adjacent areas as well as in the immediate 

area. Thus, the amount of data to be considered by 

planners in studying land-use alternatives can become very great, and use of this data in the traditional form of maps and reports can be very time 

consuming. Increasingly, this has led to the use of 

modern electronic computers and to the development of information systems to handle environmental and land-use type data. 

The management of data has become a problem 

in modern society. Almost everyone is familiar 

with the vast amount of data involved in proces-
sing credit card information and in the processing 

of hundreds of millions of federal and state income 

tax returns. Government agencies and private 

companies concerned with the land-use planning 

process are also deeply involved in this problem. 

!he amount of data handled has grown, but more 

Important has been the growth in requirements for 

managing and manipulating this data. Now environmental data must become a part of the pool of 

data used by the computer as a part of a land-use 

information system. 

This study is concerned with problems associated with the inclusion of earth science data in 

computer-based urban and regional information 

systems. How, for example, is earthquake data on 

a map read into a computer? How are map areas 

with potentially unstable slopes to be stored in a 

computer data base? This report is a summary of 

~he work .of others since the SFBRS is not directly 

Involved In the development of such an operational 

computer system but rather is interested in the 

pro~er and timely use of earth science data by 

regwnal planners. The characteristics of urban 

and regional information systems are determined 

mainly from a study of two systems in the San 

Francisco Bay region, that of the Metropolitan 

Transportation Commission and that of Santa 

Cla~a County, which are taken to be typical of 

regwnal computer-based information systems. 

These systems were used to examine the problems 

in_volved. in. making earth science data compatible 

with existing urban and regional information 

systems. 

This report is primarily for planners, especially 

those with a limited knowledge of computer 

technology. It may also be valuable to scientists 

a.nd others working with planners in this application of earth science technology. In view of the 

intended audience ofthis report, a brief section on 

computer-based information systems provides 

background information and defines terms for an 

understanding of the sections that follow. 

COMPUTER INFORMATION SYSTEMS 

This section presents a brief, nontechnical descriptio.n of certain computer information systems 

to provide a frame of reference for later sections. 

First, digital computer systems in general are discussed to define terms and give a summary of the 

rapid development of computer technology and 

what may reasonably be expected in the near future. Next, two special computer systems are 

2 

defined: management information systems and 

geographic information systems. 

COMPUTER SYSTEMS 

A digital computer system can perform the following operations: (1) ~~read" data or instructions 

with such devices as card r:eaders or keyboards, (2) 

perform arithmetic operations on data, (3) determine which operations to perform on the basis of 

instructions or the result of some previous operations, and ( 4) ~~output" the results in a form such as 

hard copy by a printer or by graphic display on a 

device similar to a television screen, hereafter 

referred to as a display tube. The sequence of 

operations is given by a set of instructions called a 

program. Within the computer the data and the 

instructions are in the form of a binary code 

because the circuit elements operate at two 

voltage levels that can be called non" and ~~off' or 

~~ zero " an d ~~ one. " E h ac on oro ff stage represents 

one binary digit (one bit of information). 

A typical system consists of a central processing 

unit (CPU), various input and output (1/0) devices, 

and auxiliary storage such as disk storage units 

and magnetic tapes. The CPU is the heart of the 

computer system comprising main storage, which 

holds programs and data, an arithmetic unit, and 

associated circuitry. The way in which the physical units are put together is called hardware architecture. 

Programs are of two types, systems or application, and together constitute what is called 

software architecture. Systems programs are a 

semipermanent part of the computer system since 

they are infrequently replaced. They include support programs for system and program control, 

programs for hardware testing, and standard 

mathematical subroutines, for example, and can 

be thought of as residing in the machine. As such, 

their instructions are in binary code in what is 

called machine language. System software relieves the user from the necessity of writing common segments of instructions for each program. 

Applications programs, as the name implies, depend upon specific user requirements, may be 

changed frequently, and can be thought of as being 

read into the machine for each use. Most applications programs are written in a higher level language such as fortran (an acronym for formula 

translation) in which the statements are quite 

similar to English. The advantages of a higher 

level language are that it is independent of the 
machine and easy te learn and use compared with 

machine language. 

When an application program with all the data 

it requires is read into the machine at one time and 

results are outputted after the program has been 

completed, the computer system is operating in 

batch mode. If the user reads in a few instructions 

at a time at a keyboard terminal and waits for 

results to be outputted (as on a display tube) before 

reading in more instructions, the computer system 

is operating in conversational or interactive mode. 

This is usually done when more than one user 

shares the computer in a multiple access or timesharing system. Switching from one terminal 

(user) to another is so rapid that any one user has 

the impression that only he is using the system. 

Special higher level languages are used for the 

conversational mode. 

Table 1 summarizes some information on four 

generations of computers and generally indicates 

the improvement in computer technology over the 

last 20 years. While the magnitude of any particular figure will be meaningful only to specialists, 

the trend over the 20-year period can be appreciated by all. 

MANAGEMENT INFORMATION SYSTEMS 

The first business application of the computer 

was recordkeeping in which the computer replaced 

clerks. This use was followed by simple control 

systems such as for inventory or production. For 

these applications the main function of the computer was to handle a large amount of data. These 

data, stored in the computer system in auxiliary 

storage devices, are called the data base of the 

system. The technical problems involved are those 

of information storage and retrieval, the computer 

system requiring a large I/0 capability rather 

than a large capacity for computation. 

Within the last several years computer systems 

have been developed with the goal of providing 

management with the information it requires for 

decisionmaking; these systems are called management information systems and incorporate 

many of the tools and techniques of the mathematical sciences such as statistical analysis of data, 

simulation and modeling of various parts of the 

business operation or segments of the economy, 

and utilization of various methods of optimization. 

The computing ability of the system has been enlarged without sacrifice of the I/0 capability. 

GEOGRAPHICAL INFORMATION SYSTEMS 

A geographical information system is a computer information system with the capability of 

handling spatially related data, specifically maprelated data such as tract boundaries and stream 

networks. The spatial characteristics should be 

preserved in the system so that (1) the data can be 

indexed by geographical location, (2) the data can 

be manipulated with relation to these spatial 

characteristics, and (3) one form of output can be a 

map. The methods by which these are achieved are 

discussed in a following section. 

EARTH SCIENCE DATA AND THE 

SAN FRANCISCO BAY REGION 

The earth science data involved in this study are 

restricted to the data generated by the San Francisco Bay Region Study Program. The San Francisco Bay region was chosen for study for the following reasons: 

1. There is a wide variety of environmental factors in the area, such as the natural hazards 

of earthquakes, landslides, and flooding, as 

well as the environmental effect of the bay. 

These factors constitute the main reason why 

the bay region was originally selected for the 

study program. 

2. Many of the land-use problems are regional in 

nature, such as waste disposal and industrial 

pollution, water supply and land subsidence, 

TABLE !.-Characteristics of four generations of computers 

Computer generation ____________________ 1 

Period _ _ __ __ ____ _ _ ____ _ _ __ _ _ _ _ __ _ _ _ _____ 1952-58 

Basic electronic component ______________ Vacuum tube 

Component density/ft3 __________________ 104 

Cost, $/bit: Core ________________________ 2 

~emory -------------------- .5 

Cost($ to execute 106 ) __________________ 10 

Relative failure rate ____________________ 1 

3 

2 

1958-63 

Transistors, solidstate diodes. 

106 

.75 

.05 

1 

.01 

3 

1963-69 

Integrated 

circuits. 

107 

.1 

.005 

.05 

.001 

4 

1969-? 

Large-scale 

integrated arrays. 

108 up 

.005 

10-5-10-6 

.001 

.0001 
landfill and its effects on the bay, and those 

problems associated with industrial locations, residential development, and transportation. 

3. The bay region is an area of rapid urban 

growth, and its land-use problems need to be 

solved soon. 

The following statistics (U.S. Geological Survey 

and Department of Housing and Urban Development, 1971) of the San Francisco Bay region are 

included for those who may not appreciate its size: 

Population (1970) ------------------------------ 5,083,549 

Dwelling units (1965) -------------------------- 1,404,146 

Area (land)------------------------------------ 6,952 mi2 

San Francisco Bay, original area (1835) __________ 680 mi2 

Filled or diked area __________________________ 280 mi2 

Remaining area ---------------------------- 400 mi2 

Linear shoreline ______________________________ 276 mi 

Shoreline, public access ________________________ 5 mi 

Comparisons 

(1970! 

Area Population 

San Francisco Bay region ____ 7,416 mi2 5,083,549 

2,910,000 

3,082,217 

Israel ______________________ 7,993 

Connecticut ________________ 5,009 

The various interpretive studies, or program 

elements, underway on the San Francisco Bay region program were reviewed. These studies, subject to change before completion of the SFBRS, are 

listed below: 

Program element Published products 

Slope stability study ______ Landslide susceptibility maps, 

four to seven zones, depending 

on the area. 

Bay mud ________________ Maps of land-use categories, 

three to fifteen zones. 

Coastal erosion Coastal strip maps of erosion 

(San Mateo County). hazards, three to four zones. 

Unconsolidated deposits __ Map of land-use categories, 

five zones. 

Active faults ____________ Map of active fault zones. 

Engineering behavior ____ Map with matrix of descriptive 

information and inferred engineering behavior for 30 to 50 

map units (bedrock). 

Water-quality studies ____ Report of water quality at 

Erosion and sediment 

studies. 

various locations in streams, 

lakes, and reservoirs. 

Map of erosion provinces, 

six to seven zones 

(plus cross sections). 

Flood inundation maps ____ Map of flood-prone areas 

(boundary of 100-year 

flood zone). 

Urban drainage system __ Peak discharge for design storms 

of2, 5, 10, 25, 50, and 100 years. 

4 

Program element 

Land pollution 

susceptibility study. 

Coastal flooding ----------

Mineral commodity study 

Seismicity and ground 

studies. 

Earthquakes and their 

locations. 

Ground water studies 

Published products 

Map of evaluation of site 

locations for four types of 

disposal systems. 

Map of areas of potential 

inundation by tsunamis. 

Commodity location maps, 30 

commodities. 

Seismic susceptibility map, 

eight zones. 

Computer data bank of recent 

earthquakes in the San 

Francisco area. 

Map of aquifer yield, four zones. 

Map of certain chemical constituents of ground water, four 

zones. Ground water hazards 

map. Water storage tables for 

main alluvial valleys. 

Most of these products are derived from basic 

information such as topography, location of active 

fault zones, and knowledge of bedrock formations, 

through the application of scientific and engineering principles. Basic data for the purpose of this 

report are considered to include: 

1. Geologic map 

a. Bedrock (including depth, where available, and strike and dip) 

b. Fault lines (or zones) 

c. Unconsolidated deposits 

d. Landslides 

e. Mineral deposits, sand and gravel 

2. Slope map (or slopes derived from topographic 

map) 

3. Topographic map 

4. Rainfall map (two types, annual average and 

maximum 48 hours) 

5. Vegetation map 

6. Land-use map 

7. Land subsidence map 

8. Tabular information 

a. Engineering properties of soil, unconsolidated deposits, bedrock 

b. Stream-quality information and rating 

tables (spot locations) 

c. Temperature and other variables in lakes 

and reservoirs 

d. Suspended sediment (spot stream locations) 

e. Accumulated sediment (various lakes 

and reservoirs) 

f. Depth to ground water (spot locations) 

9. Earthquake data 

10. Soil map 

11. Historic flood data 
For a geographical information system, this information can be classified as follows: 

1. Areal or zonal information, such as land-use 

zones or bedrock zones, represented on map 

by boundary lines. 

2. Line information such as stream networks or 

streets. 

3. Point information such as well locations. 

4. Tabular information such as engineering 

properties of soil. 

Examples of these information categories are 

given on figure 1. Areal information is illustrated 

by surficial deposits and rock formations, for 

example, the bay mud. Line information is illustrated by the San Andreas fault line and also by 

the bedrock contour lines. An example of point 

information is the location of small landslide deposits denoted by the small triangles. 

METHODS OF EARTH SCIENCE 

DATA ENTRY 

This section examines the methods by which 

earth science data are entered into the computer 

data bank and the various kinds of equipment used 

in the process. 

MAP DIGITIZATION AND IMAGE DIGITIZERS 

Earth science data was classified in the last section as tabular, point, line, and areal data. The 

entry of tabular information into the computer 

data bank presents no special problem. By properly labeling the table and organizing its contents, 

there is no difficulty in providing software that can 

locate the table and extract information from it. 

The same is true for point information because it 

can be referenced by its coordinates, for example, 

latitude and longitude. Areal and line information 

must be handled differently. 

Because an area is represented on a map by its 

boundary line, the problem of entering .. areal or 

line information into· a computer is the same-to 

convert a line into a set of discrete values that can 

then be read in. The process is called map digitization. One way this can be accomplished is by approximating a curved line by a series of straight 

line segments (fig. 2). A straight line segment can 

be represented by its end points, so that a line can 

be represented by a series of points, each given by 

its coordinates. The closer together the points, the 

better the approximation to the original line. 

An image digitizer (fig. 3) is a device with a 

5 

reading head free to move in two dimensions over a 

fiat surface on which the map to be digitized is 

placed. An operator follows the line with the reading head whose position is always digitally known. 

Coordinates of its position can be read in automatically (a ((read in" for a preset time interval or a 

((read in" when displacement in the X or Y direction exceeds a preset amount) or at operator command. Coordinates are measured from some zero 

position set at start of digitization. 

Speed of digitization depends upon operator skill 

and map complexity. Boyle (1972, p. 699, 700) 

gives a range of 120--360 inches (300--900 em) per 

hour. Experience within the U.S. Geological Survey gives a speed of 120 inches (300 em) per hour 

for digitizing elevation contours on a topographic 

map. 

The cost of image digitizers ranges from about 

$15,000 to $55,000, 1 depending upon precision and 

auxiliary equipment. 

Automatic digitizers are made in which the 

operator function is performed by an automatic 

line follower. Experience with these devices for 

map digitization is limited compared with the use 

of image digitizers. Boyle (1972, p. 702) gives 3,600 

inches (9,000 em) per hour as an attainable rate. 

High speeds are possible only for very simple 

maps. Operator initiative is required where a 

decision must be made, such as where branching 

occurs, slowing the process considerably. This 

equipment is now priced in the range of $100,000 

to $200,000. 

A recent book by Auerbach Publications (1972) 

gives an excellent description of the equipment as 

well as price information for both manual and automatic digitizers. 

OPTICAL SCANNERS 

A map can be digitized by an optical scanning 

device that records coordinates (explicitly or implicitly as described below) of any information differing from the map background as the map is 

scanned in a regular way. On a map with line 

information of only one kind, the device will see 

background (white) or part of a line (black). It 

looks at a small square (for example, 0.001 x 0.001 

inch) at a time and sees either predominant white 

or predominant black since line thickness is much 

greater than 0.001 inch (if the square is half black 

and half white, the scanner probably will see 

1 Prices and cost estimates in this circular refer to 1972-73, when this review 

was made. 
25 ' 

FIGURE L-Preliminary geologic map of part of the San Francisco Bay region showing types of land-use information, 

San Andreas fault zone (heavy lines) is shown in lower left 

corner, Contacts between rock units shown by thin lines; 

letter symbols are keyed to brief descriptions of the rock 

6 

2 Kl LOMETRES 

units in the map explanation (for example, Qls = large 

landslide deposits, Qm =bay mud), Triangles denote landslides too small to show at the scale of this map, Depth to 

bedrock shown by contours in upper half of map (after 

Brabb, 1970), 
black). If white is seen, a zero is recorded, ifblack is 

seen a one is recorded. Thus output ofthe device is 

a series of zeros and ones with a coordinate implicit 

for each since the device scans in a regular way. 

One way is to scan from left to right in a horizontal 

line (0.001 in. wide), then move down 0.001 inch 

and scan the next line. Software processing then 

removes the zeros and attaches explicit coordinates to each "one." On the computer printout of a 

line from a small map section, an "x" is printed for 

each position where a "one" has been recorded (fig. 

4A). The scale is greatly enlarged because each 

print position represents the small square the device sees each time it looks at the map-the 0.001 

x 0.001 inch square. It is also possible to record not 

the zeros but instead the explicit position (x, y 

coordinates) of each "one" as the scanner sees it. In 

either method, the next step in software processing 

is to identify lines from the series of "ones" by 

obtaining the center line of each band of "ones" 

(fig. 48 ). 

Finally, a program replaces the series of "ones" 

with a set of straight line segments, each identified 

by endpoint coordinates and a direction. This produces a file ofline segments as if the map had been 

digitized using an image digitizer (fig. 4C). 

The use of scanners for the digitization of map 

information is just emerging from the research 

and development stage. Users have very little experience on which to base predictions of equipment 

cost and performance. IBM2 developed a drum 

scanner for the Canadian Geographical Information System (Tomlinson, 1968). The material to be 

scanned is a traced zone boundary sheet. In this 

system a bit of information (zero or one) is recorded 

for each spot seen, with subsequent processing as 

described above. IBM subsequently developed a 

drum scanner for the U.S. Army Engineer Topographic Laboratories, Fort Belvoir, Va. (fig. 5) that 

can scan maps and separate eight colors from the 

manuscripts to individual digital codes. For a particular class of information (color), the particular 

code number is recorded for each spot seen, with 

subsequent software processing as described above 

for each class. 

A microdensitometer is a device used to con vert 

photometric data, as on film negative, to digital 

form for computer processing. These are commercially available and are used in research in many 

fields. Most models can be obtained with an optical 

2 Use of specific corfX)ration or trade names is for descriptive purposes on ly and is 

not an endorsement of these firms or products by the U.S. Geological Survey. 

7 

FIGURE 2.-Approximation of a curved line by a series of 

straight line segments. 

aperture as small as 25 micrometres (0.001 in.) 

square, and some models can take a film as large as 

14 x 17 inches (36 x 43 em) for the input manuscript. They can discriminate between as many as 

256 gray levels, each level having its own code for 

computer processing. Partial success has been 

achieved in digitizing maps by photographing the 

map and then using the negative for the input 

manuscript. This method can work very well if the 

map contains only one class of data. If the map 

contains more than one class of data, each class 

must have its own distinct and consistent gray 

level for subsequent separation in the software 

processing. Cost of these devices at present ranges 

from $22,000 to $25,000 for the basic unit. 

The Oak Ridge National Laboratory is digitizing map information in connection with their environmental program.3 Map overlays are prepared 

that contain only the information to be digitized. 

These overlays, or parts of them, are photographed 

by a 35-mm camera. Each 35-mm frame is then 

scanned by a Hough-Powell flying spot scannerdigitizer at the University of Pennsylvania. One 

horizontal line at a time is scanned, with the address (x, y) coordinates recorded each time the 

background changes from white to black or black 

to white. This technique is very useful for digitizing areal information. The areas to be recorded are 

3QRNL-NSF Environmental Program under NSF Interagency Agreement No. 

AAA- R-4-79. 
FIGURE 3.-Image digitizer used by the U.S. Geological Survey. 

blacked in on the overlay. The scanner then records the boundary points of each zone for each 

scan line. 

PROBLEMS ASSOCIATED WITH MAP DIGITIZATION 

The process of map digitization requires a quality control procedure to ensure that the digitized 

data represent the map data. This procedure consists of outputting the digitized map data to a plotting device, reproducing the digitized lines and 

points on transparent paper. This can be overlaid 

on the map that was digitized to provide a visual 

check of the accuracy of the process. The use of a 

display tube on line with an image digitizer enables gross errors to be detected and corrected before the entire map is completed. 

Ideally, for digitization, a map should have only 

8 

one class of information-for example, elevation 

contour lines. Most maps, however, are complex, 

containing more than one class of information and 

cluttered with various symbols. These data must 

be identified before or during digitization. 

With the use of image digitizers or automatic 

line followers, the operator involved is generally 

not familiar with the map being digitized. The 

process of digitization can be slowed considerably 

where lines serve multiple purposes (for example, 

where a fault line is also part of the boundary of a 

bedrock formation), where several classes of data 

must be digitized, or where there are gaps in the 

data or illegible data. Pre-editing the map manuscript may speed up the digitizer processes; however, for some maps it may be less costly to edit 

after digitization and correct the digitized data. 
When new maps are to be made for digitization, it 

is obviously better to keep them uncomplicated. If 

digitization is performed with an optical scanner 

that distinguishes only between map background 

and information, it is necessary to eliminate data 

that are not to be digitized. Map overlays, one for 

each class of data to be digitized, can be used to 

extract the desired data. A plot of the digitized 

data can easily be compared with the overlay as a 

quality control check. 

DIRECT ENTRY OF FIELD DATA 

Most geographically related data will be entered 

into the computer by digitization of maps. However, the possibility of direct entry of field data into 

the computer should be considered for purposes of 

correcting existing entries or adding data where 

there are gaps in the record. 

Smith and Berg (1973) have developed field 

forms for recording data that are specially designed to be read by a keypunch operator without 

further coding (fig.6). Only data entered in the 

boxes are keypunched. Similar forms have been 

designed for other kinds of data. 

Certain optical scanners can recognize and read 

in typed symbols and some handprinted symbols. 

For example, the Geological Survey has one that 

recognizes the following handprint characters: ttA 

C T X 1 2 3 4 5 6 7 8 9 0 + !" provided they are 

carefully made and of a certain size. If a field form 

could be designed requiring only these symbols 

and with boxes of the correct size, the scanner 

could read the sheets directly, thus bypassing the 

keypunch operation. Such a form should be field 

tested to see if it is practical. Alexander-Marrock, 

Friend, and Yeats (1970) have described a similar 

approach utilizing a mark-sensing field form. 

URBAN AND REGIONAL INFORMATION 

SYSTEMS 

A study conducted by the U.S. Department of 

Housing and Urban Development (1968) distinguishes between planning- and operationsoriented information systems. Operations personnel are concerned with the present, planners with 

the future. Therefore, planners are interested in 

present data only to the extent that it can be used 

to predict the future. The fundamental difference 

between the two stems from the planning process. 

Planners deal with aggregates and are concerned 

with relationships between variables, while operations personnel must deal with individual en9 

,-

1 

A 

B 

C I 

__________ _j 

FIGURE 4.-A, Computer printout of scanned line. B, Center 

line of the scanned line. C, Scanned line represented by end 

points of straight line segments. 

tities. At present (1968, when the report was published) information systems mainly retrieve data 

and produce various summaries and statistical 

analyses; in the future they will estimate current 

states, predict future states, and develop alternatives. To accomplish this, they must use simulation and mathematical modeling. 

It is possible for one computer information system to have both capabilities. The operationsoriented information system corresponds to the 

older business information systems discussed previously in the section on computer information 

systems, and the planning-oriented information 

system to the newer, evolving management information systems. 

An urban and regional information system involves socio-economic data (for example, land use, 

population, income, employment, assessed valuation) that, to be effectively used, must reflect their 

geographical location. Therefore, such a system 

should combine capabilities of a management information system with the capabilities of a geographic information system. The specifications of 

any particular system will vary depending on user 

requirements. 
DATA BASE ORGANIZA TJON FOR GEOGRAPHICAL 

INFORMATION SYSTEMS 

The fact that a system is a geographical information system is reflected in its data base because 

the information regarding the geographical distribution of data resides there. The data base of 

such a system can be organized in several ways. In 

the following discussion, a record is defined as a 

collection of related items of data treated as a unit. 

A data file is a collection of related records. A data 

item is an individual item of information in a record. Data structure refers to the arrangement and 

interrelation of records in a file. 

AREAL TYPE DATA 

POLYGON OR AREA BOUNDARY 

Consider a land-use map (fig. 7) in which the 

boundaries of the various land-use zones have 

been digitized as described in the previous section 

on earth science data entry. The set of line segments enclosing a zone constitutes a record of the 

area boundary enclosing that particular zone. 

Each enclosed land-use zone on the map has a 

separate record. All the land-use records from this 

map (and other maps, if more than one is needed to 

cover the region) constitute a file, the land-use file 

for the region. The set of line segments in a record 

is ordered so that a zone is always defined by going 

around it in the same direction, clockwise or counterclockwise. Each record contains other information regarding the zone-at least, a symbol representing its land-use category. For example, the 

zones numbered 1 and 6 might both be agricultural. For such a map, there would be other polygon 

files, one for each class of areal data (for example, 

soils or bedrock geology). 

Two data files can be combined in the computer 

by an overlay process, which is analogous to overlaying two maps, both drawn on transparent 

material, so that the polygon boundaries on both 

maps may be seen and some particular combination of results studied. For example, a land-use 

map with four categories L1, L2, L3, and L4 and a 

FIGURE 5.-IBM optical scanner. 

10 
STATION SHEET 

Geol: Station Number 

Interstation Notes ( 6) 

Location: Traverse 

STRIJCnJRE 

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~ Strike/Azm Dip/Plunge 

~lolsl+llzl71 ll 12 13 iS 16 17 

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ILIBIIolslol~ 25 26 27 29 30 31 

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PIA RE CA r.7'f.ii1 r;-r;;1 Minor~AN.:DS Tat' ~ l.!..J& 

l

5

fl 5l 1131s~ll~l~l 

~h=Q Mineral Mineral 

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Heterogn(67) rHICK 60 61 62 63 

See notes(68) g ~ Mineralization(69>< J% IN .AN\ 8 

Q Q D 

75 

Incl. (shape: type:%) 

Adtl 

Num of 

Mat spec Coll (76) 

(77) ~ T f'?l ~ ~ 

7

2 K I 1 Photos ( 78) t..fi-J Sbeet num: file (79 ,80) 

AtL AJ.f S!NC£. J../f!"T ST/Jj J..oc, t:fA-88/l.tJIC i../ff'E~S1 ,tf()sr 

II FOL1 LENTIC1 CHiEF '1>1FJ= IS G~IIW SIZE, 

II¢+ PC XJ.. s To YA em, /{N YM ORJENT1 Citr l=cL, 

7tS"t71. Aat. F.IJF Air photoSE~ 1:<0-/J Pg • ..J.l.£.._ 

~~av:s~r 13Ef/M CIJNIIL /tREA 

FIGURE 6.-Station sheet for encoding field data to be keypunched into a computer. 

soil map of four types of soil 81, 82, 83, and 84 (fig. 

8) can be overlaid to show areas of soil type 81 and 

land-use category L1 (fig. 9A). All area within 

either 81 or L1 forms the boundary of their union 

(fig. 9B). All area common to both 81 and L1 is 

called their intersection (fig. 9C). This overlay process, determining the boundaries of a new set of 

polygons on the basis of unions or intersections, 

can be done in the computer. Any number of files 

can be combined by cascading the results, the third 

file being overlaid with the set resulting from overlaying the first and second files, and so on. 

GRID STRUCTURE 

A map of areal data having four categories A 

through D can be overlaid with a grid of northsouth and east-west lines, forming a uniform cell 

structure (fig. 10). Each cell has a record, and the 

attribute of the predominant zone within the cell is 

11 

assigned to the record for that cell. For example, 

the cell in row four, column one is B; the one in row 

two, column two is C. The cell structure need not be 

uniform. Quarter-quarter sections could be used, 

which are nonuniform in some places because of 

surveying inaccuracies. 

If the cell size is large relative to the region, the 

areal maps could be hand enc.oded. If, however, the 

cell size is small relative to the region, hand encoding may be impractical. The grid file can then be 

created by first creating a polygon file and then, in 

the computer using suitable software, overlaying 

the grid structure and recording the data in each 

cell. When map digitization of areal data is done 

using the technique of the Oak Ridge National 

Laboratory as described in a previous section, it is 

possible to write software to assign the data directly to cells without first digitizing the area 

boundary. 

If an areal unit is large relative to the cell size, 

many contiguous cells will have the same information, thus wasting file space if they are all stored 

(fig. 11 ). In such a situation a type of data compression can be used. Not all the cells overlaying the 

large polygon would be used. Only the first cell in 

each row wholly within the polygon could be recorded, with a notation as to the number of cells in 

that row within the polygon. Amidon and Akin 

(1971) discussed more sophisticated techniques of 

data compression. 

FIGURE 7 .-Sample map of study region containing eight 

digitized land-use zones. 
L1 

L2 

L4 

A 

S2 

S3 

S4 

B 

FIGURE 8.-A, Map ofland-use zones. B, Map of soil types. 

Thus far, each class of areal data has been considered as constituting a separate file. For the polygon area boundary file, there is one record for each 

polygon, one set of polygons for each file. For the 

grid structure, there is one record per cell, one set 

of cells (covering the region) for each file. However, 

with the grid structure, files of areal data can be 

combined by assigning all the data to a single file 

of cells, one record for each cell with each record 

~--\ ,----

1 \ I \ 

I \ I I 

I Ia--/ I r- I 1 I 

I I \ 

I I \ 

\ ,/ I \..- I 

I 

I 

I 

I 

I 

___ ..J I 

---.::.;::La-;:;-d-use category L 1 

A 

containing data on all classes of areal data for that C 

cell location. It is obvious that the more numerous 

the classes of data, the less frequent the opportunity for data compression, since it will be FIGuRE 9.-0verlay of soil type Sl and land-use category Ll. 

increasingly rare that all classes of data will have A, The overlay. B, The union. C, The intersection. 

12 
polygons of large area in the same geographical 

location. 

INCORPORATING LINE AND POINT DATA 

There are two kinds of line data, isopleths and 

networks. Isopleths are equal-value lines, such as 

elevation contour lines. In one sense isopleths are 

area boundary lines since they separate areas of 

higher from areas of lower value. To the extent 

that they are used as such they can be handled as 

described in the preceding paragraph. However, 

an isopleth has a definite value associated with it 

(for example, a 2,000-ft elevation contour line), 

whereas area boundary lines, in general, have 

none. 

Networks are lines representing areas, such as 

streets or streams, that are too narrow to be represented as areas on a map. In a polygon type file, 

networks can be stored as area boundaries are, but 

the definitions of a file and a record are much more 

arbitrary. The storage of network information in a 

grid basis is much more difficult. The simplest 

method would be a code indicating the presence or 

absence of a particular class of data in a cell. For 

example, the cell record could have a field for 

stream data in which a ~~1" would indicate the 

presence and a ~~2" the absence of a stream in that 

particular cell. If this information were inadequate, the cell record could be enlarged to include more detailed information. A separate file 

for that particular network could also be mains 

0 

a: 

COLUMN 

2 3 

A 

FIGURE 10.-Map with grid overlay. 

4 

13 

Columnj Columnj + n 

FIGURE 11.-Grid size small relative to polygon size. 

tained, organized as it would be in a polygon file, 

thus creating a hybrid data base. 

The inclusion of point data in a geographical 

information system data base is not difficult. Some 

information can be added to a cell record, and a 

separate file for each class of point data, referenced 

by coordinates, is easy to create, use, and maintain. 

DATA STRUCTURES FOR INFORMATION SYSTEMS 

Thus far, only those aspects of data base organization resulting from the geographical nature of 

data have been discussed. Other aspects of data 

base organization relating to computer information systems in general should be mentioned. For 

example, suppose that information on all land in 

agricultural use were to be retrieved. If the data 

structure were organized on a polygon basis, the 

system would retrieve only the land-use file. Then 

it would search sequentially through the records of 

each polygon for those coded, for example, ~~oz," 

for agricultural use; this could be quite time consuming. The retrieval time could be lessened considerably by inverting the file; an inverted file is a 

cross-indexed file in which a key word identifies a 

record. An index would list all polygons in the 

land-use file, grouped by land-use classification, 

with addresses of (pointers to) each polygon. Or, 

alternatively, an index table at the beginning of 

the land-use file would contain a list of pointers, 

one for each land-use classification. The pointers 

would indicate the first polygon of each land-use 
class, ((OZ" in this example. The first HOZ" polygon 

would contain a pointer indicating the location in 

the file of the second ((OZ" polygon, and so on. The 

file could also be inverted on other data items. An 

inverted file, or a similar technique is even more 

necessary for a cell-type data base structure since 

there will probably be many more cells in a region 

than there are polygons in any given file. 

It is not possible in this paper to give a discussion 

of data base organization. Dodd (1969) has written 

an excellent introductory, nontechnical article on 

data base organization. The design of a computer 

data base can be complex and costly and should be 

undertaken by experienced computer scientists. 

SOME SELECTED SYSTEMS 

Two bay region systems were used as study 

models in determining the characteristics of 

land-use information systems: (1) the system of the 

Metropolitan Transportation Commission (MTC) 

and the Association of Bay Area Governments 

(ABAG) and (2) the system of Santa Clara County 

(SCC). 

DATA BASE ORGANIZATION 

On a dia.:grammatic re_presentation of the computer information system of the MTC, the sections 

labeled ((Urban growth and location" and ((Transportation" represent modeling capabilities (fig. 

12). The part labeled ((Data base" consists of the 

system software and files of the four types of records: 

1. Transportation facilities-organized by networks 

2. Trip zones-organized by a table (290 x 290 

matrix) 

3. Records organized by map zones: 

a. One file for each class of information 

(dwelling unit, employment, population, and land use) 

b. A record for a particular file contains 

geographical information and class information. The geographical information is given first by county (1-9), then 

by map zone (1-290), then by tract 

(1-1040), with further refinement as 

necessary. 

4. Unique locators. These are special cases organized as in 3, above. 

Most of the data are from census tracts. The 1970 

census for the nine-county bay region is divided 

DATA BASE URBAN GROWTH AND LOCATION TRANSPORTATION 

SOFTWARE REGIONAL GROWTH TRAVEL DEMANDS 

•MADAM •FORTRAN Regional employment Person trips 

•DATADOX • TRANPLAN by analysis zone 

•SPAN EMPRO TRIP GENERATION 

INVENTORIES rLOCATION l Person trips • • Employment I I Basic employment between zones 

• Land use Land use FRATAR DISTRIBUTION 

• Census tract-street index 

I SHI FT-SHARES/BEMOD j_ • Highway facilities 

• Transit lines l Vehicle and transit trips 

between zones 

MODAL SPLIT SURVEYS Population 

j. • Home interview Dwelling units 

Origin -destination Population-serving External and through 

• Roadside and license plate employment vehicle trips Land use • Truck and taxi 

PLUM FRATAR DISTRIBUTION 

SPECIAL STUDIES l NETWORK 

• Population and housing 

ANALYSIS 1 • Employment trends Households by Highway link Transit link 

• Household income Income volumes volumes 

• Land constraints Structure type HIGHWAY TRANSIT • Unique locators INCMOD ASSIGNMENT ASSIGNMENT 

l 1 i 

FIGURE 12_-Bay Area Transportation Study Commission modeling system. 

14 
into 1,040 tracts grouped into 290 tract zones (fig. 

13). 

The computer information systems of Santa 

Clara County use three types of files: 

1. Property files (tax assessor) organized by a 

property location index. Each parcel of land 

has an assessor's parcel number and the coordinate of its estimated centroid (x, y) in the 

California state coordinate system. 

2. Census files (1970) organized as follows: First 

count-by census block group with (x, y) of 

centroid in the California state coordinate 

system. Second and fourth count-by census 

tract with (x, y) of centroid in latitude and 

FIGURE 13.-Tract zones for the nine-county San Francisco Bay region. 

15 
longitude. Third count-by census block. 

3. Files organized on the AC~DIME system. 

The AC~DIME system was developed by the 

U.S. Census Bureau for use in metropolitan areas 

as a computerized geographic system for the 1970 

census. ACG stands for address coding guide, 

DIME for dual independent map encoding. Each 

street intersection is a node, and each node is 

uniquely numbered. A street segment is that part of 

the street between nodes. To avoid unusually long 

or curved segments, nodes are created between 

street intersections where necessary. A DIME record is created for each street segment containing, 

among other data: the identifying nodes and, for 

each street, the address and the block and tract 

numbers to which that side of the street belongs. 

The (x, y) coordinate of the nodes can be added to 

the record. For more details the reader is referred 

to U.S. Bureau of the Census (1970). The census 

tract is one areal unit common to both the MTC/ 

ABAG and the SCC system. 

The Oak Ridge National Laboratory is developing a regional information system as a part of their 

environmental program, ORRMIS (Oak Ridge Regional Modeling Information System), sponsored 

by the National Science Foundation, Research 

Applied to National Needs Program. Its data base 

consists of nested cells referenced to a geodetic 

coordinate system (using latitude and longitude). 

The largest cell, 30 seconds latitude x 30 seconds 

longitude, is the reference cell. Nested within it 

are cells 15 seconds x 15 seconds, and within these 

are cells 3.75 seconds x 3.75 seconds. The size of 

these cells can be appreciated by comparing them 

with familiar units. At the latitude of the San 

Francisco Bay region, the reference cell is about 

165 acres (67 ha) and the 3.75-second cells are 

about one hectare (10,000 m2). Different data elements are assigned to the various cells depending 

upon resolution, accuracy, and type of the data. A 

slope map is not digitized, but elevations are stored 

on an approximately 10 x 10m grid, and slopes are 

computed as required. 

SOFTWARE CAPABILITY 

MTC uses a general-purpose software package 

developed by System Development Corporation for 

large-scale data management and analysis. It consists of several individual processor packages and 

is able to handle data retrieval, file manipulation 

and data reduction, matrix operation, statistical 

analysis, report generation, and graphic display. 

16 

One of the processors, ICORR (Internal Correspondence), is used for the point-in-polygon evaluation whereby entities with geographical coordinates (for example, street address associated with 

census data) are assigned to study areas such as 

census tracts. The MTC system is described in 

detail by Kevany (1968). 

The regional growth forecasts and land-use projection part of the MTC system uses five models 

(fig. 12): 

1. EMPRO-forecasts regional employment 

growth in industrial categories, given certain 

trend data and assumptions about the future. 

2. SHIFT-SHARES-allocates regional employment estimates in the basic employment classes to individual counties. 

3. BEMOD-allocates basic employment to 

analysis zones within each county. 

4. PLUM-(a) allocates residential population in 

households to analysis zones, relative to the 

location of employment and other factors, and 

(b) locates population-serving employment 

by zone, relative to the location of residential 

population, basic employment, and other factors. 

5. INCMOD-projects households into income 

categories, then into dwelling-unit structure 

classes. The output of these models is at the 

tract zone level. The transportation demand 

and network analysis models shown in figure 

13 (based on results of the region growth 

models listed above) also output at the tract 

zone level. These models are discussed by the 

Bay Area Transportation Study Commission 

(1969). 

Santa Clara County is currently developing an 

interactive facility (Cristiani and others, 1973). 

Geographically related data are referenced to 334 

traffic zones. These are related to census tracts so 

that each traffic zone is wholly within a census 

tract and each tract is made up of one or more 

traffic zones. Files can be displayed on a display 

tube in either map or tabular form. They can be 

manipulated by use of a light pen and the 

keyboard. Modeling is performed using a household and commercial model with a regional forecasting model (economic growth model, demographic model, industrial location model) as the 

driving function. The user can change data relating to a policy decision, rerun the model, and check 

results. Output from the model is stored as data for 

reruns at 5-year intervals. 
The Oak Ridge National Laboratory is engaged 

in a regional modeling effort that began in 1970 

(Oak Ridge National Laboratory, 1971; Meyers, 

1971). First, a county demonstration model was 

developed made up of a series of linked submodels-socioeconomic, land-use simulation, intrinsic land-use suitability, air diffusion, and soil 

loss prediction. This model is being used to study 

Campbell County, Tenn. A comprehensive regional model is being developed for a 16-county, 

6,500-mi2 (17,000 km2) region surrounding Knoxville, Tenn. The data base for this larger model is 

being constructed. 

UTILIZATION OF EARTH SCIENCE DATA 

NATURAL RESOURCE INFORMATION SYSTEMS 

A few geographic information systems include 

only natural resource and land-use data. One of 

the oldest is the Canadian Geographic Information 

System, a very ambitious system for all of Canada, 

which was started in 1963 and implemented 

1965--71. The data base, organized on the area 

boundary or polygon system, has files on present 

land use, capacity of the land for four categories 

(agriculture, forestry, recreation and wildlife), and 

watersheds, and has data manipulation 

capabilities to calculate areas and perform overlay 

operation. It operates on the batch mode, and experienced programmers are required (Boyle, 1972; 

Tomlinson, 1968). 

GRIDS (Guided Resource Inventory Data System) was developed by the Department of Natural , 

Resources of the State of Washington. The objective of the system is to maintain a current land 

inventory for use in making operational plans for 

land managed by the department. The data base is 

organized as a regular grid structure with a cell 

660 feet square (1 0 acres, Y4 of a 14 x 14 section; 4. 05 

ha). There is one record per cell with the following 

information: land cover, slope, aspect, elevation, 

soil, watershed, road access, land use, forest cover 

(tree type) and nonforest type (R. A. Harding, written commun., 1972). 

NARIS (Natural Resources Information System) was developed by the Center for Advanced 

Computation, University of Illinois at UrbanaChampaign; it covers parts of eight counties in 

northeast Illinois. Its data base is an irregular grid 

structure, each cell being a 14 x 14 mile section ( 40 

acres, 16 ha). To date, each tract (cell) contains 15 

classes of information under five major headings: 

17 

Geology: 

1. Interpretation of waste disposal 

2. Interpretation of construction 

3. Water resources 

4. Sand and gravel resources 

5. Surficial deposits 

Land use: 

6. U.S. Department of Housing and Urban 

Development codes 

7. Northeastern Illinois Planning Commission codes 

Forestry: 

Soil: 

8. Native woody vegetation 

9. Planted woody vegetation 

10. U.S. Dept. of Agriculture Soil Conservation Service soil characteristic 

Water: 

11. Watershed 

12. Wells 

13. Present impoundments 

14. Future impoundments 

15. Streams 

Each of these 15 classes is, in turn, made up of 

data elements. The values of these data elements 

are the attributes of each tract. For example, soil 

information is collected by plots of various size 

within each tract. Each plot is described by the 

following data elements: 

NUMBER-the type of soil in the plot; 

SLOPE-the slope of the soil in the plot; 

EROSION-the current erosion of the soil in the 

plot; 

ACRES-area of the plot in acres; 

OVERLAP-denotes whether or not the plot extends into an adjacent tract. 

The objective of the system is to provide natural 

resources information to public agencies and to the 

private sector. It can be user operated in a conversational mode with its own language (Center for 

Advanced Computation, 1972b). 

The Raytheon Company (1973) is currently developing a natural resources information system 

for the Department of the Interior (Bureau of Indian Affairs and Bureau of Land Management). It 

is to include natural resources and land-use information and will be used as an aid in the management of public lands. System specifications call for 

the ability to store and retrieve map data and for 

the capability of data manipulation such as overlaying maps to obtain unions or intersections of 

specified types of data and making area and 
perimeter calculations. The data base currently 

being developed is a variation of the area boundary 

structure. However, instead of storing complete 

polygons, the system stores only arcs, which are 

parts of an area boundary between two branch 

points. For example, polygon I consists of arcs ab, 

be, cd, de, and ea, and polygon II consists of de, cf, 

fg, and gd (fig. 14). An index table in the data base 

gives (for each file) the arcs making up each polygon. When maps (files) are overlaid, new arcs are 

created whenever an arc from one file crosses an 

arc of another file. Also new polygons are created, 

and a new table of polygon arcs is written. It remains to be seen how effective this data base organization is. 

Boyle (1972) and Center for Advanced Computation (1972a) give additional information on other 

natural resources information systems. 

PROGRAM INVENTORY AND GEOLOGIC MODELING 

Earth science information in its most commonly 

available form consists of maps and tables of physical, chemical and engineering properties, which 

are the basic data on page 4. They are developed 

from raw data (field observations and actual measurements of various physical and chemical properties) with the benefit of scientific interpretation. 

These basic data are still very technical and generally require further interpretation by scientists 

or engineers to be useful to the planner. This interpretation of basic data has been one of the main 

features of the San Francisco Bay Region Study 

Program. The inventory of these studies listed on 

page 4 is now presented in a slightly different 

form (table 2). The left-hand column contains basic 

data. Most of these data are generated routinely by 

the U.S. Geological Survey, although not uniformly for all areas of the country. The right-hand 

column lists results of the interpretive studies. 

The center column lists the basic data that would, 

ideally, be used in the interpretive study on the 

same row to the right. In some instances all of the 

basic data so listed is used. In others, only part of it 

is used, depending on the area being considered, 

the availability of the basic data for that area, and 

the current methods involved in the particular 

study. Most of the items in the right-hand column 

are generally not developed routinely by the survey but are the outgrowth of special requirements 

developed on the San Francisco Bay Region Study 

Program. The data in the right-hand column plus 

some items in the left-hand column can be used for 

18 

FIGURE 14.-Polygons made up of arcs. 

the evaluation of land-use plans, especially to 

avoid hazards. 

The development of most interpretive data from 

basic data is, in a sense, a form of mathematical 

modeling or simulation. Some of the interpretive 

studies consist mainly of overlaying basic data and 

interpreting the overlays (McHarg, 1969), which 

can be performed in the computer. To the extent 

that interpretation of the overlays can be reduced 

to a table look-up procedure or a mathematical 

formula, this can also be performed in the 

computer. 

On the landslide susceptibility map prepared for 

TABLE 2.-Program inventory 

Basic data Data used in interpretive studies Interpretive studies 

1. Bedrock________________ 1, 3, 4, 6, 15, 

17, 18, 19 A. Landslide susceptibility. 

2. Unconsolidated deposits 1, 2, 4, 5, 7, 8, B. Land use implications12, 19, I region of bay mud. 3. Landslides _____________ _ 

4. Active faults_________ __ 1-6, 9, 11-15, 

17-20, l C. Coastal geologic processes. 

5. Topographic map _______ _ 6. Slope map______________ 2, 4, 5, 7-10, 

12, 14, 17, 

18, 19, I 

D. Physical properties of uncon- solidated deposits and land-use implications. 7. Bay mud _____________ _ 

8. Land subsidence _______ _ 

9. Ground-water-----------

10. (a) Stream networks (b) rating tables. 

11. Water bodies _________ _ 

1-4, 6, 11, E. Hillside materials-inferred 12, 17 engineering behavior and land-use implications. 

5, 10, 14, 15, 19 F. Flood-prone areas and land-use implications 

(boundary of 100-year flood). 

12. Earthquake locations____ 5, 10, 14, 15 G. Urban drainage system 

(technical report). 13. Mineral commodities ___ _ 14. Historic flood data ______ 1, 6, 9, 11, 15, H. Land pollution 

15. Rainfall (isohyetal map) 

16, 17, 19, A, F susceptibility. 

16. Evaporation data map __ 1-4, 7, 12 I. Seismic response map. 17. Soils _________________ _ 

18. Vegetation ____________ 1-5, 9, 10, 11, J. Ground water studies. 

19. Land use _____________ _ 

20. Water quality and suspended sediment. 

15, 16, 18, 19 

1, 2, 3, 5, 6, 10, K. Erosion and 11, 14, 15 sediment studies. 17-20 
San Mateo County, only three basic data items are 

used: bedrock, landslides, and slope categories. 

Seven categories of susceptibility to landslides are 

defined ranging from lowest to highest, according 

to the following list (Brabb and others, 1972): 

I. Areas least susceptible to landsliding. Very 

few small landslides have formed in these 

areas. Formation of large landslides is possible but unlikely, except during earthquakes. Slopes generally less than 15 percent but may include small areas of steep 

slopes that could have higher susceptibility. 

Includes some areas with 30 percent to more 

than 70 percent slopes that seem to be underlain by stable rock units. 

II. Low susceptibility to landsliding. Several 

small landslides have formed in these areas, 

and some of these have caused extensive 

damage to homes and roads. A few large 

landslides may occur. Slopes vary from 5 to 

5% percent for unstable rock units, to more 

than 70 percent for rock units that seem to 

be stable. 

III. Moderate susceptibility to landsliding. Many 

small landslides have formed in these areas, 

and several of these have caused extensive 

damage to homes and roads. Some large 

landslides likely. Slopes generally greater 

than 30 percent but includes some 15--30 

percent slopes in areas underlain by unstable rock units. 

IV. Moderately high susceptibility to landsliding. 

Slopes all greater than 30 percent. Several 

large landslides likely. These areas are 

mostly in undeveloped parts of the county. 

V. High susceptibility to landsliding. Slopes all 

greater than 30 percent. Many large and 

small landslides may form. These are mostly 

in undeveloped parts of the country. 

VI. Very high susceptibility to landsliding. Slopes 

all greater than 30 percent. Development of 

many large and small landslides is likely. 

These areas are mainly in undeveloped 

parts of the county. 

L. Highest susceptibility to landsliding. Consists 

of landslide and possible landslide deposits. 

No small deposits are shown. Some of these 

areas may be relatively stable and suitable 

for development, whereas others are active 

and causing damage to roads, houses, and 

other cultural features. 

In developing the susceptibility map, the map of 

landslide deposits is first overlaid on the map of 

bedrock geology. Those areas with landslide deposits are zones in category HL." Next, the result is 

overlaid with a slope map of six slope categories. 

Those areas not categorized as zone ttL" are 

categorized according to table 3 (from Brabb and 

others, 1972). 

If it were possible to generate the results of all 

the interpretive studies by simulation or modeling, it would be feasible to have the computer data 

bank consist mainly of basic data. Interpretations 

could be generated as required. This approach has 

several advantages. Owing to the pressure of urbanization and its impact on the environments, 

interest in the type of data generated by the interpretive studies will grow, fostering the development of better geologic models so that a data 

base of interpretive data will probably have to be 

updated frequently. However, the basic data are 

relatively stable, requiring much less updating. 

As areas of application grow, the amount of data 

generated by models will probably result in a 

greatly enlarged data base if it consists of interpretive data. Finally, there is a great advantage if, 

instead of a digitized slope map, the data bank 

contains elevations at grid intersections of suitable size so that slopes can be computed as needed. 

Then models requiring slope information as an 

input are not locked into preselected slope 

categories. 

EARTH SCIENCE DATA FOR INFORMATION AND 

PREDICTIVE LAND USE 

Large-scale regional land-use models are developing a need for earth science data owing to the 

growing requirements to study the environmental 

impact of various land-use alternatives. The Oak 

Ridge National Laboratory, in developing the 

models for their environmental program, are providing for this need by including the necessary 

data, mostly assigned to the level of the 30 x 30 

second cell. The data include geology (bedrock and 

surficial, mineral deposits, aquifers), surface water information, and soil types. Tables of physical 

and chemical properties are included in the data 

base (C.R. Meyers, Jr., oral commun., 1972.) The 

Center for Advanced Computation of the University of Illinois included the following data in the 

data base of the recommended Illinois Resources 

Information System Center for Advanced Computation, 1972a): 

19 
Geology 

Surface deposits ......................... Digitized area. 

Oil, gas, and water wells ................. Point. 

Oil and gas fields and storage . . . . . . . . . . . . . Section. 

Oil and gas statistics ..................... Township. 

Coal statistics ........................... Point. 

Soil 

Soil associations and interpretations . . . . . . Section. 

Soil types ............................... County. 

Soil productivity ......................... County. 

Soil and water ........................... County. 

Conservation needs . . . . . . . . . . . . . . . . . . . . . . Watershed. 

Land use 

Aerial photography ...................... 1:24,000. 

Land-use interpretation .................. Vi x Vi section. 

Water 

Stream network digitization .............. Network. 

Water quality standards .................. Network. 

Pollutant emissions ...................... Point. 

Surface water quality . . . . . . . . . . . . . . . . . . . . Point. 

Ground water quality .................... Point. 

Public water-supply quality .............. Point. 

Flood plans .............................. Digitized area. 

Flow duration and low flow frequencies .... Point. 

Time of travel ........................... Network. 

Public water-supply adequacy ............ County, 

municipality. 

EVALUATION AND RECOMMENDATIONS 

The need for user participation in the design of a 

computer information system is well recognized. 

In the design of an urban and regional land-use 

information system, participation of users must be 

preceded by identification of the using community, 

which may include not only various government 

bodies but also the private sector. The larger the 

region, the more varied the potential users of the 

system. Totschek, Almendinger, Kevany, and 

Needham (1970) discussed the need for a common 

information center for the nine-county bay region, 

its relation to the various components of the user 

community, and a recognition of the differing data 

requirements of the user. However, the report is 

concerned only with socioeconomic information 

and does not consider earth science data. The 

Center for Advanced Computation of the University of Illinois (1972a) recommends a statewide 

information system. A survey of user needs indicated five potential user groups: (1) social service 

agencies, (2) the economic and industrial sector, 

(3) the physical services sector (for example, 

transportation), (4) the natural resources sector, 

and (5) agricultural agencies. 

20 

Few land-use decisionmakers have much knowledge of earth science or the role it should play in 

land-use planning; many have little knowledge of 

computer technology. Therefore, in the design of a 

specific system, after the user community has been 

identified, considerable interchange of know ledge 

may be necessary between users, computer scientists, and specialists in the field of earth science 

technology, primarily geologists and civil engineers. Any design effort that bypasses or 

weakens this step may waste much time and 

money. 

An urban and regional land-use information 

system with a large and diverse user community 

will require a wide range of capabilities. The main 

characteristics of such a system are: 

1. Interactive and batch mode capability-Many of 

the demands on the system can be met by 

using programs run in the batch mode, such 

as routine reports and simulation studies 

where no outside decisions are required. 

However, the ability to interact with the system so as to make decisions during a simulation program enables planners to get a better 

feel for the results of various decisions. This is 

especially true where geographical data can 

be shown on a display tube in its spatially 

distributed form. If a storage-type display 

tube is used, overlays can be performed on the 

screen of the tube without the necessity of 

performing the calculations in the computer. 

It should be mentioned that with an interactive system, special user languages should be 

available so that the user will not require a 

programmer in order to communicate with 

the system. 

2. Large data manipulation and computational 

capabilities-Large-scale urban and regional 

information systems will be required to do 

much more than provide fast retrieval of 

large amounts of data for printout or display. 

The system must also be capable of sorting 

and recombining the data in various ways for 

analysis and reporting. Closely allied with 

this is the requirement for statistical 

analysis. Finally, a capacity to do large-scale 

simulation and modeling is necessary. 

3. Multiple-type input and output devices- Programs run in batch mode imply a hard copy 

output on a printer or a plotter or both. Work 

in the interactive mode implies the use of 
TABLE 3.-Landslide failure record for rock units in San Mateo County 

[Modified from Brabb and others, 1972] 

Rock unit on 1~eologic map in order of increasing 

proportion of surface having failed by landsliding 

Map 

symbol 

Approx. 

area in county 

(mi2 1 

Approx. 

area 

that has failed 

(mi2 1 

Relative 

susceptibility 

numbers 

Susceptibility numbers in each slope interval 

5-15° 15-30° 30-50° 50-70° >70° 

(No data for surficial deposits, undivided, 

Qu; alluvium, Qal; San Francisco Bay mud, 

Qm; windblown sand, Qd; beach deposits, Qb; artificial fill, Qaf; terrace deposits, Qt; 

Page Mill Basalt, Tpm; unnamed volcanic rocks, KJv; marble, m; shale near Palo Alto, 

Ksh; conglomerate, fcg; or metamorphic rocks, 

fm, but ext€·nt of landsliding probably small) 

Limestone-------------------------------------.. ------_ fl Colma Formation ________ ____ __________ ____________ __ Qc 

Sandstone at San Bruno Mountain ____________________ KJs 

Butano(?l Sandstone __ -------------------------------- Tb? Unnamed sandstone ---------------------------------- Tus Granitic rocks__________________________ _____________ Kgr 

Serpentine -------------------------------------- ____ sp Sandstone of Franciscan assemblage ________________ __ fs 

Slope wash and ravine fill ------------------- _____ ____ Qsr Greenstone of Franciscan assemblage __________________ fg Chert of Franciscan assemblage________________________ fc Lompico Sandstone_____________________________________ Tlo 

Sheared rocks of Franciscan assemblage________________ fsr 

Pigeon Point Formation ------------------------------ Kpp San Lorenzo Formation, undivided _____________________ .. Tsl Merced Formation _ _________ ___________________________ QTm 

Sandstone, shale and conglomerate ____________________ Tss Butano Sandstone, Sky Londa area ____________________ Tb 

Santa Clara Formation _______________________________ _ 

Rices Mudstone Member of San Lorenzo Formation _____ _ Vaqueros Sandstone _________________________________ _ 

Monterey Shale ___ ---------------------------- ______ _ Purisima Formation, undivided _______________________ _ Lambert Shale _______________________________________ _ 

Mindego Basalt and other volcanic rocks _____________ _ Butano Sandstone along Butano Ridge _______________ _ Santa Cruz Mudstone _______________________________ _ 

QTs 

Tsr 

Tvq 

Tm 

~fa Tmb 

Tb 

Tsc 

San Gregorio Sandstone Member of Purisima Formation Tpsg Tunitas Sandstone Member ofPurisima Formation ______ Tptu Tahana Member of Purisima Formation________________ Tpt 

Pomponio Member ofPurisima Formation ______________ Tpp 

Two bar Shale Member of San Lorenzo Formation ___ ___ Tst 

Santa Margarita Sandstone____________________________ Tsm 

San Lorenzo ]["ormation and Lambert Shale, undivided __ Tls Lobitos Mudstone Member of Purisima _________________ Tpl 

Landslide deposits ------------------------------------ Qls 

.30 11.11 

4.77 

10.56 

1.81 

24.61 

4.76 

22.19 

4.51 

11.70 

1.43 

.40 

9.99 

7.77 

1.00 

7.91 

3.34 

22.55 

6.67 

1.37 

7.60 

5.11 

23.06 

19.95 

10.80 

20.18 

19.25 

2.41 

2.76 

33.46 

11.97 .80 

.65 

6.83 

3.71 

83.88 

either a keyboard, both for data entry and 

system replies, or possibly a display tube for 

output. Data and program input may be by 

keyboard, the conventional keypunch and 

card reader, or optical scanners. 

4. Support of many terminals-Terminals for both 

updating the data bank and obtaining system 

output may be spread out within the region. 

This does not necessarily imply time-sharing, 

although that may be most efficient. 

5. Support of multiple data bases-Some data 

bases will be .geographical in nature, others 

wiH have no spatial distribution. Among 

those with spatial distribution, some may be 

organized on a node and segment basis, such 

as an ACG-DIME census file or a stream 

network, while areal data may exist in a file 

of cells. Software must provide for handling 

.00 

.01 

.10 

.19 

.04 

.90 

.09 

.74 

.18 

.61 

.10 

.03 

.83 

.84 

.11 

1.01 

.51 

4.33 

1.85 

.43 

2.41 

1.76 

7.81 

7.25 

4.01 

7.66 

7.98 

1.06 

1.24 

16.08 

5.76 

.42 

.41 

4.56 

2.57 

83.88 

II 

III 

IV 

v 

VI 

L L 

I 

I 

I 

I 

II 

I 

I 

I 

I 

I 

I 

II 

I 

I 

I 

I 

II 

L 

I 

II 

I 

I 

I 

I 

I 

I 

I 

I 

II 

II 

I 

II 

I 

II 

III 

II 

II 

II 

III 

II 

II 

II 

I 

I 

III 

III 

II 

II 

I 

III 

II 

L 

I 

II 

I 

I 

II 

II 

II 

II 

II 

I 

III 

II 

II 

II 

I 

III 

IV 

III 

III 

IV 

IV 

III 

III 

III 

III 

IV 

v 

v 

v 

III 

III 

v 

VI 

L 

I 

II 

I 

I 

II 

II 

II 

II 

II 

I 

III 

III 

II 

III 

II 

III 

IV 

IV 

III 

IV 

IV 

III 

III 

IV 

IV 

v 

v 

v 

v 

IV 

III 

VI 

VI 

L 

and combining all of these data bases. 

II 

II 

II 

II 

II 

II 

II 

II 

II 

II 

III 

III 

III 

III 

III 

III 

IV 

IV 

IV 

IV 

IV 

IV 

IV 

IV 

IV 

v 

v 

v 

v 

v 

VI 

VI 

VI 

L 

Special user languages were mentioned above. 

An example is included at this point for the benefit 

of those who have not used one. The language is 

DIRAC, described by Askevold and Vallee (1973). 

A file of all mineral deposits in Montana has been 

restricted to a subset of 25 properties. This subset 

is interrogated by a series of commands to find 

properties that satisfy the following criteria: (1) 

contains zinc, (2) contains silver, (3) occurs in an 

igneous or igneous-metamorphic area, (4) contains 

gold, (5) contains copper, (6) occurs along east-west 

structures, and (7) located in Jefferson County. 

Figure 15 shows how the interrogation would appear either typed on teletypewriter or shown on a 

display tube. Another excellent example is given 

by the Center for Advanced Computation (1972b). 

A particular system will have a mixture of 

21 
query 

25 ENTRIES 

? find ELEM Zn! 

18 ENTRIES 

? hold 1 

? file 0 

25 ENTRIES 

? find ELEM Ag! 

22 ENTRIES 

? hold 2 

? file 0 

25 ENTRIES 

? find ROCK IG 

15 ENTRIES 

? or ROCK IM 

18 ENTRIES 

? and ELEM Au! 

18 ENTRIES 

? and ELEM Cu! 

16 ENTRIES 

? and GEOL EW 

2 ENTRIES 

? and LOCA JF 

1 ENTRIES 

? type 

EW Carbonate Chief. Alhambra-Warm Springs. Jefferson County 

<Sec. 29, T. 8 N., R. 2 W.> 

Au 8,055 oz. 

Ag 66,037 oz. 

Cu 76,364 lb. 

Pb 403,584 lb. 

Ore minerals: galena, chalcopyrite, arsenopyrite, pyrite 

Gangue: quartz 

Host rock: quartz monzonite 

Alteration: 

? file 0 

25 ENTRIES 

? 

FlGURE 15.-Example of an interactive language (part 1). A series of commands and responses 

in which two subfiles (1 and 2) are retained for later use (see fig. 16) and data are typed out for 

one property that satisfies seven criteria specified through a series of logical commands 

(actual terminal listing) (G. V. Askevold and J. F. Vallee, 1971, unpub. data). 

capabilities determined by its own user community under the influence of the natural environment 

of the region. When the recommended system for a 

region is too expensive for immediate consideration, which is most likely, it should be designed to 

evolve in a planned fashion. Boyle and Sharp 

(1972) give three methods of developing equipment systems for spatial data processing, which 

22 

are similar to regional information systems: 

1. Use a large computer on a service basis with 

normal 1/0 equipment. 

2. Purchase low-cost equipment as an interim 

measure and add to or replace with a larger 

system later. 

3. Purchase expensive high-quality equipment at 

the start. 
file 1 

18 ENTRIES 

? corr DIST DIST 

? draw 0 

DIST 

--- * 

3 16.6 WINS ******** 

2 11.1 CAMO ***** 

1 5.5 COCL ** 

1 5.5 NIMI ** 

1 5.5 CURL ** 

2 11.1 ALHA ***** 

2 11.1 AMAZ ***** 

6 33.3 BASI **lj(************* 

% 

% 

% 

% 

% 

% 

% 

% 

% 

? file 2 

22 ENTRIES 

? corr ROCK ELEM 

? type 

ROCK ELEM 

Au Ag Pb Zn Cu Fe 

5 4.5% 0 1 1 1 1 1 0 

70 63.6% IG 3 14 14 14 11 14 0 

10 9.1% SD 1 1 2 2 2 2 0 

15 13.6% IM 3 3 3 2 2 2 0 

5 4.5% IS 2 0 1 0 0 1 1 

5 4.5% ex 0 1 1 1 1 1 0 

TOTAL= 110. 9 20 22 20 17 21 1 

8.2 18.2 20.0 18.2 15.5 19.1 0.9 

? 

FIGURE 16.-Example of an interactive language (part 2). (Top) A histogram showing the distribution of zinc-producing 

mines (file 1) in the various mining districts. (Below) A correlation matrix that relates elements to host rock type for 

the silver-producing properties (file 2) (G. V. Askevold and J. F. Vallee, 1971, unpub. data). 

The rapid advances in hardware technology are 

leading to rapid declines in the cost of certain 

hardware items and to rapid changes in ideas of 

urban and regional system design (table 1). The 

development of low-cost disc storage devices, with 

more dramatic developments promised for the future, are radically changing our concepts of what 

constitutes a large data bank for practical use. 

More recently, the development of the minicomputer is making possible the design of many systems 

requiring a small, inexpensive computer. A 

minicon1puter can be used with several image digitizers and display tubes for online editing of the 

23 

map digitization process. When the amount of calculation is small, a minicomputer with a storage 

display tube can form a small system for display 

and analysis of geographical data, including overlaying of areal data files. 

While these developments make large-scale urban and regional information systems more 

economically feasible than was imagined 10 years 

ago, they also make if difficult to estimate costs for 

a system unless it is currently under development. 

Also, a cost estimate cannot be made in general, 

but only for a specific system. The Canadian Geographic Information System cost over $3,000,000 
with data acquisition costing over $20,000,000 

(Center for Advanced Computation, 1972a). Cost 

of the IRIS system is estimated at $4,000,000 for 

the computer system and $1,500,000 for the initial 

data base. 

The original cost of the data base may be very 

great. Obviously, the greater the detail, the greater the costs. Taylor (1971) distinguishes four 

types of planning as regional, monitor, community, and ordinance administration and estimates 

their data needs as follows: 

Characteristic Region Monitor and Community Ordinance 

early warning administration 

Study area size ____ Total region ___ Total region ___ 100 mi2 1,000 acres. 

(64,000 acres! 

Largest subarea ___ 200 acres _____ 200 acres ____ .1 acre _________ Parcel. 

Preferred subarea __ 40 acres _____ 40 acres _____ Subparcel ______ Subparcel. 

Typical activity 2 digits _____ 2 digits _____ 3 digits ______ _4 digits. 

code levels. 

Updating _________ Annual _______ Monthly ______ Monthly _______ Weekly. 

He then relates accuracy requirements to these 

systems (fig. 17). The cost of establishing and 

maintaining a computer data base to support a 

geographical information system is not linearly 

related to accuracy but rather rises very rapidly as 

accuracy approaches 100 percent. 

DATA BASE ORGANIZATION 

One of the major problems in the design of a 

geographical information system or an urban and 

regional information system is the design of the 

data base, especially with regard to area type data. 

The two main types are the polygon and the cell, 

both previously described. The polygon type has 

the advantage of storing the boundaries of an area 

to within system tolerance. This may be useful 

where boundaries are precisely known, such as 

political or land ownership boundaries. It may be 

unnecessary where the boundaries are not precisely known, such as in many bedrock formations. 

90 95 100 

ACCURACY, IN PERCENT 

FIGURE 17.-Error tolerance by type of planning (from Taylor, 

1971). 

If overlaying polygons must be done in the computer, it can be very expensive. The polygon type, 

therefore, is not conducive to mathematical modeling where a large amount of overlaying is required. The properties ofthe cell structure are just 

the reverse. The overlaying has already been accomplished in establishing the data base. The 

main drawback of the cell type is its imprecise 

representation of areal boundaries. 

At present, a cell structure is recommended for 

earth science areal type data for a region such as 

the San Francisco Bay region, especially if a large 

amount of simulation and mathematical modeling 

is to be done. Imprecise representation of the 

boundary is not a serious problem for these purposes. A cell size of about 150 x 150 m (or its 

equivalent in geodetic cell size) is required for 

some geologic areal data; for the remainder, a 300 

x 300m cell is satisfactory. Although it may be 

advisable to include line and point information 

with the cell record, it probably will be necessary 

to have separate network files of some line information for modeling purposes. 

The handling of slope information depends on its 

use and on the terrain of the region. In relatively 

flat country, a slope map of about five zones may be 

amenable to digitization, since it is no more complex than many land-use maps. However, a similar 

slope map for mountainous terrain can be so complex as to be virtually impossible to digitize with 

an image digitizer or a line follower. For example, 

the slope map of the Mount Sizer area, near San 

Jose in the San Francisco Bay region, has six 

zones: 0--5, 5-15, 15-30, 30--50, 50--70, and over 70 

percent (fig. 18). If such a slope map must be 

digitized, it might best be done using a scanner for 

the intermediate slope map products, the so-called 

((choke negatives" used in making the map. The 

first choke negative has only zones with slope over 

70 percent and would be digitized giving the sixth 

zone. The second choke negative has zones with 

slope over 50 percent and would be digitized. Suitable software would allow the results of the first 

digitization to be subtracted from the results of the 

second (in the computer) yielding zones with slope 

between 50 and 70 percent. The process could be 

continued until the six slope zones were digitized. 

If slopes are not to be used in modeling, this process 

seems an unwarranted expense. If slopes are to be 

used for modeling, reliance on a slope map restricts 

the user to the slope categories of the map. For 

24 
0 1 M ILE 

0 .5 1 KILOMETRE 

PERCENT OF SLOPE 

30-50 

This slope information was developed at I :24,000 scale using the contour 

co lor-separation plate for the 7.5 - minute quadrangle map covering the area. 

Each slope category is the result of photomechanical manipulation of the contour 

lines. The slope zones have been edited to remove the effects of contour values, 

elevation figures, and nonfactual images produced by the process. 

FIGURE 18.-Partofslope mapofMount Sizer quadrangle (U.S. Geological Survey, 1970). 

25 
these reasons, it is recommended that, in mountainous areas such as in the San Francisco Bay 

region, elevation on a small grid rather than a 

slope map be digitized. 

The compatibility of earth science data with 

other geographically distributed data depends on 

use of the data. An example is census data, generally organized on the ACG-DIME system or assigned to census tracts. Some systems include or 

provide for a limited amount of land-use information and transportation networks. The census 

tracts are small in urban areas and large in rural 

areas. However, much of the planning- hence 

much of the modeling-will be concerned with 

areas not presently urbanized. Census information should be assigned to cells in rural areas, cells 

much smaller than the census tracts and whose 

boundaries coincide with boundaries of square 

groups of earth science data cells as in the ORNL 

organization. 

Ifthere is much demand for the use of an interactive system, for example, using a storage display 

tube and not requiring much computation in the 

computer, it may be cheaper to maintain a separate set of polygon files, in addition to the cell file, 

for just such applications. Depending on the 

method of digitization, these files may be archival 

files derived from digitization of maps and used in 

the generation of the cell file . 

Finally, the data should be organized on the 

basis of geodetic coordinates, and the metric system should be used for linear measure, since national adoption of the metric system is near. The 

difficulty of mathematical modeling across zones 

of a projective system, such as the Universal 

Transverse Mercator (UTM) (fig. 19), is largely 

removed by the use of geodetic coordinates. Although cell size becomes smaller toward the north, 

the variation is small from one row to the next and 

is easily handled in a mathematical model. Finally, maps can be plotted in any of the commonly 

used projections with existing software routines. 

MAP DIGITIZATION 

The cost of digitizing the earth science data generated by the San Francisco Bay Region Program 

has been estimated. As a preliminary step, the 

amount of data to be digitized was estimated. From 

126° 120° 114° 108° 102° 96° 90° 84° 78° 72° 66° 

FIGURE 19.-Map zones for the Universal Transverse Mercator projection in the United States. 

26 
this, an estimate of the time of digitization was 

made, and the results then converted to cost. Such 

estimates are difficult to make and not very reliable except for very simple maps. Many companies 

in the map digitizing business will only bid on a 

((cost per point" basis. This particular estimate was 

made more difficult for two additional reasons. The 

region covered is very large so that for each data 

element, such as bedrock geology, the estimate for 

the region is based on an extrapolation of measurements made for a small part of the region taken 

as representative or average. This may lead to 

errors if the part selected is not truly average. 

Second, an average rate of digitization was used. 

Actually, the rate will vary considerably from map 

to map. 

The estimate provides for digitization of elevation contours and not slope maps. It is assumed 

that the elevations will be gridded subsequently 

and stored in the data bank, with slopes being 

computered as needed. 

The estimate is based on the use of manual image digitizers. Most of the maps are too complex to 

consider use of automatic line followers. No estimate of the cost of digitization by the use of scanners is included because (1) scanners used for map 

digitization are still in the research and development stage, (2) software development is incomplete, and (3) the amount and kind ofpreediting or 

preparation of special manuscripts for scanning 

depend on the scanner and the data being scanned 

and are, at present, difficult to estimate. 

The following table is based on extrapolation of 

measurements made on small parts of the bay 

region: 

Total line length 

Data type Data element Map scale (inches) 

Basic data. Bedrock____________________ 1:62,500 39,500 (plus 33,500 

Interpretive. 

Surficial deposits____________ 1:62,500 

Landslides ________ -··-_______ 1:62,500 

____ .do ______________________ 1:24,000 

Active faults________________ 1:250,000 

Annual rainfall ____________ 1:500,000 

Soil------------------------ 1:62,500 

Vegetation _ _______________ 1:62,500 

Land use __________________ 1:62,500 

Landslide susceptibility ____ 1:62,500 

Bay mud __________________ 1:125,000 

Land-use categories ________ 1:62,500 

Flood inundation maps______ 1:24,000 

Aquifer yields ______________ 1:125,000 

Hillside materials 

and inferred 

16,000 

8,500 

145,000 

200 

1,000 

38,000 

82,000 

97,000 

155,000 

500 

500 

13,700 

2,500 

symbols) 

engineering behavior. 1:62,500 39,500 (identical to 

bedrock) 

Data are not yet available for other program elements that may be digitized. 

Total line length is now summed to give two 

grand totals, one for a data base of basic data and 

one for a data base for interpretive data. The total 

for interpretive data also includes active faults 

and land use since these data elements will probably be required in both sets. 

Total line length (inches) ___ _ 

Time to digitize (hours)_ ____ _ 

Basic data 

427,000 

4,270 

Interpretive data 

309,000 

3,090 

The time is based on a digitization rate of 100 

inches per hour, a conservative estimate based on 

the experience of the U.S. Geological Survey in 

digitizing elevation contours from topographic 

maps. Their experience also calls for allowing 

about 40 percent of total time for editing, which 

means that the time shown above is 60 percent of 

total time. Therefore, total time including allowance for editing is: 

Basic data Interpretive data 

Total time (hours) __________ 7,150 5,150 

27 

To these totals should be added the time to digitize elevation contours, 25,000 hours. This estimate (including allowance for editing) is based on 

estimates furnished by the U.S. Geological Survey. With elevation added, the totals are: 

Basic data Interpretive data 

With elevations Without elevations 

Total hours ______ 32,150 30,150 5,150 

Converting this to cost depends on local conditions. Taking a cost factor that should include 

operator salary, cost of supervision, and amortization of equipment, the following costs are obtained 

for a range of cost factors: 

Cost factor Basic data Interpretive data 

With elevations Without elevations 

$10/hr $321,500 $301,500 $ 51,500 

15/hr 482,250 482,250 77,250 

20/hr 643,000 603,000 $103,000 

SUMMARY 

The focus of this study is urban and regional 

information systems that must handle relatively 

large (multicounty) areas including urbanized 

parts that are expanding. The main concern has 

been with the incorporation of earth science data, 

whose main attribute, from the point of view of a 

computer system designer, is its spatial quality. 

This attribute must be preserved in the computer 

system. 
The design of such an information system should 

recognize the following factors: 

1. System specifications should be developed considering the requirements of the user community. The users will need the aid of 

geologists, hydrologists, and engineers regarding the use of earth science data for their 

particular region. 

2. A system can have a wide range of capabilities. 

Some of the more important ones are: 

a. Interactive and batch mode capability. 

b. Large data manipulation and computational capability. 

c. Multiple type input and output devices. 

d. Multiterminal support. 

e. Support of multiple data bases. 

The extent to which a particular system incorporates these will depend on regional needs. 

3. The initial cost of the data base will be high. 

The greater the accuracy, the higher the cost, 

in a rapidly increasing, nonlinear fashion. 

When considering specifically an information 

system for the San Francisco Bay region, it may 

not be necessary to build from scratch but only to 

modify the system of the MTC to make it truly 

regional. It must be decided whether to incorporate earth science data as interpretive data or as 

basic data plus the necessary software to develop 

interpretations as required. 

Decisions must be made on two additional items. 

The data base may be organized on a cell basis or as 

an area boundary file. At present a cell-organized 

data base seems best if there will be much modeling and simulation. The area boundary file is best 

for a simple storage and retrieval system. If overlaying of files is required, it may prove very costly 

with an area boundary file, unless the Raytheon 

arc approach is a significant improvement. 

Second, the operation of map digitization should 

consider the hardware used, map standards, and 

editing procedures together as a whole. Use of an 

image digitizer is probably most economical at 

present. Scanning devices may be. better for very 

simple maps. However, for scanning, the production of overlays from complex maps may not be 

much different than digitizing with an image digitizer. For the San Francisco Bay region in particular, (1) a cell-type data base is recommended for 

the earth science data, (2) the slope maps for the 

area should not be digitized, and (3) the cost to 

digitize the earth science data can be only roughly 

estimated. 

28 

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