Wood and Halsing—Opportunities and Needs for Mobile-Computing Technology to Support U.S. Geological Survey Fieldwork—U.S. Geological Survey Circular 1299

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Opportunities and Needs for Mobile-Computing 

Technology to Support U.S. Geological Survey 

Fieldwork

By Nathan J. Wood and David L. Halsing

Circular 1299

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

Dirk Kempthorne, Secretary

U.S. Geological Survey

Mark D. Myers, Director

U.S. Geological Survey, Reston, Virginia, 2006

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Suggested citation:

Wood, Nathan J., and Halsing, David L., 2006, Opportunities and needs for mobile computing technology to support 

U.S. Geological Survey fieldwork: Reston, Virginia, U.S. Geological Survey Circular 1299, 24 p.

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Produced in the Western Region, Menlo Park, California

Manuscript approved for publication, May 25, 2006

Text edited by George A. Havach

Layout and design by Stephen L. Scott 
Contents

Abstract--------------------------------------------------------------------------------------------------------1

Introduction----------------------------------------------------------------------------------------------------1

Background----------------------------------------------------------------------------------------------------2

Methods--------------------------------------------------------------------------------------------------------3

Survey Design-------------------------------------------------------------------------------------------3

Survey Dissemination----------------------------------------------------------------------------------3

Data Analysis--------------------------------------------------------------------------------------------4

Results---------------------------------------------------------------------------------------------------------4

Participant Demographics-----------------------------------------------------------------------------4

Fieldwork Goals, Technology, and Conditions-------------------------------------------------------6

Postfieldwork Data Processing and Analysis----------------------------------------------------- 10

Discussion--------------------------------------------------------------------------------------------------- 15

Conclusions-------------------------------------------------------------------------------------------------- 17

Acknowledgments------------------------------------------------------------------------------------------ 17

References Cited-------------------------------------------------------------------------------------------- 17

Appendices:

A. Survey Questions and Results-------------------------------------------------------------------- 18

B. Survey Cover Letter-------------------------------------------------------------------------------- 24

Figures

1. Photographs showing examples of hand-held mobile-computing technology---------------------2

2. Photographs showing examples of portable mobile-computing technology-----------------------3

3. Histogram of respondents’ numbers of years of field research--------------------------------------5

Tables

1. Job titles of survey participants-------------------------------------------------------------------------5

2. Types of landscape for fieldwork by survey participants--------------------------------------------5

3. Primary objectives for fieldwork by survey participants---------------------------------------------6

4. Types of observation made and sample taken by survey participants-----------------------------7

5. Environmental features mapped in the field by survey participants--------------------------------7

6. Mapping of environmental features in the field by survey participants,

- -- by USGS discipline----------------------------------------------------------------------------------------8

7. Uses of mobile-computing technology in the field by survey participants-------------------------8

8. Type, purpose, and scale of maps used in the field by survey participants-----------------------8

9. Primary uses of mobile-computing technology by survey participants----------------------------9

10. Aspects of fieldwork conditions reported by survey participants-------------------------------- 10

11. Estimated hours worked by and salary paid to survey participants to conduct

- -- postfieldwork data processing------------------------------------------------------------------------ 11

III
12. Salary table for Federal employees used to estimate salary paid to survey

participants to conduct postfieldwork data processing-------------------------------------------- 11

13. Uses of GIS by survey participants------------------------------------------------------------------- 12

14. Types of remotely sensed imagery used by survey participants---------------------------------- 12

15. Uses of remotely sensed imagery by survey participants----------------------------------------- 12

16. Uses of geospatial datasets by survey participants------------------------------------------------ 13

17. Correlation matrix between field activities and use of specific technologies------------------- 14

18. Mean numbers of years of USGS field experience between who answered

“Yes” and “No” on field activities and use of specific technologies----------------------------- 16

19. Results of statistical t test comparing years of USGS field experience with

field activities and use of specific technologies---------------------------------------------------- 17

IV


Abstract

To assess the opportunities and needs for mobile-computing technology at the U.S. Geological Survey (USGS), we 

conducted an internal, Internet-based survey of bureau scientists whose research includes fieldwork. In summer 2005, 144 

survey participants answered 65 questions about fieldwork 

activities and conditions, technology to support field research, 

and postfieldwork data processing and analysis. Results suggest that some types of mobile-computing technology are 

already commonplace, such as digital cameras and Global 

Positioning System (GPS) receivers, whereas others are not, 

such as personal digital assistants (PDAs) and tablet-based 

personal computers (tablet PCs). The potential for PDA use 

in the USGS is high: 97 percent of respondents record field 

observations (primarily environmental conditions and waterquality data), and 87 percent take field samples (primarily 

water-quality data, water samples, and sediment/soil samples). 

The potential for tablet PC use in the USGS is also high: 59 

percent of respondents map environmental features in the 

field, primarily by sketching in field notebooks, on aerial photographs, or on topographic-map sheets. Results also suggest 

that efficient mobile-computing-technology solutions could 

benefit many USGS scientists because most respondents spend 

at least 1 week per year in the field, conduct field sessions that 

are least 1 week in duration, have field crews of one to three 

people, and typically travel on foot about 1 mi from their field 

vehicles. By allowing researchers to enter data directly into 

digital databases while in the field, mobile-computing technology could also minimize postfieldwork data processing: 

93 percent of respondents enter collected field data into their 

office computers, and more than 50 percent spend at least 1 

week per year on postfieldwork data processing. Reducing 

postfieldwork data processing could free up additional time 

for researchers and result in cost savings for the bureau. Generally, respondents support greater use of mobile-computing 

technology at the USGS and are interested in training opportunities and further discussions related to data archiving, access 

to additional digital data types, and technology development.

Introduction

Although such technology as satellite imagery has 

greatly improved regional data collection, many U.S. Geological Survey (USGS) scientists rely on manual data entries, 

sketches, and paper-map corrections in the course of their 

fieldwork. Emerging mobile-computing technologies, such 

as personal digital assistants (PDAs) and tablet-based personal computers (tablet PCs), can potentially simplify and 

improve field data collection and postfieldwork data processing and analysis in USGS research. Using this technology, 

USGS scientists could devote less time and energy to the 

logistic concerns of fieldwork and data management. For 

research initiatives that have data-collection protocols, such 

as regional water-quality monitoring, mobile-computing 

technologies could run integrated data-collection templates. 

In addition to simplifying data collection, mobile-computing 

technology may also alter field efforts by enabling researchers to create dynamic or adaptive sampling designs.

The use of mobile-computing technology to facilitate 

fieldwork is increasing at the USGS, including the Water 

Resources Discipline’s Field Computer Interest Group’s 

use of PDAs in water-quality monitoring (URL http://water.

usgs.gov/usgs/nwis/FCIG/fcig_index.html) and the Geologic 

Discipline’s annual workshops on digital techniques related 

to geologic mapping (Stoller, 2003). However, these and 

other bureau efforts related to mobile-computing technology (Pavlis and Little, 2001; Williams, 2001; Edmondo, 

2002) may remain within specific disciplines, and other 

USGS scientists may not benefit. With no bureauwide technical-assistance group, individual researchers are devoting 

limited resources and time to technology development and 

implementation. Although individual use of mobile-computing technology is not new for some researchers, demand for 

bureau coordination and communication on mobile-computing technology development and implementation is increasing. In response, the USGS Geospatial Information Office 

(GIO), the USGS Western Ecological Research Center, and 

the USGS California Water Science Center jointly sponsored a PDA Workshop in San Diego, Calif., in November 

2004 to (1) obtain an overview of how PDAs are currently 

being used for research and monitoring in the USGS and the 

Department of the Interior, (2) create a forum for PDA users 

throughout the USGS to meet and discuss common issues, 

and (3) determine the need for a bureau-level technical team 

to coordinate and promote mobile-computing technology to 

support USGS fieldwork.

To further the objectives of the 2004 USGS PDA Workshop, we have conducted a bureauwide issues and needs 

By Nathan J. Wood and David L. Halsing

Opportunities and Needs for Mobile-Computing 

Technology to Support U.S. Geological Survey 

Fieldwork
 Opportunities and Needs for Mobile Computing Technology to Support U.S. Geological Survey Fieldwork

assessment to document current and potential use of mobilecomputing technology for USGS field data collection and 

mapping activities. An internal, Internet-based survey was 

used to collect data for this study, with questions about 

fieldwork activities and conditions, technology to support 

USGS fieldwork, and postfieldwork data processing and 

analysis. This report summarizes the results of this survey, 

as well as suggesting potential next steps related to the use 

and effectiveness of mobile-computing technology in USGS 

research. This report is intended to (1) reveal opportunities 

for technology sharing, (2) foster a community of technology developers and users, (3) identify new areas for mobilecomputing-technology development and implementation, 

and (4) encourage a coordinated bureauwide effort to further 

evaluate mobile-computing technology to support USGS 

fieldwork.

Background

For those who are unfamiliar with mobile-computing 

technology, we provide a short overview of the two most 

common types—PDAs and tablet PCs. Although laptop PCs 

can be considered mobile devices, we focus specifically on 

hand-held devices that can be used easily while standing.

A PDA is a hand-held device that combines a PC, cellular phone, an e-mail client, and (or) a personal organizer 

(fig. 1). Unlike laptop computers with keyboards, most 

PDAs use a stylus, a penlike instrument for tapping the 

screen to input information. Newer PDAs include both a 

stylus and a miniature keyboard designed primarily for input 

by one’s thumbs. Since many PDAs can run full-featured 

databases, PDAs can be used to download data from field 

monitors and dataloggers. Currently, many PDAs have builtin Global Positioning System (GPS) receivers and can run 

geographic-information-system (GIS) software, allowing 

users to navigate, mark study sites, and collect georeferenced data.

Tablet PCs are laptop PCs that can be manipulated to 

resemble a writing pad (fig. 2). In addition to a full-size 

keyboard and built-in mouse, tablet PCs are equipped with a 

touch-sensitive screen designed to interact with a digital pen 

that operates like a combined computer mouse and stylus. 

These configurations allow the user to use the tablet PC 

while standing up and cradling the device. A tablet PC also 

has handwriting- and voice-recognition software, allowing 

users to capture information in ways that best suit them. A 

full screen and large data-storage capacity (currently max 

80 GB) allows users to transport and access high-resolution 

data and imagery in the field. Within a GIS environment, a 

tablet PC allows a user to use the digital pen to access data, 

attach notes to geographic locations, and delineate boundaries. Creating boundaries, either as lines or polygons, directly 

in a GIS eliminates the need to later digitize the information, 

such as collected waypoints or sketches on a paper map.

When deciding which technology is appropriate, a 

fieldworker should relate the features of each device to 

the research objectives, field conditions, and budget of a 

project. With full-featured databases but small screens and 

small data-storage capacities, PDAs are ideal for downloading data, creating points on a map, recording audio notes, 

and collecting data by way of predetermined dropdown 

menus. With greater computing power, a digital pen, and a 

full-size screen, a tablet PC can do all of these things plus 

offer the full functionality of a laptop computer, as well as 

the ability to write digital field notes and to draw polygons 

when delineating boundaries. In comparison with a PDA, 

however, a tablet PC has a shorter battery life, is more 

expensive, and may offer rather more functionality than a 

fieldworker needs.

Figure 1. Examples of hand-held mobile-computing technology. A, Portable digital assistant (PDA). B, Scientist using a PDA in the field.


Methods

Survey Design

This study is based on data collected from an Internetbased survey conducted in summer 2005 that consisted of 65 

questions about (1) the goals and logistic aspects of USGS 

fieldwork, (2) postfieldwork data processing and analysis, and 

(3) survey-participant demographics. The survey was a mix 

of scripted and open-ended questions; out of 65 questions, 37 

offered the respondent a choice of predetermined answers, 

such as “Yes” or ”No,” and the other 28 allowed the respondent to type a response directly into a text box. The full set of 

survey questions and responses is provided in appendix A.

We designed the survey Web site so that a respondent saw 

only one question at a time. Questions were contingent and 

nested, wherein a respondent answering “Yes” to an initial 

question would then see a set of more detailed, followup questions, whereas a respondent answering “No” to the same question would be routed past these followup questions to the next 

major question. This structure shortened the survey time for 

those respondents with nothing to add to a particular line of 

questioning, while extracting as much information and insight 

as possible out of others.

After a survey participant clicked or typed a response and 

clicked a “Submit” button for each question, the response was 

automatically entered into a temporary memory buffer. While 

taking the survey, a respondent had the opportunity to change 

previous responses, start over, or quit. The default selection 

for all questions was “No answer,” allowing a respondent to 

proceed to the next question without answering. The entire 

set of responses was entered into a permanent database after a 

respondent completed the survey and actively clicked a final 

“Submit” button.

At the beginning of the survey, participants were notified 

that they would be asked for their name and contact information at the conclusion of the survey, but that this information 

was optional and that their responses would be counted even 

if they chose not to provide it. Contact information would be 

used by the USGS to begin to build a virtual community of 

interested mobile-computing-technology users and an archive 

of USGS scripts and tools that capitalize on mobile-computing 

technology. However, the survey stated upfront that responses 

to questions would be separated from contact information to 

protect a respondent’s privacy and confidentiality.

Survey Dissemination

The survey was hosted on an internal USGS Web site. 

Our intent was to reach all individuals involved in field-based 

research at the USGS, including those who are currently using 

mobile-computing technology. USGS staff scientists and managers were notified about the survey and requested to respond 

in the following ways:

•	 E-mails with a survey cover letter (see app. B) were sent 

from the Geographic Analysis and Monitoring Program 

(Geography Discipline) and the Enterprise Information 

Program (Geospatial Information Office) to all USGS 

program coordinators and team leaders, who were asked 

to forward the information to the appropriate fieldworkers 

in their groups;

•	 A notice with a survey link was posted on the USGS internal Web site; and

• A notice in Western Exposure, a weekly electronic newsletter of the USGS Western Region, invited readers to 

respond to the survey and provided a link to the survey’s 

Web site.

Figure 2. Examples of portable mobile-computing technology. A, Tablet-based personal computer (tablet PC). B, Use of a tablet PC with a stylus.
 Opportunities and Needs for Mobile Computing Technology to Support U.S. Geological Survey Fieldwork

Data Analysis

Analysis of the survey data varied, based on question type. 

For scripted questions (for example, “In which USGS region are 

you employed? (a) Western Region, (b) Central Region, (c) Eastern Region, (d) Headquarters”), we calculated the percentage of 

each option. For certain ”Yes or No” questions, we performed statistical analysis to assess the degree of correlation and the strength 

of influences between several of the factors addressed in the 

survey. For example, we used statistical t tests to check for significant differences in the mean numbers of years of field experience 

between groups of respondents who answered “Yes” or “No” to 

questions about their use of certain devices and (or) perform various field activities.

For open-ended questions, we grouped responses on the 

basis of similarity. For example, to one question, “How many 

years have you been doing field research?”, some respondents 

typed in a number (“5”), others spelled it out in words (“five 

years”), others answered with mixed numerals and words (“5 

years”), and still others gave partial years (“4 years and 11 

months”). For each response, all partial years were rounded up to 

the next whole year, and all verbal or mixed numerical and verbal 

answers were changed to whole numbers only, creating a column 

of integers that could be more readily analyzed and compared 

with other responses.

Similar, though commonly nonnumerical, reclassification 

procedures were conducted for other open-ended questions, such 

as job title, field objectives, and type of data collected, as well as 

for various comments. For some open-ended questions, reclassification was difficult because of the wide range of responses. 

For these questions, certain responses are used in the body of this 

report to illustrate issues; the full list of responses is provided in 

appendix A. In addition, because some respondents gave more 

than one answer to open-ended questions, our reclassifications 

sometimes created more than one data point per respondent. For 

these questions, because we did not wish to discard any useful 

data, the tables in the next section distinguish between the number 

of respondents, N, and the number of individual responses, R, to a 

particular question. Where this notation is used, N<R.

Survey results and the discussion presented here do not 

represent all possible views on issues related to the use of mobilecomputing technology at the USGS but only the opinions of 

those individuals who chose to participate in the survey. Thus, the 

conclusions in this report are based on those responses alone and 

do not necessarily reflect any official USGS position. Because the 

collected data represent expressed, instead of revealed, participant 

perceptions and preferences, survey results cannot be used to 

solely gauge the potential success for mobile-computing technology at the USGS but should be considered only a starting point 

for further discussion on the opportunities and needs for mobilecomputing technology to support USGS fieldwork.

Results

The survey Web site was active for 2 months, and most 

responses were received within the first 2 weeks. After 2 

months, responses were extracted from the database for analysis. During the survey, we received 144 responses from USGS 

scientists and technicians. Because few survey participants 

answered every question, the number of respondents to a question is typically less than 144; and because of the nested and 

contingent survey structure, not every respondent saw every 

question. In this report, we present survey results based on 

the number of participants who responded to each question, 

and remove nonrespondents, unless otherwise noted, where 

“respondents” refers to an adjusted percentage reflecting those 

survey participants who responded to that particular question. 

Unadjusted percentages and nonresponse rates are presented 

in appendix A. Apparent discrepancies between some of the 

percentages reported in the body of this report and those 

presented in appendix A reflect differences between these 

adjusted and unadjusted percentages.

We were unable to determine an overall response rate 

for the survey as a whole because we do not know how many 

USGS employees actually conduct fieldwork, whether a 

project chief answered as a representative for a group that 

conducts fieldwork, or how many USGS fieldworkers may 

have been unavailable during the survey period (for example, 

on annual leave or in the field). We note that survey participants were self-selecting and not a truly random sample of 

USGS fieldworkers. Thus, some bias may exist between those 

respondents who chose to complete the online survey and 

those who did not. We hope that future assessments will be 

conducted to further promote mobile-computing technology at 

the USGS.

One way to promote mobile-computing technology is to 

provide mechanisms for collaboration and communication 

between individuals who are already using it or are receptive 

to learning more about it. When asked, 75 survey respondents 

(52 percent) gave their name, telephone number, or both so 

that they could be contacted for further input on developing 

field tools. This information could be used to create a bureauwide network of technology-using field scientists and a clearinghouse of mobile-computing-technology strategies, scripts, 

and product-use ideas.

Participant Demographics

To provide some context for the survey results discussed 

in this report, we summarize the demographics of survey 

participants. Most participants are employed in the Western 

Region (68 percent), with fewer from the Central Region (19 

percent) and the Eastern Region (12 percent), and some in no 

stated region (1 percent). In addition, most participants are 

from the Water Resources Discipline (WRD) (54 percent), 

fewer from the Biological Resources Discipline (22 percent), 

the Geology Discipline (14 percent), the Geography Discipline 

(10 percent), and the Geospatial Information Office (0 percent), and, again, some with no stated discipline (1 percent). A 

significant WRD presence was also observed in the distribution of participants’ position titles, where 41 percent of participants were either hydrologists or hydrologic technicians (table 

1). Without complete knowledge of the full range of USGS 


fieldworkers, we cannot state whether the predominance of 

WRD employees demonstrates an unrepresentative sample 

population for the survey. In fact, such a large percentage of 

USGS fieldwork may be performed by hydrologists or hydrologic technicians.

The distribution of years of field experience of survey participants (N=141) is quite broad, ranging from 1 to 41 years, 

with a mean of 15.5 years (s.d., 9.5 years) and a median and 

mode of 15 years. A histogram of years of field experience 

shows a distribution of survey respondents (fig. 3) that is not 

significantly skewed toward either end of the spectrum, suggesting that the sample population, at least in terms of years 

of USGS fieldwork, may fairly represent all USGS fieldworkers. Figure 3 also shows spikes at 15, 20, 25, 30, and 35 years, 

suggesting that some respondents with many years of experiLandscape

type

No. of

responses

Various---------------- 40

River------------------- 24

Wetland --------------- 18

Desert ----------------- 13

Estuary ---------------- 7

Forest------------------ 7

Riparian --------------- 5

Polar------------------- 4

Arid-------------------- 3

Grassland ------------- 3

Mountains------------- 3

Volcanic--------------- 3

Lacustrine------------- 2

(None given)---------- 2

Urban------------------ 2

Coastal zone ---------- 1

Flood plain------------ 1

Well area-------------- 1

N139

Table 2. Types of landscape for fieldwork by survey participants.

[N, number of respondents to question]

0

2

4

6

8

10

12

14

16

5 10 15 20 25 30 35 40

Years

Responses

Figure 3. Histogram of respondents’ numbers of years of field research.

 

 Response

rate

(percent)

Hydrologic technician----------------------------------- 23

Hydrologist----------------------------------------------- 18

Geologist-------------------------------------------------- 11

Geographer------------------------------------------------ 6

Physical scientis------------------------------------------ 6

Ecologist--------------------------------------------------- 5

Biologist--------------------------------------------------- 4

Wildlife biologist----------------------------------------- 4

Fishery biologist------------------------------------------ 3

Supervisory hydrologist--------------------------------- 2

Electronic technician------------------------------------ 1

General biologist----------------------------------------- 1

Hydraulic engineer--------------------------------------- 1

IT specialist----------------------------------------------- 1

Research scientist---------------------------------------- 1

Supervisory hydrologic technician--------------------- 1

Zoologist--------------------------------------------------- 1

Assistant unit leader------------------------------------- 1

Biological science technician-------------------------- 1

Cartographer---------------------------------------------- 1

Chemical oceanographer-------------------------------- 1

Chemist---------------------------------------------------- 1

Chief------------------------------------------------------- 1

Electrical engineer--------------------------------------- 1

Environmental scientist--------------------------------- 1

Field ecologist-------------------------------------------- 1

Geodetic control specialist------------------------------ 1

Geophysicist---------------------------------------------- 1

Marine ecologist----------------------------------------- 1

Physical science technician----------------------------- 1

Remote-sensing scientist-------------------------------- 1

Senior scientist------------------------------------------- 1

Social scientist-------------------------------------------- 1 

Job Title

N=143

Table 1. Job titles of survey participants.

[IT, information technology. N, number of respondents to question]
 Opportunities and Needs for Mobile Computing Technology to Support U.S. Geological Survey Fieldwork

ence may have rounded their answers to the nearest 5-year 

increment.

To understand field conditions, we asked survey participants to answer an open-ended question on the types of landscape or environment in which they typically work while in 

the field. The purpose of this question was to understand field 

conditions and not to assess the demand for mobile-computing technology, because such demand will vary with the type 

of research. Similar responses were grouped into categories of 

our own design (table 2). If more than one distinct landscape 

type was mentioned (for example, “Mountains, deserts, forests, croplands, urban”), the response was coded as “Various.” 

Results suggest that most survey participants conduct fieldwork in more than one type of landscape. The most common 

responses after “Various” were rivers and wetlands, possibly 

reflecting the many respondents who are WRD scientists.

Fieldwork Goals, Technology, and Conditions

One set of survey questions focused on the goals, procedures, supporting technology, and environmental conditions 

of fieldwork. Survey participants were first asked, in an openended question, to describe their primary fieldwork objectives. 

Similar responses were grouped into categories of our own 

design (table 3). The large number of responses (R=46) to 

questions related to “water-quality and (or) -quantity monitoring” reflects the high percentage of survey participation by 

WRD scientists. On the basis of subsequent responses, this 

type of fieldwork most likely consists of site sampling and 

data downloads, which could benefit from the use of PDAs. In 

hindsight, this question may have been too vague, because the 

second most common type of response included some generic 

comment on data collection. Furthermore, two respondents 

commented that the question itself was too vague, and gave 

no other response. Following these first two response types, 

the next set of responses all focused on some type of regional 

mapping, including geologic, hydrologic, biologic, and land 

use/landcover. Mobile-computing solutions that use tablet PCs 

would probably be more beneficial for this type of fieldwork, 

although such a decision should be on a case-by-case examination.

Survey participants were then asked to comment on their 

field activities. Nearly all respondents (99 percent) answered 

that they record observations, and 87 percent answered that 

they take samples. Nonetheless, the focus of their data-collection efforts varies considerably. Similar responses were 

grouped into categories of our own design (table 4). Again, 

observations and samples related to hydrologic issues were the 

most common responses. General environmental conditions 

of a study area were the most common recorded field observation, regardless of the type of research being conducted. We 

note also that here N≠R, because some respondents gave more 

than one answer.

In addition to recording observations and taking samples, 

most respondents (59 percent) map environmental features 

while in the field (table 5). Sampling sites are the most commonly mapped features, followed by geologic and (or) geomorphic features. Most respondents stated that they sketch 

features directly into paper notebooks, on aerial photographs, 

on annotated GPS points, or on topographic-map sheets. 

Apparently, as we originally hypothesized, many of these 

fieldworkers may benefit from the use of mobile-computing 

technology to digitally map environmental features. As the 

USGS begins to develop computer applications and mobilecomputing technology specifically for field use, it will be 

important to determine exactly what types of feature are being 

sketched (for example, points, lines, or areas) and for what 

purpose.

We also compared the percentage of respondents who 

answered that they conduct mapping with the discipline in 

which they are employed (table 6). Results suggest that the 

percentage of respondents in the Geography and Biological 

Resources Disciplines who map environmental features does 

not differ significantly from the overall response rate of all the 

disciplines. The Geology Discipline may be somewhat overrepresented in the group of mappers, whereas WRD may be 

somewhat underrepresented. The large percentage of hydrologists and hydrologic technicians who participated in the survey 

explains much of this difference because they are less likely to 

conduct mapping.

To determine the breadth of mobile-computing-technology use in the USGS, we asked survey participants whether 

they use certain devices while in the field (table 7). Almost all 

respondents (98 percent) to this question use cameras, and most 

respondents (96 percent) claim to use digital cameras, either 

alone (93 percent) or in combination with film photography 

Primary objective 

for fieldwork

No. of

responses

Water quality and (or) quantity monitoring -------------- 46

Data collection—general ----------------------------------- 36

Geologic mapping------------------------------------------- 14

Ecologic mapping ------------------------------------------- 7

Land-use/landcover mapping ------------------------------ 7

Fish sampling ------------------------------------------------ 5

Amphibian monitoring-------------------------------------- 3

GPS surveys ------------------------------------------------- 3

Vegetation mapping----------------------------------------- 3

Question considered too vague, no answer given-------- 2

Bear monitoring --------------------------------------------- 1

Bird counts and sampling----------------------------------- 1

Data collection—GHT-------------------------------------- 1

Data collection—meteorologic ---------------------------- 1

Field verification of remote-sensing classifications ----- 1

Forest mapping ---------------------------------------------- 1

Geomorphic mapping --------------------------------------- 1

Lava-flow monitoring--------------------------------------- 1

Postearthquake mapping------------------------------------ 1

Soil sampling ------------------------------------------------ 1

Walrus monitoring ------------------------------------------ 1

N137

Table 3. Primary objectives for fieldwork by survey participants.

[GHT, Geographic Hash Table; GPS, Global Positioning System. N, number of 

respondents to question]


Field observation No. of

responses Type of sample

Environmental conditions ---------------- 55 Water-quality data----------- 32

Water-quality data------------------------- 54 Water-------------------------- 29

Channel and control conditions---------- 33 Sediment/soil----------------- 25

Location ------------------------------------ 27 Animal------------------------ 16

Habitat/vegetation characteristics ------- 25 Rocks ------------------------- 12

Animal behavior/characteristics --------- 17 Plants-------------------------- 9

Time ---------------------------------------- 16 Site characteristics----------- 6

Bedrock structures/ rock types----------- 13 Suspended sediment--------- 5

Date ----------------------------------------- 13 GPS data---------------------- 4

Quality-assurance data -------------------- 11 Animal history --------------- 3

Landcover type/condition ---------------- 8 Various ----------------------- 3

Sediment characteristics------------------ 7 Schedule 19 QW------------- 1

Landform----------------------------------- 4 Unknown --------------------- 1

Photograph --------------------------------- 4

Unknown ----------------------------------- 2

Archeologic remains ---------------------- 1

Field datalogger parameters-------------- 1

Human behavior--------------------------- 1

Name---------------------------------------- 1

Ocean conditions -------------------------- 1

Seismic/sonar data------------------------- 1

Sketches ------------------------------------ 1

Snow characteristics ---------------------- 1

N=133, R=297 N=123, R=146

No. of

responses

Table 4. Types of observation made and sample taken by survey participants.

[GPS, Global Positioning System. N, number of respondents to question; R, number of individual responses to question]

What do you map? No. of

responses How do you do map features? No. ofresponses

Site location--------------------------------- 28 Sketch -------------------------------------------- 18

Geomorphic features----------------------- 11 GPS ----------------------------------------------- 16

Geologic/geomorphic features------------ 8 Sketch on aerial photograph-------------------- 9

Geologic features--------------------------- 6 Sketch with annotated GPS data--------------- 7

Vegetation type ----------------------------- 6 Sketch on topographic-map sheet ------------- 6

Habitat--------------------------------------- 5 ArcGIS ------------------------------------------- 5

Various -------------------------------------- 5 GPS, aerial photographs------------------------ 4

Anthropogenic features-------------------- 3 Numerous ---------------------------------------- 4

Landcover ----------------------------------- 3 GPS total station--------------------------------- 2

Nests ----------------------------------------- 3 None ---------------------------------------------- 2

Bathymetry---------------------------------- 2 Compass and tape ------------------------------- 1

Digital -------------------------------------------- 1

GPS, ArcGIS ------------------------------------ 1

GPS, laser rangefinder-------------------------- 1

GPS, sketch on topographic-map sheet------- 1

GPS, tape, compass ----------------------------- 1

Hypack software--------------------------------- 1

N=80, R=80 N=80, R=80

Table 5. Environmental features mapped in the field by survey participants.

[GPS, Global Positioning System. N, number of respondents to question; R, number of individual responses to question]
 Opportunities and Needs for Mobile Computing Technology to Support U.S. Geological Survey Fieldwork

Response rate

(percent) Geography Geology BiologicalResources

Water

Resources N

Those who map features ---------- 11 21 23 45 82

Overall survey response----------- 10 14 22 54 143

Table 6. Mapping of environmental features in the field by survey participants, by USGS discipline.

[N, number of respondents to question]

Respondents

who say they:

Response

rate

(percent)

Take photographs ----------- 98

Use digital camera----------- 96

Use a GPS receiver---------- 88

Use a paper map ------------- 78

Use a field computer -------- 76

Use a PDA ------------------- 34

Use a tablet PC -------------- 12

Type No. of

responses Purpose No. ofresponses

Scale No. of

responses

Topographic maps/DRG----------- 73 Site location --------------------- 35 1:24,000----------- 55

Aerial photography/DOQ---------- 24 Navigation ----------------------- 28 Various------------- 26

Roadmaps --------------------------- 15 Sampling-site location---------- 14 1:100,000 --------- 6

Self-made GIS map----------------- 8 Plotting--------------------------- 5 1:10,000----------- 3

City/county map -------------------- 8 Reference ------------------------ 5 Unknown --------- 3

Atlas---------------------------------- 8 Confirm features---------------- 4 1:63,360----------- 2

Remotely sensed imagery---------- 7 Ground-truthing----------------- 3 1:15,000----------- 2

NOAA chart------------------------- 5 Visualization -------------------- 3 1:12,000----------- 2

Geologic maps ---------------------- 5 Notation-------------------------- 3 1:80,000----------- 1

Other Federal agencies------------- 5 Computer backup --------------- 2 1:40,000----------- 1

Paper map --------------------------- 3 Flight planning ------------------ 2 1:30,000----------- 1

Hand-drawn map ------------------- 2 Stream-channel tracking ------- 2 1:6,000 ------------ 1

Landcover map---------------------- 2 Habitat demarcation ------------ 2 1:2,000 ------------ 1

Soils map ---------------------------- 2 Bedrock distribution------------ 1 1:1,000 ------------ 1

Internet-based map ----------------- 2 Watershed delineation---------- 1 1:500 -------------- 1

Private-sector software------------- 3 Land ownership ----------------- 1 1:200 -------------- 1

Commercial GPS map ------------- 1 Locate datum-------------------- 1 Other -------------- 1

Self-made image map -------------- 1 Vegetation mapping ------------ 1

Navigation software ---------------- 1 Georeferencing------------------ 1

UTM reference grid ---------------- 1 Data analysis -------------------- 1

N=100, R=76 N=78, R=115 N=98, R=108

Table 7. Uses of mobile-computing 

technology in the field by survey participants.

[GPS, Global Positioning System; PC, personal 

computer; PDA, personal digital assistant]

Table 8. Type, purpose, and scale of maps used in the field by survey participants.

[DRG, digital raster graphics; DOQ, digital orthophoto quadrangle; GPS, Global Positioning System; NOAA, National Oceanic and Atmospheric Administration; UTM, universal 

transverse mercator. N, number of respondents to question; R, number of individual responses to question]

(2 percent). The second-most common device mentioned by 

survey participants (88 percent) is a GPS receiver. When asked 

to describe how they use GPS receivers in the field, respondents 

commonly answered for “Location/navigation,” although many 

respondents misunderstood the question and instead listed the 

type of GPS receiver that they use (see app. A).

Although significant percentages of respondents use such 

devices as GPS receivers (88 percent) and laptop computers 

(76 percent) in the field, the use of paper maps remains quite 

prevalent among survey participants (78 percent). To many of 

the open-ended questions (see app. A), many survey participants answered that they gain much efficiency by combining 

lightweight, “low tech” paper versions of spatial information 

1

Owing to rounding, not all the percentages presented herein sum to 100 

percent.


with more advanced techniques to perform different aspects 

of fieldwork. Respondents were asked followup questions on 

map type, purpose, and scale in their fieldwork (table 8): the 

most common type is a topographic map, followed by aerial 

photographs; the most common purpose is for locating study 

sites and for general navigation; and the most common scale is 

1:24,000, that of USGS topographic-quadrangle maps.

Although GPS receivers and laptop computers have 

become almost commonplace in USGS fieldwork, other 

devices have not caught on to the same extent. Only 34 percent of respondents use PDAs in the field, and only 12 percent 

use tablet PCs (table 7). Primary uses of PDAs and tablet PCs 

include data collection and GPS data acquisition (table 9). 

Results suggest that survey participants are using tablet PCs 

to download data and to program field equipment, as if they 

are simply larger versions of PDAs, without tapping into the 

unique functionality of the tablet PC, such as digital feature 

mapping or voice recognition. Despite the low rates of PDA 

and tablet-PC use (table 7), those who do use them apparently 

do so at a fairly high level of sophistication because more than 

24 percent of users of those devices have created specialized 

scripts or software programs for them. Typical scripts include 

checking data accuracy, field calculations, collecting and processing water quality and quantity data, linking field observations to databases, processing GPS data, and miscellaneous 

data-collection forms (see app. A).

After responding on the purpose and type of mobilecomputing technology used in fieldwork, survey participants 

were asked to comment on fieldwork conditions (table 10). 

Information from this set of responses could help determine 

the overall demand for and type of mobile-computing technology at the USGS. For example, if many USGS scientists 

spend significant time in the field, the USGS could allocate 

bureauwide resources to promote mobile-computing technology. In this series of scripted questions, survey participants 

were asked to comment on the average length of a field trip, 

the number of days per year spent in the field, and the size of 

field crews. Results for the average time per year spent in the 

field (table 10) indicate more than 1 week for 95 percent and 

at least 1 month for 68 percent of survey participants. Results 

for the average length of an individual field investigation (table 

10) indicate 1 week for 44 percent and 1 day for 22 percent of 

survey participants. Therefore, approximately two-thirds of 

field investigations are 1 week or less in duration.

Sizes of field crews vary among survey participants; 28 

percent of respondents stated that they go to the field alone. For 

these individuals, backpack space and weight are significant 

concerns, and so mobile-computing technology, such as PDAs 

or tablet PCs, could minimize the number of items, such as 

hardcopy maps, compasses, and notebooks, that a researcher 

needs to transport to the field. At the other end of the spectrum, 

38 percent of respondents stated that their field crews consist of 

three or more people. For these larger field crews, mobile-computing technology can also be helpful by streamlining logistics 

and standardizing data collection and processing with common 

software platforms and hardware tools.

To examine issues relating to equipment weight and battery life of mobile-computing technology, survey participants 

were asked to comment on how far they travel in the field. 

A total of 79 percent of respondents to this question (N=142) 

stated that they travel on foot away from a vehicle for fieldwork. An open-ended followup question asked how far they 

typically travel away from the field vehicle. We first standardized the 110 responses to this question (for example, “two 

miles” and “a mile or two” became “2 miles”) and then created 

categories of (1) up to 1⁄2 mi, (2) from 1⁄2 to 2 mi, (3) greater 

than 2 mi, and (4) various, to approximate the willingness of 

an individual to carry mobile-computing technology and (or) 

walk back to a vehicle for additional batteries. (Many laptop 

computers, in addition to being heavy, have batteries that last 

only about 4 hours.) We assumed that willingness to retrieve 

batteries was high for distances up to 1⁄2 mi, moderate for 1⁄2 to 

Use of PDA No. of

responses

Use of tablet PC No. of

responses

Collect data---------------------------- 38 Download data-------------------------------- 7

Use GPS ------------------------------- 9 Program equipment -------------------------- 5

Look up contact information -------- 6 Collect data------------------------------------ 4

Maintain schedule/calendar---------- 5 Graph data------------------------------------- 2

Download data ------------------------ 4 Use GPS --------------------------------------- 2

Access background data ------------- 3 Map vegetation ------------------------------- 1

Navigation----------------------------- 2 Map human-impact features----------------- 1

Monkes1 ------------------------------- 1 Provide information for field ID------------ 1

Same as PDA but with bigger files--------- 1

N=48, R=68 N=16, R=24

Table 9. Primary uses of mobile-computing technology by survey participants.

[GPS, Global Positioning System; PDA, personal digital assistant. N, number of respondents to question; R, number of individual responses to question]
10 Opportunities and Needs for Mobile Computing Technology to Support U.S. Geological Survey Fieldwork

2 mi, and low for more than 2 mi from a field vehicle. Most 

respondents (57 percent) travel more than 1⁄2 mi from a vehicle 

during fieldwork (table 10). These considerations are important 

when developing hardware and (or) software solutions because 

of the need to address equipment weight and battery life.

Along with similar concerns about mobile-computing-technology development and implementation, survey 

participants were also asked how rugged their field equipment needed to be: 55 percent of respondents to this question answered that equipment needed to be waterproof or 

environmentally sealed, and 45 percent would be using this 

equipment in controlled environments and so would not need 

ruggedized equipment or take special precautions (see app. 

A, question 11).

Postfieldwork Data Processing and Analysis

One of the primary strengths of mobile-computing technology is its ability to minimize transcription time and errors 

in postfieldwork data processing and analysis because scientists are able to enter data directly into digital databases while 

in the field. To better understand the need for this facility, 

survey participants were asked a series of questions about 

current postfieldwork data processing and analysis in their 

offices. A total of 93 percent of respondents to this question 

answered that they enter collected field data into their office 

computers, of which 70 percent enter their own data and 23 

percent of participants have someone else do it for them (see 

app. A, question 16). These results suggest that most individuals enter their own collected field data into their office computers, except in the Biological Resources Discipline, where 

15 of 25 respondents answered that someone else enters their 

data for them (see app. A, question 16). In addition to data 

input, 27 percent of respondents to this question also spend 

office time recording field observations or mapping features 

manually on a paper map or photograph (see app. A, question 

17).

The length of time reported for postfieldwork data processing by survey participants varies considerably (table 11), 

from 1 day (31 percent) to greater than 1 month (11 percent); 

the most common responses were 1 day (31 percent) and 1 

week (32 percent). To evaluate the potential impact of postfieldwork data processing on USGS financial resources, we 

calculated the amount, in terms of salary dollars per year, 

that could be spent on this activity for the 111 respondents to 

this question. For general estimates, we multiplied the hourly 

rates of different grade-step (GS) levels for the length of 

time that respondents answered it took them to process field 

data. For example, 35 respondents stated that they took 1 day 

to process field data, 35 that they took 1 week, and 14 that 

they took 2 weeks (table 11). For respondents who answered 

“greater than 1 month,” we used 1 month as a conservative 

assessment of the time spent by these individuals. For this 

calculation, we used hourly rates from the U.S. Office of 

Personnel Management’s 2005 Salary and Wages general 

locality pay tables (URL http://www.opm.gov/oca/05tables/

indexGS.asp); results for the various time periods are listed 

in table 12. We assume homogeneous blocks of employees 

and not a range of GS levels, although in reality a mixed 

group of GS levels exists across the bureau. Therefore, the 

range of GS levels from 7 to 15 should be the main focus 

for further discussion and not the specific values in each 

column. With this cautionary note, general estimates of the 

salary paid to conduct postfieldwork data processing range 

from $105,000 to $309,000 per year for the 111 respondents 

to this question. This range is certainly much greater when 

Average time

per year

spent in the field

Response

rate

(percent)

1 week ------------------------------ 4

1 month----------------------------- 27

1–3 months -------------------------- 40

3–6 months -------------------------- 23

6 months --------------------------- 5

No response ------------------------- 1

Average length of

an individual

field investigation

1 day --------------------------------- 22

1 week-------------------------------- 44

2 weeks ------------------------------ 10

1 month ------------------------------ 6

1 month----------------------------- 13

No response ------------------------- 6

Size of field crew, in

addition to

the respondent

No one else -------------------------- 28

One other person-------------------- 33

Two other people ------------------- 21

Three other people------------------ 6

Four or more other people --------- 11

No response ------------------------- 1

Distance traveled

from vehicle

for fieldwo1k

Up to 1⁄2 mi--------------------------- 38

From 1⁄2 to 2 mi---------------------- 32

From 2 to 5 mi ---------------------- 25

Various------------------------------- 4

No response ------------------------- 1

Table 10. Aspects of fieldwork conditions reported 

by survey participants.
11

the full spectrum of USGS fieldworkers is included in the 

calculations.

We note that the phrasing of the original question 

focused on the actual time spent entering field data, although 

some respondents may have interpreted this question to be 

asking how long it took them to get around to actually doing 

it, or the additional time for data-quality assurance and 

control. Therefore, our general estimates of the salary paid 

to conduct postfieldwork data processing should, again, be 

considered only starting points for additional discussions, not 

as an endorsement for data-collection standards or mandated 

use of mobile-computing technology. Survey results indicate 

that USGS research is quite diverse and that the development 

of data-collection standards or mandated use of mobile-computing technology will require additional study and bureauwide discussion. Nonetheless, it is clear that field-data entry, 

both into databases and on paper maps, is a time-intensive 

task for many USGS scientists and that properly designed 

mobile-computing technology may be able to reduce this 

time for individual researchers or larger research groups 

composed of multiple fieldworkers with similar data-collection methods. These preliminary estimates of the salary paid 

to conduct postfieldwork data processing suggest that the 

USGS could examine enterprise solutions to help fieldworkers use mobile-computing technology, through both training 

opportunities and a bureau-level technical team.

After USGS scientists enter their new field data into 

their office computers, many will then analyze those data 

Salary expenses if staff who are doing data entry are grade/step (GS):

Time

worked

Response

rate

(percent)

No. of

respondents

7/1 9/1 11/1 13/1 15/1

1 day------------- 31 35 $4,050.43 $4,954.52 $5,994.09 $8,543.23 $11,872.05

1 week----------- 22 35 20,252.16 24,772.61 29,970.43 42,716.16 59,360.26

2 weeks---------- 13 14 16,876.80 20,643.84 24,975.36 35,596.80 49,466.88

1 month --------- 14 16 37,128.96 45,416.45 54,945.79 78,312.96 108,827.14

1 month -------- 11 12 27,002.88 33,030.14 39,960.58 56,954.88 79,147.01

Total--------- 91 112 $105,311.23 $128,817.56 $155,846.25 $222,124.03 $308,673.34

Table 11. Estimated time worked by, and salary paid to, survey participants to conduct postfieldwork data processing.

Grade/step

(GS)

Hourly

rate

1-day wages

(8 hours)

1-week wages

(40 hours)

2-week wages

(80 hours)

1-month wages

(160 hours)

1/1 $7.67 $61.36 $306.80 $613.60 $1,227.20

2/1 8.63 69.04 345.20 690.40 1,380.80

3/1 9.41 75.28 376.40 752.80 1,505.60

4/1 10.57 84.56 422.80 845.60 1,691.20

5/1 11.82 94.56 472.80 945.60 1,891.20

6/1 13.18 105.44 527.20 1,054.40 2,108.80

7/1 14.65 117.20 586.00 1,172.00 2,344.00

8/1 16.22 129.76 648.80 1,297.60 2,595.20

9/1 17.92 143.36 716.80 1,433.60 2,867.20

10/1 19.73 157.84 789.20 1,578.40 3,156.80

11/1 21.68 173.44 867.20 1,734.40 3,468.80

12/1 25.98 207.84 1,039.20 2,078.40 4,156.80

13/1 30.90 247.20 1,236.00 2,472.00 4,944.00

14/1 36.51 292.08 1,460.40 2,920.80 5,841.60

15/1 42.94 343.52 1,717.60 3,435.20 6,870.40

Table 12. Salary table for Federal employees used to estimate salary paid to survey participants to conduct postfieldwork data processing.

[Hourly rates (from U.S. Office of Personnel Management 2005 Salary Tables and Wages, URL http://www.opm.gov/oca/05tables/indexGS.asp) reflect 

basic salary at step 1 for each grade, exclusive of overtime or additional locality-pay rates; values in other columns are hourly rates multiplied by the 

number of hours in each time period]
12 Opportunities and Needs for Mobile Computing Technology to Support U.S. Geological Survey Fieldwork

using software tools on their desktop PCs. A series of questions asked about these software tools to determine whether 

mobile-computing technology could support their use in field 

efforts. With their ability to manage and analyze spatial data, 

GISes have become powerful tools for researchers. Survey 

results indicate that 68 percent of respondents currently 

use a GIS in their research (see app. A, question 18). In an 

open-ended followup question, respondents could list one 

or more types of GIS system. Of the 90 respondents to this 

question, 85 stated that they use some sort of ESRI product, 

such as ArcGIS or ArcPad (see app. A, question 18a). Of the 

143 responses (R=143), 111 (78 percent) mentioned an ESRI 

product. Therefore, the USGS should consider compatibility 

with ESRI data frameworks and software in future efforts to 

develop mobile-computing-technology solutions for fieldworkers.

Respondents who answered that they use a GIS were 

asked to comment, in an open-ended question, on how 

they used a GIS in their research. Again, many of the 87 

respondents to this question (N=87) chose to give more than 

one answer, bringing the total number of responses to 131 

(R=131). Similar responses were grouped into categories 

of our own design (table 13). The most common response 

to GIS use was for general mapping and illustration (49 

responses), followed by spatial analysis (39 responses) and 

site selection (19 responses). With a PDA’s ability to run 

ArcPad, a limited GIS package, and a tablet PC’s ability to 

support full ArcGIS functionality, individual researchers 

should determine whether these GIS uses would be advantageous in the field. For example, the ability of a tablet PC 

to illustrate the landscape may be useful to researchers for 

creating adaptive or dynamic sampling designs, interacting 

with their field crews, or communicating research results to 

customers, such as land managers. Field-based spatial analysis may be useful for decisionmaking about ongoing events, 

such as disaster preparedness or emergency-response efforts. 

Finally, mobile-computing technology can address the use of 

a GIS for database management because the technology can 

be used to instantly process data digitally as they are collected 

in the field.

Another useful tool for researchers is remotely sensed 

imagery. A total of 44 percent of respondents answered that 

Category of use No. of

responses

Mapping/illustration----------- 49

Spatial analysis ---------------- 39

Site selection ------------------- 19

Database management -------- 12

Spatial modeling--------------- 6

Location verification ---------- 3

Various ------------------------- 2

Image processing -------------- 1

N87, R131

Table 13. GIS usage by survey participants.

[N, number of respondents to question; R, number of individual responses to question]

Type of

remotely sensed imagery

No. of

responses

Aerial photography -------------------- 15

Landsat---------------------------------- 15

Digital orthophotoquad---------------- 13

Lidar------------------------------------- 11

Satellite—generic ---------------------- 11

N/A -------------------------------------- 6

GOES------------------------------------ 4

InSAR ----------------------------------- 4

MASTER ------------------------------- 4

ASTER---------------------------------- 3

Stage------------------------------------- 3

Hyperspectral--------------------------- 2

Infrared---------------------------------- 2

Quickbird ------------------------------- 2

SPOT ------------------------------------ 2

Others (1 response per type)---------- 19

N=57, R=116

Table 14. Types of remotely sensed imagery used by survey 

participants.

Table 15. Uses of remotely sensed imagery by survey participants.

[N, number of respondents to question; R, number of individual responses to question]

Category of use No. of

responses

Navigation/orientation ---------- 12

Hydrologic studies -------------- 9

Landcover mapping ------------- 9

Geologic mapping--------------- 7

Mapping/illustration ------------ 6

Study design --------------------- 4

Animal-habitat studies---------- 3

Vegetation mapping------------- 2

Annual data report--------------- 1

Change detection ---------------- 1

Elevation models ---------------- 1

Ground-truthing ----------------- 1

Modeling ------------------------- 1

Multiband classification -------- 1

N=55, R=58

[ASTER, Advanced Spaceborne Thermal Emission and Reflection 

Radiometer; GOES, Geostationary Operational Environmental Satellite; 

InSAR, interferometric synthetic-aperture radar; MASTER, MODIS/ASTER 

airborne simulator; SPOT, Satellite Pour l’Observation de la Terre (French for 

“Earth-observation satellite”). N/A, not applicable. N, number of respondents 

to question; R, number of individual responses to question]
13

they use remotely sensed imagery in their research. The types 

and sources of remotely sensed imagery used by respondents 

vary greatly, although the most common types are aerial photographs, Landsat images, and digital orthophotoquads (table 14).

As in the previous questions about GIS usage, respondents who answered that they use remotely sensed imagery 

were asked to comment on how they use these data. Similar 

responses were grouped into categories of our own design 

(table 15). The most common response was “Navigation/orientation”; use for some type of mapping—whether landcover, 

geologic, or vegetation—was also a common response. Both 

PDAs and tablet PCs are capable of running GIS software 

with linked GPS receivers, allowing users to digitally create 

high-resolution GIS polygons in the field, such as wetland 

delineation or geologic boundaries, while also accessing 

remotely sensed imagery. Mobile-computing technology may 

increase the accuracy and precision of GIS mapping efforts 

and reduce the amount of data translation back in the office.

In addition to hardware and software, certain geospatial 

datasets are needed by researchers, and mobile-computing 

technology may allow researchers to use these data in the 

field. The proliferation of mobile-computing technology 

may increase the demand for certain data types as this facility becomes more widely available. Survey participants were 

asked to comment on their usage of a range of data types and 

formats (table 16). For 12 different data types, respondents 

chose one of the following options: paper map, digital format, 

would use if available, don’t use, or no response (the default 

answer); they were also given the opportunity to identify 

other data types not mentioned here. As an aside, many 

respondents to this followup question answered that “Both 

paper map and digital format” would also have been a beneficial choice. To reflect the current and potential demand for 

certain data types if mobile-computing technology was more 

accessible, we present the total percentage of respondents 

who stated that they currently use paper maps or digital data 

or would use them if they were available.

Survey results suggest that the data types most sought 

after by respondents, regardless of format, are topographic 

maps (86 percent), roads (80 percent), aerial photographs (76 

percent), and elevation data (70 percent); fewer respondents 

stated the need for bathymetric (30 percent) or soil (40 percent) data. Between these two extremes is a group of data 

types that about half of respondents believed to be useful, 

including landcover (45 percent), land use (46 percent), geology (48 percent), structures (50 percent), hydrology (51 percent), and geographic names (60 percent).

On the basis of responses in the category “Would use 

if available,” the important areas for greater bureauwide 

access are aerial photographs, land use, geology, hydrology, 

and soils (each identified by approximately 14 percent of 

respondents). It is unclear whether “available” here meant 

actually available, affordably available, or feasibly available 

in the sense that it could be readily integrated with other data 

types and formats. In addition to the data types mentioned in 

the survey, respondents answered that other data types were 

important and listed them in an open-ended followup question: Advanced Spaceborne Thermal Emission and Reflection 

Radiometer (ASTER) imagery to identify rock types and 

alluvium sources, cultural features, streamflow data, GPS latitude/longitude waypoints, engineered structures (for example, 

dams), hazard maps, historical land use/disturbances, land 

ownership, National Oceanic and Atmospheric Administration (NOAA) charts, aviation maps, and weather data (including tide and wave heights).

In addition to the descriptive summaries of responses 

presented in this report, we also conducted statistical analyses 

to compare various sets of survey responses. We first developed a correlation matrix between certain field activities and 

the use of specific technologies to better understand possible 

synergies or interactions between the two categories (table 

17). Values in the correlation matrix range from −1 to 1, 

where values closer to 1 suggest that users are more inclined 

to use a specific technology, values closer to −1 suggest that 

users are less inclined to use a specific technology, and values 

close to 0 suggest little correlation, either positive or negative, between the two field activities or specific technologies 

on the axes of the matrix. For example, according to table 

17, there is a strong, positive correlation (0.504) between the 

use of PDAs and the creation of specialized scripts for PDAs, 

indicating that users of PDAs are also more inclined to create 

their own PDA scripts.

Data format

Topographic

map

Aerial

photography

ElevationBathymetryLand

use

LandcoverGeology

Soils HydrologyRoads Structures

Geographic

names

Paper map------------------------ 51 30 28 11 14 13 17 11 21 44 26 31

Digital format ------------------- 28 32 29 11 18 19 17 15 16 27 13 19

Would use if available---------- 6 14 12 8 14 13 14 14 14 9 11 10

Don’t use ------------------------ 9 14 15 31 22 24 25 30 20 6 16 13

No response --------------------- 6 10 16 40 32 32 27 30 29 15 34 27

Either paper map, digital

format, or would use if

available.

(86) (76) (70) (30) (46) (45) (48) (40) (51) (80) (50) (60)

Table 16. Uses of geospatial datasets by survey participants.

[All values in percent]
14 Opportunities and Needs for Mobile Computing Technology to Support U.S. Geological Survey Fieldwork

Correlation values vary greatly, depending on the field 

activity and specific technology. On the basis of results of 

our statistical analysis, only weak correlations were obtained 

between researchers who collect samples in the field and the 

use of a GPS receiver (−0.014), paper maps (−0.030), a PDA 

(−0.023), a tablet PC (0.054), a laptop computer (0.096), a 

GIS (-0.092), or remotely sensed imagery (−0.074). Other 

than a slightly positive correlation with paper maps (0.194), 

the same weak correlations were obtained between researchers who record field observations and the same specific 

technologies. Although weak correlations were obtained 

in responses related to field technology and recording field 

observations or collecting samples, relatively strong correlations were obtained between researchers who map 

environmental features in the field and the use of a specific technology. In addition to a strong correlation (0.504) 

between the use of a PDA and the creation of specialized 

scripts, other moderately strong positive correlations were 

obtained between researchers who map environmental features in the field and the use of remotely sensed imagery 

(0.351), a GIS (0.337), paper maps (0.233), and a GPS 

receiver (0.206). For these same researchers, a slightly positive correlation (0.157) was obtained for tablet-PC use, and a 

slightly negative correlation (−0.107) for PDA use, suggesting 

opportunities for the USGS in the development and implementation of tablet PCs for field mapping.

In addition to examining the potential correlations 

between certain field activities and the use of a specific technology, we also investigated whether the number of years of 

field experience correlates with field activities and (or) the use 

of a specific technology. Positive correlations may help guide 

technology development, outreach, and training efforts. We 

first compared the mean numbers of years of field experience 

between survey participants who answered “Yes” or “No” to 

the question whether they conducted certain activities or used 

specific technologies. Results (table 18) suggest that those 

scientists who map environmental features, use paper maps, 

use a tablet PC, use a field computer, or use remotely sensed 

imagery may have more years of field experience, whereas 

those who collect samples, use a GPS receiver, use a PDA, 

write their own scripts or applications, or use a GIS may 

have slightly fewer years of field experience. Because only 

one respondent did not record field observations, this activity 

cannot be fairly commented upon, except to say that apparently scientists at all levels of field experience do so.

None of the differences between means listed in table 

18, however, were particularly striking, and no obvious 

trend is evident toward greater or lesser technology use, 

Collect

samples?

Record

field

observations?

Map

features?

Use

a GPS?

Use

paper

maps?

Use

a PDA?

Use

a tablet

PC?

Developed

scripts?

Use

a laptop

computer?

Use

a GIS?

Use

remotely

sensed

imagery?

Collect

samples? 1.000

Record

field

observations?

0.285 1.000

Map

features? 0.038 0.124 1.000

Use

a GPS? 0.014 0.035 0.206 1.000

Use

paper

maps?

0.030 0.194 0.233 0.069 1.000

Use

a PDA? 0.023 0.119 0.107 0.221 0.057 1.000

Use

a tablet

PC?

0.054 0.042 0.157 0.054 0.069 0.005 1.000

Developed

scripts? 0.046 0.057 0.111 0.189 01119 0.504 0.116 1.000

Use

a laptop

computer?

0.096 0.059 0.017 0.116 0.069 0.090 0.100 0.130 1.000

Use

a GIS? 0.092 0.067 0.337 0.371 0.203 0.057 0.116 0.046 0.166 1.000

Use

remotely

sensed

imagery?

0.074 0.091 0.351 0.030 0.217 0.091 0.106 0.039 0.079 0.129 1.000

Table 17. Correlation matrix between field activities and use of specific technologies.
15

information use, or field activity with regard to years of 

experience. To test whether or not the differences in means 

listed in table 18 are statistically significant, we ran two-sample 

t tests. At the α=0.05 confidence level, few of the means were 

statistically different (table 19), suggesting that increasing 

numbers of years of field experience does not generate either 

a strong inclination or disinclination to conduct certain field 

activities or use specific technologies. The exceptions to this 

general conclusion were mapping of environmental features 

in the field and use of tablet PCs while in the field, for each 

of which the mean age of “Yes” respondents was significantly 

higher than that of “No” respondents, suggesting that individuals with more years of field experience were more inclined to 

map environmental features in the field and to use a tablet PC. 

One possible explanation may be functional differences in staffing responsibilities within a project, where study sites may be 

established by senior researchers and then monitored by junior 

researchers. In other words, senior researchers map environmental features, while junior researchers collect site-specific 

data.

Finally, survey participants were asked in an open-ended 

question for additional comments related to mobile-computing 

technology to support USGS fieldwork (see app. A, question 

29). In general, respondents were highly supportive for mobilecomputing technology at the USGS. Many fieldworkers are 

already using it and are interested in sharing their success stories and venting their frustrations with other USGS scientists. 

Respondents believed that better coordination and communication on the development and implementation of mobile-computing technology is needed both inside and outside the USGS. 

In addition, some respondents requested training opportunities 

related to the use of this technology.

 Although many respondents were supportive of its further 

promotion, respondents also expressed concerns with mobilecomputing technology at the USGS, primarily centered on the 

cost, weight, battery life, ease of use, and ability to withstand 

rugged field conditions. Many respondents expressed the need 

for tools that are simple enough to be modified in the field by 

researchers themselves without having to rely completely on 

specialized information-technology (IT) personnel. They also 

stated that the USGS should not pursue a common field-datacollection standard, thereby limiting researchers’ efforts, or lock 

into one specific technology, because the private sector is creating new technology everyday. Several respondents expressed 

a concern that certain technologies or tools will be mandated 

by the bureau, and they were clearly resistant to that idea. To 

fully embrace new technology, respondents were also interested 

in access to specific digital data, such as base-map resources, 

weather data, real-time streamflow conditions, high-resolution 

stereopair aerial photographs, and gauging-site maps. Finally, 

many respondents expressed a concern about data backup and 

digital archiving and stated that paper-copy backups are critical.

Discussion

Fieldwork is the foundation for much USGS research, 

whether monitoring habitats, determining water quality, 

delineating wetland boundaries, or mapping geologic features (table 3). Regardless of topic, respondents record field 

observations, take samples, and map environmental features 

in the course of their USGS research—all activities where 

PDAs and tablet PCs could simplify data entry, database 

management, and field analysis.

Survey results suggest that although current use of these 

technologies is low, a potential exists for greater use of 

mobile-computing technology at the USGS. Results reveal 

many opportunities for technology sharing, whether easier 

access to common digital datasets or specialized scripts and 

data-entry forms. Although digital datasets already exist for 

many of the types and formats listed by respondents (table 

16), evidently more work needs to be done to facilitate the 

use of these datasets by those USGS researchers who traditionally have not used a GIS and are unfamiliar with the 

associated file types. Additional data-delivery mechanisms 

that easily integrate with mobile-computing technology 

need to be developed for The National Map and Geospatial 

One-Stop to support USGS scientists. This work could, in 

addition, push the frontiers of geospatial archives like The 

National Map by framing it within a dynamic and mobile 

data delivery and collection system.

Nonetheless, many USGS scientists are quite comfortable with mobile-computing technology and GIS usage and 

are already developing specialized scripts for their research 

(see app. A). These more advanced users of mobile-computing technology now need mechanisms for sharing these 

scripts with those who do similar fieldwork, as well as a 

forum to support future script development. A potential 

next step for the USGS is to create an accessible archive of 

all current specialized scripts for PDAs and tablet PCs—a 

Web site that describes installation, use, lessons learned in 

the field, and the code—so that interested users have the 

opportunity to obtain, install, and modify scripts to fit their 

particular needs. Creation of this archive and virtual forum 

addresses the second objective for this study, which was 

to foster a community of technology developers and users. 

Survey participants already serve as a starting point for a 

larger and more coordinated group.

With regard to new areas for technology development 

and implementation, survey results suggest great opportunities for the use of PDAs and tablet PCs in USGS fieldwork. 

Most respondents spend significant periods of time in the 

field recording observations, collecting samples, and mapping environmental features but are not yet widely using 

mobile-computing technology. Many respondents still are 

primarily sketching field observations and maps in notebooks, on aerial photographs, and on topographic-map 

sheets. Some USGS scientists may basically be unaware of 

this new technology. For these scientists, simple scripts and 

training opportunities to showcase the benefits of mobilecomputing technology may increase its use and improve the 

efficiency and data consistency of USGS science. In addition, some researchers may know that a technology exists but 

not understand its full functionality, because survey results 

suggest that many fieldworkers are using tablet PCs just as 

larger versions of a PDA. For other researchers, mobile-com-
16 Opportunities and Needs for Mobile Computing Technology to Support U.S. Geological Survey Fieldwork

puting technology may not yet be at a level where it could 

be beneficial to them, possibly because it is too heavy or 

not rugged enough. For those researchers, the USGS should 

continue discussion on the potential for mobile-computing 

technology as the technology continues to become more 

sophisticated, more affordable, and more rugged. In this way, 

the USGS can position itself for more rapid adoption of this 

technology as the price, durability, and flexibility of these 

devices become more suitable for bureau purposes. As for 

specific opportunities, survey results suggest that applications 

which would have the greatest benefit for the bureau are additional PDA scripts for monitoring water quality and tabletPC scripts for geologic, ecologic and landcover mapping. 

Another next step for promoting mobile-computing technology at the USGS is to identify research areas that already 

have protocols for field-data collection; these protocols may 

easily be transferable to a digital format where mobile-computing solutions may be simple to develop and implement.

Overall, respondents support the use of mobile computing technology at the USGS and are interested in training opportunities and further discussions related to data 

archiving, access to additional digital data types, and technology application. Respondents were clear that they were seeking assistance in script development, training opportunities, 

and the ability to archive digital data but not in the bureauwide adoption of technology or data-collection standards 

if they do not already exist. Respondents seem hesitant to 

lock themselves into one technology or brand but instead 

seek assistance in navigating the multitude of private-sector 

options and in developing multiplatform scripts. With the 

ever-changing nature of technology, researchers seek help 

in distilling the options and information related to mobile 

computing and in developing solutions that are cost effective, 

light, long lasting, and modifiable by researchers and not just 

by IT specialists.

Survey results suggest that the USGS should determine 

where enterprise approaches to mobile-computing technology and better coordination of current application efforts are 

needed. Currently, only the WRD has an organized Field Computer Interest Group to support its researchers; the Biological 

Resources, Geology and Geography Disciplines all have only 

isolated efforts by individual researchers. Because so many 

USGS scientists spend significant portions of the year in the 

field with small field crews, they are already devoting limited 

financial and human resources to developing time-saving technologies. The USGS could assemble a permanent technical 

team to support fieldworkers in their mobile-computing-technology needs. This team could also serve to coordinate and 

connect the various development projects across the bureau. 

Greater use of mobile-computing-technology could not only 

improve fieldwork but also reduce the amount of—or, at 

least, the time required for—postfieldwork data processing. A 

bureauwide investment could help minimize errors and redundancies in these efforts across the Nation.

On the basis of survey results, we recommend the following next steps with regards to the use of mobile-computing 

technology at the USGS:

• Determine additional issues and needs for those not 

adequately represented in this study, namely, individuals from the Central and Eastern Regions and from 

the Geologic Discipline;

• Compile an accessible USGS archive of all current 

specialized scripts for PDAs and tablet PCs;

• Determine specific needs for specialized USGS 

scripts related to water-quality monitoring for PDAs 

and geologic, ecologic, and landcover mapping for 

tablet PCs;

• Create data-delivery mechanisms within data archives 

like The National Map and Geospatial One-Stop that 

easily integrate with mobile-computing technology, 

so that USGS researchers can access critical digital 

datasets, including topographic maps, roads, aerial 

photographs, and elevation data; and

• Provide training opportunities on the use of mobilecomputing technology, specifically PDAs and tablet 

PCs, to support USGS field research.

By devoting bureauwide resources to coordinate the 

development and implementation of mobile-computing technology, the USGS will help its scientists devote more time 

to their research, rather than to the logistics of their research. 

Thus, scientists can fully focus their attention on work that 

furthers the USGS’ mission. We hope that this survey serves as 

a starting point toward attaining this goal.

Collect

samples?

Record field

observations?

Map

features?

Use

a GPS?

Use

a paper map?

Yes No Yes No Yes No Yes Yes No No

Mean (years)------------- 15.43 17.29 15.76 1.00 17.30 13.66 20.20 13.92 15.83 14.61

No. of responses--------- 122 17 136 1 80 53 15 26 86 28

S.d------------------------- 9.34 10.32 9.46 N/A 9.17 9.12 9.27 6.88 10.16 10.06

Table 18. Mean number of years of USGS field experience between respondents who answered “Yes” or “No” on field activities and use of 

specific technologies.
17

Conclusions

To better understand the opportunities and needs for 

mobile computing technology at the USGS, 144 researchers 

answered 65 questions about fieldwork goals, activities, and 

conditions, technology and data to support research, and postfieldwork data processing and analysis. Primary conclusions 

from this study include the following:

• Some mobile-computing-technology devices are 

already commonplace, such as digital cameras and 

GPS receivers, whereas others are not, such as 

PDAs and tablet PCs;

• The potential for PDA use in the USGS is high 

because 99 percent of respondents record field 

observations (primarily environmental conditions and 

water-quality data) and 87 percent of respondents 

take samples in the field (primarily water-quality 

data, water samples, and sediment/soil samples);

• The potential for tablet PC use in the USGS is also 

high because 59 percent of respondents map environmental features in the field, primarily by sketching in field notebooks, on aerial photographs, or on 

topographic-map sheets;

• Demand is high for a bureauwide effort to coordinate and develop efficient mobile-computing technology for USGS scientists because many researchers devote much time to fieldwork;

• Mobile-computing technology could also minimize the significant amount of time and cost that 

researchers devote to postfieldwork data processing; 

and

• Respondents support greater use of mobile-computing technology at the USGS and are interested in 

training opportunities and in further discussions 

related to data archiving, access to additional digital 

data types, and technology development in general.

Acknowledgments

This study was supported by the Advanced GIS-Based 

Field Mapping Techniques project managed by the USGS’ 

Geographic Analysis and Monitoring (GAM) and Cooperative Topographic Mapping (CTM) programs. We thank 

Sean Stone and Alan Vaughan for their assistance in creating the project Web site and the online survey, and Doug 

Muchoney, GAM Program Coordinator, and Tom Gunther 

of the GIO Enterprise Information Office for their assistance in advertising the survey within the bureau. Chris 

Brown and Sara Compton supplied the photograph of 

Zsolt Kahanzca in figure 1B; all other photographs were 

taken by Nathan Wood. Susan Benjamin, Tom Gunther, 

and Chris Brown provided insightful reviews of the manuscript.

References Cited

Edmondo, G.P., 2002, Digital geologic field mapping 

using Arcpad, in Soller, D.R., ed., Digital Mapping 

Techniques ‘02—workshop proceedings: U.S. 

Geological Survey Open-File Report 02–370, p. 129–

134.

Pavlis, T.L., and Little, Jason, 2001, Using handheld 

personal computers as field data collection tools: 

Use

a PDA?

Use

a tablet PC?

Write

scripts?

Use

a computer?

Use

a GIS?

Use remotely

sensed

imagery?

Yes No Yes No Yes No Yes No Yes No Yes No

14.54 16.18 20.20 15.12 13.92 15.83 16.11 13.75 14.99 16.54 17.82 14.71

46 90 15 116 26 86 104 32 92 39 56 73

7.46 9.97 9.27 9.06 6.88 10.16 9.60 9.14 8.92 10.03 9.72 9.11

Years versus: N t-stat P-value

Collecting samples ---------------------------- 139 0.7596 0.4488

Recording field observations1 ---------------- N/A N/A N/A

Mapping features2 ----------------------------- 133 2.2456 0.0264

Use of GPS------------------------------------- 138 1.2584 0.2104

Use of paper maps----------------------------- 135 -0.7351 0.2104

Use of PDA ------------------------------------ 136 0.9798 0.3289

Use of tablet PC2 ------------------------------ 131 2.0375 0.0436

Writing scripts and (or) applications -------- 112 0.8932 0.3737

Use of computer ------------------------------- 136 1.227 0.222

Use of GIS--------------------------------------- 131 0.8757 0.3828

Use of remotely sensed imagery ------------- 129 1.8657 0.0644

Table 19. Results of statistical t test comparing years of USGS field 

experience with field activities and use of specific technologies.

Table 18. Mean number of years of USGS field experience between respondents who answered “Yes” or “No” on field activities and use of 

specific technologies—Continued.

1

All respondents but one answered “Yes" to this question.

2

Significant at the 5-percent-confidence level.
18 Opportunities and Needs for Mobile Computing Technology to Support U.S. Geological Survey Fieldwork

6) Do you use a paper map in the field?

76% Yes

22% No

3% N/A

a) What type(s) of map(s) do you use, and what do 

you use them for? (open ended; see table 8)

b) What map scale do you typically use? (open 

ended; see table 8)

7) Do you take photographs while in the field?

97% Yes

2% No

1% N/A

a) Do you use a digital or film camera? (write in; 

 similar responses are into categories)

90% Digital

4% Film

2% Both digital and film

4% No response

8) Do you use a personal digital assistant (PDA) in the field?

33% Yes

64% No

3% N/A

a) How do you use the PDA in the field? (open 

ended; see table 9)

9) Do you use a tablet PC in the field?

11% Yes

83% No

6% N/A

a) How do you use the tablet PC in the field? (open 

ended; see table 9)

10) Have you developed specialized scripts or applications for 

your field use of PDAs or tablet PCs?

19% Yes

62% No

19% N/A

a) What are the main features of your script or 

application? (open ended; raw responses)

 • Calculations for navigation, fuel consumption, 

heat index, tides and currents, misc. [sic] others.

• Checking accuracy of data entered on forms.

some lessons learned in the school of hard knocks in 

the Wingate Wash project and related projects using 

fieldlog/fieldworker software exported to ArcInfo, in

Soller, D.R., ed., Digital Mapping Techniques ‘01—

workshop proceedings: U.S. Geological Survey OpenFile Report 01–223, p. 115–121.

Stoller, D.R., ed., 2003, Digital Mapping Techniques 

‘03—workshop proceedings: U.S. Geological Survey 

Open-File Report 03–471, 262 p. [URL http://pubs.

usgs.gov/of/2003/of03-471/]

Williams, V.S., 2001, Conclusions from four years 

collecting digital map data using a PDA, in Stoller, 

D.R., ed., Digital Mapping Techniques ‘01—workshop 

proceedings: U.S. Geological Survey Open-File Report 

01–223, p. 125–126.

Appendix A. Survey Questions and 

Results

The following section summarizes the survey questions 

asked in this study. Response options for scripted questions 

and the percentage of each option received are also provided; 

open-ended questions are discussed in the main body of this 

report.

Fieldwork

1) What is the primary objective when you do fieldwork 

for your various projects? (open ended; see table 3)

2) Do you collect samples in the field?

87% Yes

13% No

0% N/A

a) What type of samples do you collect? (open ended; 

see table 4)

3) When at a fieldsite, do you record field observations?

97% Yes

1% No

1% N/A

a) What type of field observations do you record? 

(open ended; see table 4)

4) Do you map features when in the field?

57% Yes

39% No

4% N/A

a) What do you map? (open ended; see table 5)

b) How do you do map features? (open ended; see 

table 5)

5) Do you use a Global Positioning System (GPS) receiver in 

the field?

88% Yes

12% No

1% N/A

Use of GPS No. of responses

76

28

5

5

3

1

1

1

1

1

1

Location/navigation--------------

Garmin product-------------------

Trimble product-------------------

Various-----------------------------

Rockwell product-----------------

“See previous answer” ----------

Ashtech product-------------------

Card in pocket PC----------------

Sutron product--------------------

Teletype product------------------

Setting time stamp----------------

a) What is the GPS receiver used for in the field? 

(open ended; similar responses are into categories)
19

• Collect data in the way we need the data collected.

• Created specialized scripts within Pendragon to 

address a variety of situations. Not sure what you 

mean by “main feature.”

• Creation of forms.

• Data dictionaries for Trimble GeoXT.

• Datalogger and telemetry programs.

• Downloading data from a serial port, converting it 

from hex to text, and giving a quick summary of 

the file.

• Dropdown lists, GPS download, automatic look 

up of next ID number, weight and length range 

proofing.

• Equipment inventory—“standardized” field 

notes—instrument calibration tracking...

• Excel document to store stream-discharge-meassurement data and to automatically compute the 

results.

• Field notes and well/pump electric/gas powercoefficient computations.

• For previous work (FWS Kodiak NWR brownbear study) I developed scripts in Pendragon soft 

ware for use on Palm OS PDAs to enter brown-

 bear observations and synchronize them to a MS 

Access database.

• GPS connectivity, record observations, Java so it 

runs on Pocket PC or Linux PDAs, easy for me to 

modify or adapt to new projects.

• I am the author of PCFF. This software allows 

you to collect and process water-quality data, 

 record meter-calibration information, print out 

required forms, and allows for the automated 

update of the data into the NWIS database.

• Menu-driven forms and combo boxes.

• Pendragon forms.

• Plot lat/long coordinates in ArcMap.

• Print field notes, produce database input file to 

log in sample.

• Question is too broad for a mere ecologist. We 

have data fields, and we enter data.

• Stated in previous answer.

• Streamflow calculator.

• To calculate levels data.

• Unix scripts to process GPS data: Excel spread

sheets to help reduce gravity data. Written for 

laptop, not tablet PC or PDA.

• USGS ARMI program has developed numerous 

specific scripts to allow our specialized use of

PDAs in the field. The San Diego field station 

has been instrumental in this and would be able to 

provide details of these scripts.

• We use Pendragon forms on a Palm OS PDA to 

store our field measurements and observations.

• We worked hard to develop script to use ARC on a 

Pocket PC. The idea was that we could import a 

digital map image (using MRSID) and the Pocket 

PC would use the attached GPS to show our 

 location and record the location of birds that we 

observed (by pointing on the screen to the location 

of the bird that we saw). At each screen contact, a 

popup menu would come up that would allows us to enter 

species names, etc., for each data record. After a year 

of trial, we gave up because it was still faster and more 

efficient to use hardcopy maps in the field and enter the 

data into ARC later. I think that this method has general 

promise, but we would have needed someone with real 

programming skills to get it fast and bug free.

11) Do you use a computer while in the field?

74% Yes

24% No

3% N/A

a) What type of operating system does your 

 field computer use?

69% Windows based

1% MacOS based

3% Other—MS–DOS laptop 

27% No response

b) How rugged does this computer need to be?

40% Needs to be waterproof or environmentally sealed

33% Not rugged, would be used in controlled environment

27% No response

12) How many days of the year do you spend in the field?

4% <1 week

27% <1 month

40% 1–3 months

23% 3–6 months

5% >6 months

1% No response

13) What is the average length of an individual field 

investigation?

22% 1 day

44% 1 week

10% 2 weeks

6% 1 month

13% Longer than 1 month

6% No response

14) How many people go with you when you do your 

fieldwork?

28% No one else

33% One person

21% Two people

6% Three people

11% Four or more people

1% No response

15) Do you travel on foot while in the field?

78% Yes

21% No

1% N/A

a) How far do you typically travel on foot away 

from a motorized vehicle? (open ended; 

see table 9)

Office Work

16) After returning from the field, do you (or does someone in 

 your office) enter your field data into a computer system?
20 Opportunities and Needs for Mobile Computing Technology to Support U.S. Geological Survey Fieldwork

b) What is GIS used for in your research? (open 

ended; see table 13)

19) Do you use remotely sensed imagery in your work?

40% Yes

52% No

8% N/A

a) What kind of remotely sensed imagery do you use? 

(write in; similar responses are grouped into 

categories)

68% Yes, I enter the data

22% Yes, someone else enters the data for me

7% No

3% No response

Responses broken down by respondents’ 

USGS discipline:

Category of 

GIS software

No. of

responses

Arc (unspecified) ------------------------- 5

ArcCatalog -------------------------------- 1

ArcGIS------------------------------------- 34

ArcIMS ------------------------------------ 1

ArcINFO----------------------------------- 16

ArcMAP ----------------------------------- 20

ArcPad ------------------------------------- 4

ArcView ----------------------------------- 23

Don’t know-------------------------------- 2

Earthvision -------------------------------- 1

ENVI--------------------------------------- 5

ERDAS Imagine -------------------------- 8

ESRI (unspecified) ----------------------- 7

Fugawi ------------------------------------- 1

Garmin ------------------------------------- 1

Global Mapper ---------------------------- 1

Remote-sensing

type

No. of

responses

Aerial photography---------- 15

Landsat------------------------- 15

DOQ----------------------------- 13

Lidar----------------------------- 11

Satellite ------------------------- 11

Other; N/A --------------------- 6

GOES --------------------------- 4

INSAR-------------------------- 4

MASTER----------------------- 4

ASTER-------------------------- 3

Stage ---------------------------- 3

Hyperspectral ----------------- 2

IR--------------------------------- 2

Quickbird ---------------------- 2

SPOT ---------------------------- 2

Aeromagnetic ----------------- 1

AirSAR------------------------- 1

Altimetry----------------------- 1

AVHRR------------------------- 1

AVIRIS------------------------- 1

Brock’s------------------------- 1

CASI---------------------------- 1

CIR------------------------------ 1

FLIR----------------------------- 1

Hyperion ----------------------- 1

LEO ----------------------------- 1

MOD IS ------------------------- 1

NCEP---------------------------- 1

Radar---------------------------- 1

RadarSat------------------------ 1

SAR-derived DEM----------- 1

Side-scan sonar--------------- 1

Telemetry---------------------- 1

Thermal------------------------ 1

Biological Resources 10 15 5

Geography 11 2 1

Geology 14 4 1

Water Resources 62 11 3

No Yes, someone 

else enters

Yes, I enter the 

data

Respondents’

discipline

a) How many hours are spent doing this task after 

a field session?

24% 1 day

24% 1 week

10% 2 weeks

11% 1 month

8% Greater than 1 month

23% No response

b) What operating system does the computer 

system have?

79% Windows

6% UNIX

1% MacOS

1% Other

12% No response

17) Back in the office, do you record your field observations or mapped features manually on a paper map or 

photo?

25% Yes

68% No

7% N/A

18) Do you use a geographic information system (GIS) in 

your research?

64% Yes

30% No

6% N/A

a) What type of GIS software is used? (open 

ended; similar responses are grouped into 

categories)

ArcMAP ----------------------------------- 20

ArcPad ------------------------------------- 4

ArcView ----------------------------------- 23

Don’t know-------------------------------- 2

Earthvision -------------------------------- 1

ENVI--------------------------------------- 5

ERDAS Imagine -------------------------- 8

ESRI (unspecified) ----------------------- 7

Fugawi ------------------------------------- 1

Garmin ------------------------------------- 1

Global Mapper ---------------------------- 1

GRASS ------------------------------------ 2

IDRISI ------------------------------------- 1

MapInfo------------------------------------ 1

MapTech—Terrain Navigator Pro------ 1

Microimages ------------------------------ 1

Oasis Montaj ------------------------------ 1

PCI Geomatica ---------------------------- 2

Photolink----------------------------------- 1

Surfer--------------------------------------- 1

TOPO! ------------------------------------- 2

All ESRI products ------------------------ 111

All others ---------------------------------- 32
21

b) What do you use it for? (open ended; see table 15)

20) Having certain types of data in the field can sometimes 

make fieldwork easier. Do you currently use any of the 

following types of information in your fieldwork, and 

would you use it if it were available? (see table 16)

Background Information

21) Job title (open ended; see table 1)

22) USGS discipline

22% Biological Resources

10% Geography

0% Geospatial Information Office

14% Geology

54% Water Resources

23) Region

12% Eastern

19% Central

68% Western

1% No response

24) Science Center (open ended; results dropped because most 

responses were incomplete)

25) What type of landscape or environment do you typically 

work in when you are in the field? (open ended; see table 2)

26) How many years have you been doing field research? 

(open ended; see fig. 1)

27) Name (write in; not disclosed to maintain participant 

confidentiality)

28) Phone number (write in; not disclosed to maintain participant confidentiality)

29) Is there anything else you would like to tell us related to 

the development of advanced GIS-based field data-collection 

and mapping techniques for USGS research? (open ended; 

raw responses)

• “One size does not fit all.” Each project is different, 

and the data that need to be collected will vary, especially from researcher to researcher. Tools that are 

loose enough to be configured easily and deployed at 

the researcher level are the best. Simple tools that use 

the conventional computer technology (not the latest) 

are best because the “bugs” have mostly been worked 

out of them. Persons developing these applications 

should be more biologist than programmer or database person or IT type.

• A bottle labeler that would print out barcoded labels, 

along with the verbal sample info from the PDA or 

tablet, could be very useful.

• A GIS map of a gauging site would be very useful. 

Critical points for the station could be precisely plotted, which would aid technicians and others to find 

RMs, etc.

• A system needs to be developed that would incorporate real-time Internet-based GIS of streamflow 

conditions using ArcIMS that could be accessed via 

wireless handheld devices in the field for the access, 

retrieval, and immediate storage of field data. This 

system would be accessible via any Web browser 

(including the PDA browser) and contain basic online 

analytical GIS tools and data for basic query and 

analysis. The handheld devices should be robust and 

standardized with USB and infrared data-exchange 

technology.

• All notes take on field trips are done with pen and 

paper, then they need to be transferred to electronic 

form. Eliminating the paper step would save time.

• Any spatial data collected today most likely will 

involve the use of GPS technology, where the spatial 

data are georeferenced to 3D positions determined in 

real time or determined from postprocessing of the raw 

observational data. The 3D positions (lat, long, and 

ellipsoid height (h) and, if known, the geoid height (N) 

for the work area, elevations (H)), where the accuracy 

required for the positions may be dependent on the 

purpose for the spatial data collected, and distance 

relationship to adjacent spatial data collected in 

the work area. The metadata should include, as a 

minimum, methodology used for the GPS-determined 

coordinates, data/time of measurement (or when 

spatial data are collected), an indication of position 

accuracy (1σ [67%] or 2σ [95%] confidence level), 

the datum (such as for NAD1983 and ITRF2000), 

and other appropriate information required for historical records.

• Better training is needed.

• Cost of data-collection systems is a concern, as well 

as data backup.

• Don’t limit the way people can collect the information they need by writing some policy that no one 

will read anyway.

• For me to use such technology, it would have to be 

very rugged and completely waterproof. It would also 

need to be relatively cheap so I could afford to lose 

one without bankrupting a project budget.

• Foresee PDAs running into PCs with miniaturization—would recommend planning for that eventually 

by not locking in to a certain technology.

• GPS accuracy still presents problems in many environments—forcing reliance on paper maps and past 

experience. We will be developing digital field datacollection protocols as part of interdisciplinary research 

efforts in the Everglades. These will likely make use of 

PDAs. I typically record crucial measurements made by 

specialized instruments (i.e., ceptometers) on paper at 

the same time data are being captured by the device 

because their data logging capabilities remain primitive and are susceptible to failure in harsh physical 

environments.

• Hardcopy prints of aerial photos on photographic 

paper cannot be replaced in the digital world without a great deal of expense and effort. Aerial photos 

are the single biggest and best resource for geologic 

mapping, and must be used stereoscopically. We now 

order aerial photos from other agencies, even though 

the USGS has the biggest and best library. What a 

shame that resource is no longer available!
22 Opportunities and Needs for Mobile Computing Technology to Support U.S. Geological Survey Fieldwork

• I am dismayed by how far behind the USGS is in 

investigating and acquiring modern ways of collecting field data. It is very difficult to get funding 

for items such as tablet PCs, which I think would 

facilitate and streamline data collection. It would 

also be incredibly helpful to have 3D stereo capabilities, such as Stereo Analyst for ArcGIS, and rectified 

aerial photography to use in such systems. We need 

to move away from dedicated PG2 plotter labs to 

desktop capability for 3D mapping and data analysis.

• I am interested in using our field data in conjunction 

with space-based systems.

• I am very pleased with Trimble GPS products for 

fieldwork. The Trimble GeoXT allows a background 

raster and a vector data file with a data dictionary. 

Data collection and management with the GeoXT is 

efficient in the field and in the office.

• I am very satisfied with my system now and believe 

it is more efficient for map production. Support staff 

also critical for me in preparing digital geologic 

maps. Comment: only one choice offered in survey 

questions (e.g., I use both paper and digital topos).

• I feel that there is a definite use for PDAs in science, 

not just in the GIS-based field data collection or 

mapping techniques but for a variety of other reasons. 

However, it has sometimes proven difficult to get 

support from the manufacturers of these machines, so 

it seems that we are individually going at it alone. I 

would like to see a greater push for a more open communication that can involve idea sharing, technical 

advice, etc.

• I found that “off the shelf” GIS software is more user 

friendly in the field. A GIS application that would 

easily synchronize field and office data would be of 

great value.

• I have seen applications in use regarding groundwater wells, their location, and the associated data. 

Impressive!

• I have used PDAs in fieldwork before, and they 

were extremely useful and applicable for the kind 

of research that we do. However, I think that lack 

of money and perceived electronic difficulties may 

hamper the switch from conventional to digital forms 

of data storage.

• I heartily recommend pursuing advanced methods 

for field data collection. We’ve needed more of these 

tools for some time now. The team I’m working with 

is currently interested in acquired a Trimble GeoXT 

for the field. We would like to be able to segment 

imagery using eCognition software, upload the resulting polygons into the device, and use this digital data 

directly in the field to automate collection of all the 

information we’re acquiring.

• I support the effort!

• I would appreciate the availability of a tablet PC 

into which data could be input and later uploaded to 

GWSI, QWDATA, and which has voice-recognition 

software.

• I would love to use a small rugged computer in the 

field daily and compile directly to digital, rather 

than use mylar compilation and digitizing later as a 

substitute. BUT I am a Mac diehard, and the working 

conditions (often driving rain, dirt and rocks on hands 

and all over pack, etc.) make it hard to believe any 

system will be rugged enough to survive (whereas a 

plastic map case can be hidden under your rainjacket 

to write on and bends when large samples impinge on 

it in your backpack while falling 6′ down a cliff.)

• I’d be more likely to use pen tabs more consistently 

for field mapping if they were a little lighter, more 

rugged, screens worked better in broad daylight, and 

batteries held charges longer.

• I’ve spent more time developing PDA applications 

for other projects, primarily Landfire, than for my 

own fieldwork. In working with other scientists, I’ve 

found that a full-blown GIS app is usually far more 

than is needed in the field. The ability to compile 

GPS, date/time, field photos, and observations 

digitally into a database is the most important aspect. 

Having GPS-connected digital maps and imagery in 

the field is a bonus. I was unable to attend the San 

Diego conference, but I am very interested in the 

development side of this issue.

• If you develop wonderful programs for us to use in 

the field, please do so, to the greatest extent possible, 

in relatively low level programs that we can have 

easy access to while in the field. We DON’T want to 

enter data in ArcPad or ArcMap while in the field. 

We will use something like ArcPad for recording a 

bit of navigation/location data, but the data WILL 

be entered in something else. We have been working on small standalone programs in something like 

HanDbase for recording vegetation data. I like the 

idea of recording in Access, using specialty Visual 

Basic programs (or something similar). The benefit of 

recording data in what I’m calling low-level software 

programs is that we can modify things in the field. If 

we notice something that was not anticipated, we can 

add a field or category on the fly and collect that data. 

If we have a data-entry program written by someone 

in Tim-buck-too using some high-level and very 

expensive program, then we cannot add the additional 

field. Having a data-entry program that is easily 

modifiable in the field would be the bee’s knees.

• It has been my experience that a wide range of reliability exists in equipment, software applications, 

data capture, etc. I am interested in developing 

customized methods for recording observations and 

landcover classifications

• It has got to work under heavy tree cover and brush 

cover and in narrow canyons, or we won’t use it, as it 

causes more trouble than it solves when locations are 

degraded owing to poor satellite constellations. Also, 

I can drop a notebook and pick it up again, and it will 

still work. Most PDAs are not that durable. And a 

paper map does not pose the continuing archival chal-
23

more rugged is better.

• American Fisheries Society’s Computer User Section 

has PDA scripts available for download for fisheries 

applications.

• The cost! We would do much more with digital data 

collection if there was money to do it. We could be 

much more efficient if there was a way to upgrade 

our equipment!

• There exists some neat software which incorporates 

orthoquad/aerial imagery with a digital camera that 

has a GPS device embedded in it. The user takes a 

pic. [sic], and it shows up on the map with date/time/

aspect/link to photo.

• Trimble currently has a great hand-held device for 

compiling field data needed for many of our landcover- and vegetation-mapping projections. This 

device would be a great addition and timesaver for 

the work we do.

• Moving toward the tablet PC. Will replace the laptop 

or at least supplement it. I am currently alpha-testing 

one now.

• We use a hand-held device from YSI called a 650. 

This device is able to log data from a YSI probe and 

a GPS unit at the same time. This is a very valuable 

tool for gathering data for salinity maps.

• We wish that we could have access to USGS basemap resources to better analyze our results.

• We’re excited by the possibilities of tablet PCs and 

tablet Collector-PC base stations. We’d like to be able 

to log in our samples from the field, using a phone.

• Weather information is the single most important 

determinant to our ability to work safely in the 

marine environment.

• Whatever device I take to the field must tolerate a lot 

of dust, heat, rainstorms, dropping on rocks; it must 

be easy and cheap to replace; it must be lightweight 

(no more than 1 lb); it must have a large screen (8 by 

8 in. at least). Unless it meets these criteria, it’s useless and a waste of time.

• Like to see an easily usable system for taking standard 

notes for a gauge visit and discharge measurement on 

PDA.

• Yes, we need better development of sunlight suitable 

screens, EXTREMELY portable (lightweight), durable 

(rain, surf, heat), and multipurpose units to collect 

GPS data and record or map information. Good if 

linked to camera, too, and having good data-entry 

capabilities. Currently, we use codes to facilitate digital entry in the field on small units, or use the reliable 

old notebooks.

lenges of the digital age, where so-called “permanent” media is woefully temporary. The archive problems are real and are not being adequately addressed 

by those who would rush us into a digital age. Digital 

information is great for timely transmission back to 

the bosses at the office, but hardcopy backup is essential, in my opinion. I achieve that with paper maps.

• Nice to have easier access to high-resolution, contactprint DOI aerial photography; closing EROS was a 

great loss.

• Just to mention that we used mixed platform. Most of 

the GIS and data collection is Windows based. I use 

Mac OS for regular work.

• Lack of availability of stereopair aerial photographs 

seriously impairs the ability to perform geomorphic 

research. Until the resolution of the printed stereopair 

aerial photograph can be fully reproduced digitally (printed copies are viewed with magnifying 

glasses, not simply with the unaided eye), geomorphic research must rely heavily upon film-based 

printed aerial photographs. The loss of the ability of 

the USGS to provide these has seriously hindered 

research where these photographs are unavailable 

from other agencies.

• No, but I will like to see what comes of your survey 

and what others are using. I believe we could all benefit by being better informed as to what other USGS 

researchers are using in their work in this regard.

• Nope, just hope we get to use it soon. Using a PDA 

would cut down on the amount of data-entry error, 

increase the legibility of data entry, and reduce the 

risk of failing to fill out data forms in their entirety.

• Regarding locating thermal features in Yellowstone 

Park: many features are within meters of each other, 

so high GPS accuracy is needed; our Garmin 3+ unit 

is sometimes of marginal accuracy for this purpose.

• Sometimes high-tech options aren’t really needed 

in the field. Good old accurate and careful sample 

collection is really the best type of fieldwork. I used 

a PDA once, and it lost all my data, so now I use my 

old dependable field notebook.

• Tablet PCs and Ipaqs/PDAs with ArcPad, or similar 

software, are still a bit too expensive and not rugged 

enough for my needs. I use a low-end, inexpensive 

PDA for data entry because the batteries last a long 

time, and if I lose it or it breaks, it’s no big loss, plus 

it’s very lightweight. Ruggedized tablet PCs would 

be great if they didn’t weigh so much and have such 

short battery lives. Much of my work requires bushwhacking through forests, so lighter, cheaper, and 
24 Opportunities and Needs for Mobile Computing Technology to Support U.S. Geological Survey Fieldwork

Appendix B. Survey Cover Letter

Supervisors, managers, team leaders: Please provide a copy of this memorandum to your 

employees without access to electronic mail.

MEMORANDUM

June 23, 2005

To: All USGS Employees

From: Doug Muchoney, Geographic Analysis and Monitoring Program

Tom Gunther, Enterprise Information Program

Nathan Wood, Western Geographic Science Center, Geography Discipline

Subject: Field Data Collection and Mapping Survey

The Geographic and Analysis Monitoring Program is conducting a survey to document 

field data collection and mapping activities being conducted throughout the USGS and to 

identify mobile-computing opportunities for field researchers. This survey is part of a larger 

project to develop field data-collection and mapping tools that capitalize on emerging technologies, such as personal digital assistants (PDAs) and tablet PCs.

Your responses to this survey will help us identify ways in which PDAs and tablet PCs 

can be used in combination with software and scripts to facilitate data entry, management, 

and integration, as well as mapping and postfieldwork processing and analysis. It will identify 

opportunities for sharing and measure support for a bureauwide team to develop the concept 

of mobile computing as an aid to USGS field science.

We ask USGS employees involved in fieldwork activities in all disciplines and all regions 

that are using or planning to use mobile-computing technology to complete this survey. The 

success of this study and the future development of advanced GIS-based tools are highly 

dependent on the cooperation and involvement of other USGS scientists. We thank you for 

your contribution to this effort.

The survey is brief (~15 minutes) and can be filled out by linking to the following URL: 

http://internal.wr.usgs.gov/geography/FieldsToolsSurvey/.
Wood and Halsing—Opportunities and Needs for Mobile-Computing Technology to Support U.S. Geological Survey Fieldwork—U.S. Geological Survey Circular 1299

Printed on recycled paper