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Visual analytics for ManageMent

This book provides students with an in-depth understanding of the concepts, frameworks and processes used to analyze and present visual data for better decision-making. Expert contr ibutors provide guidance in translating complex concepts from large data sets and how this translation drives management practice.

The book’s first part provides a descriptive consideration of state-of-the-art science in visual design. The second part complements the first with a rich set of cases and visual examples, illustrating development and best practice to provide students with real-world context. Through their presentation of modern scientific principles, the editors inspire structured discussions of audience and design, recognizing differences in need, bias and effective processes across contexts and stakeholders.

This cutting-edge resource will be of value to students in business analytics, business communication and management science classes, who will learn to be capable managers through the effective and direct visual communication of data. Researchers and practitioners will also find this an engaging and informative book.

Elliot Bendoly is the 2015 Academy of Management’s Operations Management Division Distinguished Scholar and a Full Professor in the Fisher College of Business at The Ohio State University, USA.

Sacha Clark is the founder of Mulsanne Management, USA, a firm that assists clients with business strategy development and implementation, productivity enhancement and technology exploitation, and change and transformation management. He has worked with leading companies such as FedEx, General Motors, IBM, Mitsubishi, Procter & Gamble, Pennzoil, Shell, Samsung and WestPac.

In no other source will you find so much helpful knowledge and so many examples of the psychological and decision-making context behind visual representations of data. Serious visual analytics professionals need to be aware of and address these issues if they wish to be effective.

Visual analytics for ManageMent

Translational Science and Applications in Practice

First published 2017 by Routledge

711 Third Avenue, New York, NY 10017

and by Routledge

2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN

Routledge is an imprint of the Taylor & Francis Group, an informa business © 2017 Taylor & Francis

The right of Elliot Bendoly and Sacha Clark to be identified as the authors of the editorial material, and of the authors for their individual chapters, has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988.

All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers.

Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe.

Library of Congress Cataloging in Publication Data

A catalog record for this book has been requested

ISBN: 978-1-138-19071-9 (hbk)

ISBN: 978-1-138-19072-6 (pbk)

ISBN: 978-1-315-64089-1 (ebk)

Typeset in Bembo by codeMantra

7

Erika

Sian Joel-Edgar, Lei Shi, Lia Emanuel, Simon Jones, Leon Watts, Linda Newnes, Stephen Payne, Ben Hicks and Stephen Culley 9

Randi Foraker

10 Optimization,Visualization and Data Mapping

James W. Hamister, Michael J. Magazine and George G. Polak

11

Paul Rosenthal, Linda Pfeiffer, Nicholas Hugo Müller and Georg Valtin

R.1

4.1

5.3

5.4

5.5

6.1

6.2

6.3

6.5

6.6

7.1

7.2

7.3

7.4

7.5

11.1 Work Position of Two Air Traffic Controllers; Area Control Center in Langen, Germany

in

11.4 Electronic Flight Strip Display with Touch Input; Area Control Center in Bremen, Germany

11.5 Strip’TIC: Mixing Electronic Flight Strips with Augmented Paper

11.6 Staging and Measurement at the

11.7 Visual Stimuli Used for Studying Air Space Encodings: (a) Two-dimensional Visualization of One Air Space Sector with a Side-by-side Comparison of Two Different Visual Encodings for Aircraft, Their Height, and Speed; (b) Empty Sector with Interactive Aircraft Symbols, which Can Be Used to Reproduce the Previous Situation

11.8 A Paper Prototype, Produced in the Design of the New

12.1

contributors

(listed in chapter order)

elliot bendoly

Fisher College of Business, The Ohio State University

sacha clark

Mulsanne Management Ltd

alice comi

Department of Management Studies, Aalto University School of Business

Martin eppler

Institute for Media and Communications Management, University of St. Gallen

l. alberto franco

Management Science and Operations Management, Loughborough University

Jukka Huhtamäki

Tampere University of Technology

Martha g. russell

mediaX, Human Sciences Technology Advanced Research Institute, Stanford University

Kaisa still

VTT Technical Research Centre of Finland

xii Contributors

Jeremy c. adams

Strategic Planning and Execution at Cardinal Health

erika braun

Design Research and Development Graduate Program,The Ohio State University

sian Joel-edgar

Computer Science, University of Bath

lei shi

Engineering and Design, University of Bath

lia emanuel

Computer Science, University of Bath

simon Jones

Computer Science, University of Bath

leon Watts

Computer Science, University of Bath

linda newnes

Engineering and Design, University of Bath

stephen Payne

Computer Science, University of Bath

ben Hicks

Engineering Systems and Design, University of Bristol

stephen culley

Engineering and Design, University of Bath

randi foraker

Division of Epidemiology, The Ohio State University

James W. Hamister

Raj Soin College of Business, Wright State University

Michael J. Magazine

Carl H. Lindner College of Business, University of Cincinnati

george g. Polak

Raj Soin College of Business, Wright State University

Contributors xiii

Paul rosenthal

Department of Computer Science, Technische Universität Chemnitz

linda Pfeiffer

Department of Computer Science, Technische Universität Chemnitz

nicholas Hugo Müller

Department of Humanities, Technische Universität Chemnitz

georg Valtin

Department of Humanities, Technische Universität Chemnitz

David staley

Department of History, The Ohio State University

shaun fontanella

Center for Urban and Regional Analysis, GIS Graduate Program at The Ohio State University

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Preface

We’re drowning in a sea of digital content, data and information. A 2015 study (Virtualization & Cloud News, April 5, 2105) determined that every second, the world generates nearly 30 gigabytes (GB) of data—that’s enough to fill ten million Blu-ray discs per day. Those discs stacked on top of one another would be as tall as four Eiffel Towers, and that growth rate is accelerating by the moment. By the same token, technology vendors are also flooding the market with ever more capable, flexible data analytics and visualization tool sets that are becoming more and more accessible and affordable.

That should be good news, but with an abundance of technologies to apply to this tidal wave of content, the bad news is that it’s all the more important to address the perennial challenge of what data to present to whom and how to choose the right representational means.

The challenge of designing the right data and information presentation on an automobile dashboard is a familiar analogy. It helps to illustrate some of the trade-offs inherent in providing professionals and executives with the right dashboard format and content to help them make good decisions. Today’s automobile technology is rapidly evolving to the point where drivers already have a palette of choices for custom configuring their individual dashboards (and, in some cases, heads-up displays).

However, is all of that choice necessarily a good thing? Consider the tabletdelivered digital dashboard in Figure R.1—the technology to create this kind of display exists today in the form of cables to connect to vehicles’ OBD-II port (on-board diagnostics ports found on every modern vehicle), special software and a tablet. But because data about high-pressure oil pump (HPOP) pressure is available, does that mean that the average driver’s attention should be diluted by it? And while the operator of a commercial vehicle or piece of heavy equipment may need to monitor values like engine and transmission oil temperatures, should

you and I attempt to track that data while navigating the family sedan down the motorway?

Coming from an executive who has spent most of his career engaged in the application of emerging technologies to transform business practices for the better, this may seem almost heretical: I would submit that, for the average driver who is commuting to and fro at normal posted speeds, the vintage car dashboard content and presentation in Figure R.2 is far more effective. It displays an absolute indication of vehicle speed, relative values for engine coolant temperature and fuel levels, and it has warning lamps (“idiot lights”) that illuminate in the event of a potentially catastrophic low-oil pressure or improper battery charging condition. As minimalistic as that may be, compared to our first example, it does a far better job of providing the right amount of information to the “average driver.” No more than a quick glance allows that driver to get the necessary information for everyday driving conditions while staying focused on the primary task at hand.

Figure R.3, on the other hand, shows a racing car’s dashboard. The speeds and competitive environment faced by the racer demands laser-like focus on what’s happening on the track and the ability to tune out any and all distractions. At first blush, the plethora of gauges may seem to contradict the earlier points extolling the simplicity of the vintage car’s dashboard. However, drivers competing in events that cover long distances at high speed need to monitor a number of indicators of their vehicles’ health. Those indicators will directly impact strategy over the course of a multiple-hour race.

The gauges on this car are arranged so that they require little more than using peripheral vision, or, at most, that quick glance. Engine speed is the most important data point and therefore the tachometer is several times larger than all of

figure r.1 Tablet-delivered Digital Dashboard
Source: Macrovector/bigstockphoto

figure r.2 Vintage Vehicle Dashboard

Source: BCFC/bigstockphoto

figure r.3 Race Car Dashboard

Source: Barry Blackburn/shutterstock

the other gauges. Also, all of the gauges are oriented in such a way that, when all data values are in their optimal ranges, all of the orange needles are pointed to “12 o’clock”—straight up and down. If something is out of whack, the driver can quickly zero in on the gauge that is showing an abnormal reading and take the appropriate decisions.

In business, it’s imperative to sort out the what—data and information needs within an enterprise vary as widely as, if not more than, those described in the automotive analogy above. Applying the techniques and concepts from seminal works such as Norton and Kaplan’s The Balanced Scorecard: Translating Strategy into Action and Huling, Covey and McChesney’s 4 Disciplines of Execution: Getting Strategy Done enables enterprise measurement blueprints that contain well-aligned combinations of leading and lagging indicators. The blueprints define measures and metrics at every level of the organization that ultimately roll up to, and are aligned with, the business’s overall strategy.

This book, in turn, provides useful examples of frameworks and implementations that help create the right “gauges” and “warning lights” for individuals in various enterprise roles and levels—the how The authors illustrate how various strategy-aligned measurement blueprints are transformed into effective presentations of management content, data and information. These insights help create visual business dashboards to keep decision makers informed with the right performance and information read-outs. Well-designed data visualizations ensure that the quick glance tells your “drivers” what they need to know in order to take any necessary actions or decisions.

We hope that you enjoy the book and that it helps you and your stakeholders to stay on track and ahead of the competition.

introDuction

Translation. The communication of something’s meaning through an equivalent representation. The word derives from the Latin for “a carrying across”—and indeed the gulfs that need to be crossed can sometimes be daunting. The term has a long history of use in the study of languages, with one of the most evocative symbols being that of the famed Rosetta Stone.

When I visited the British Museum with my family in 2013, I couldn’t miss this. Seeing it in person, knowing it was the real thing and not a replica, had a visceral effect on me. I had first learned about the Rosetta Stone from Carl Sagan’s series COSMOS (Episode 12: Encyclopedia Galactica, 1980). The stone was found by Pierre Bouchard, a French soldier under Napoleon’s command during the Egyptian campaign in 1799. It contained portions of passages written in Greek, Egyptian hieroglyphics and Egyptian demotic. By the time of Napoleon however, an understanding of the literal meaning of hieroglyphics and their composite passages, adorning monuments across Egypt, had been lost to time. Egyptologist Jean-Francois Champollion, an expert in languages (including Greek and demotic), ultimately deciphered the hieroglyphics two decades later. To do so he leveraged the fact, or rather the assumption at the time, that the three passages on the stone each had the same meaning.

However the task was not simply one that involved determining which set of hieroglyphic symbols represented which words in Greek or demotic. It required developing an understanding of how these “words” were comprised. Ultimately, Champollion determined that some of the symbols indeed represented sounds (phonetic signs), while others represented entire conceptual meanings in and of themselves (ideographic signs).

The choice of the ancient Egyptians to develop a written language that made the simultaneous use of both simple and highly complex signs, although doing the later European scholars no favors, was an artifact of their context and history.

It made sense to be able to succinctly capture commonly confronted concepts in unique images, while allowing for a system of signs to permit the evolution of the written language. It was efficient in some sense, and certainly not the only instance of such an effort by mankind—take for example the Japanese joint use of Kanji ideographs along with Hiragana and Katakana phonetics.

The world is full of simple signs that serve as fundamental building blocks of visual expression. The world is also full of richer signs, not necessarily more complex in terms of the number of pen strokes (or pixels) used to form them, but vastly more sophisticated in the meaning they are intended to express. The trick for the user of these signs is in understanding the audience to which they are attempting to convey meaning. If the audience has a frame of reference equivalent to the communicator, issues of translation aren’t raised. However when the audience has a different background, the task needs to be approached in a more sensitive manner. A C-suite manager can be just as stymied by a report developed by modern data analysis as can a French soldier stumbling on pretty pictures carved into a stone.

In this text, we hope to provide a little guidance for those charged with translating complex concepts gleaned from large data sets and sophisticated analysis. The kind of guidance that can facilitate translation and have the greatest chance of driving practice. Our hope is to appeal to both casual readers and dedicated researchers/practitioners critically examining their own processes.

For instructional interests, our book is designed as an accompaniment to primary texts in business analytics coursework as well as to texts in business and technical communications coursework. For those focusing on data analytics as a career path, the ability to leverage data visualization as an analytical approach in parallel to mathematical, statistical and computational modeling in undeniable. For those positioning themselves as capable management communicators, the

figure i.1 The Rosetta Stone and Examples of Phonetic and Ideographic Signs

virtues of effective and direct visual communication of data is similarly without question. As instructors delivering curriculum in support of either of these paths, our hope is that the present text offers much needed support.

More specifically, the assistance needed in both instructional cases goes beyond the simple presentation of stunning infographics and requires structured discussions of audience and problem focus in design; looking not just at end consumer audiences but also at intermediate users of data visualization in analytical development. It requires recognizing differences in need, bias and effective processes across contexts and stakeholders; rationalizing the effectiveness of lean or sophisticated representations; static versus dynamic interactive ones, again with a focus on need; solitary idioms versus dashboard systems of such, as well as how to effectively design these.

To accomplish this and set in place best practices for managers and analysts, it is critical to provide them with well-defined frameworks and processes, based on modern scientific principles and backed with rich case examples of use. This ultimate aim is facilitated by a two-section structure. The first section of this text provides a descriptive consideration of the state-of-the-art and science in visual design. The second provides case examples describing development and/or use in practice, with vivid visual artifacts associated with such work provided as an insert.

It is our sincere hope that all readers find these to be both informative and inspirational in their own work, and that ours will be remembered as only the first of many forays in the intelligent development of visual analytics.

The Ohio State University

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Part i The Science of the Art

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1 incoMPlete Pictures

W.E. Deming is quoted as saying “You can expect what you inspect.” Not exactly a phrase that roles off the tongue, but an important premise nonetheless. It implies that since most systems can be decomposed into inputs, outputs and connecting processes, intelligence into the details of each of these can facilitate precise mappings of cause–effect relationships; relationships necessary for the anticipation of risks and opportunities, and therefore the discerning of means for improvement. In the absence of complete details, such mappings suffer.

Yet, despite being much more widely (and incorrectly) referenced, Deming never actually suggested “You can’t manage what you can’t measure.” Human decision makers are not incapable of making very good, even optimal, decisions in the absence of complete information. After all that’s almost always what we are confronted with, even in the age of “big data.” The trick is being able to make use of smart rules, and not falling into traps formed by our own biases.

In this chapter we begin with a description of the boundedly rational mind, drawing on now traditional economic literature on decision theory. We describe the assumptions and implications of this perspective, in practical terms focusing on the manner in which individuals make sense of the world around them. We then extend this discussion by considering three families of biases that specifically affect the way individuals extrapolate from limited data in an effort to make intelligent decisions. Specifically we discuss what we refer to as Set Biases (beliefs in data Homogeneity, Uniformity, Normality), Trend Biases (beliefs in associative data Linearity, Continuity, Unboundedness, Stasis), and Causal Biases (beliefs in relational data Immediacy, No coincidences, Absence of feedback loops). We describe the implications of such biases in the presence of contemporary visual depictions of data; depictions which often do not provide enough details that might otherwise avoid boundedly rational extrapolations and better inform decisions on immediate inspection.

1.1 How We Make lemonade

As stated, most of the decisions we make are done in the absence of complete information out of practical necessity. Nevertheless humans have survived as a species so far, so we must be doing something right. It turns out, when handed the lemon-equivalent of data, humans have a remarkable knack for making lemonade; and more often than not it tastes pretty good.

How do we do it? What is this talent?

In large part we benefit from our ability to construct and reference rules for making sense of our environment. This is a skill developed over the eons of the evolution of our species. Those that maintained efficient and effective rule sets could scan their environment for risks and opportunities, formulate tactics for evading or leveraging, and out-perform those who held less efficient or less effective rules. From a translational perspective, it was as if the high performers had Google Maps advising them on how to plan their routes, while the low performers only had an aerial photograph; i.e. the difference in large part was in how the data available was converted to actionable compositions.

Today we often discuss the process by which we transform data that we observe into potential action as “sensemaking.” Sensemaking as a concept is something we’ll talk about a lot in this text, as it is not simply an inherent ability that individuals possess but also an ability that can be developed. It is also something that is key in the development of artificial intelligence. Granted, sometimes that development is in the wrong direction; individuals or artificial agents latch on to a new rule based on erroneous observation. But in other instances it can lead to much more intelligent decision making, in some cases superseding older less effective rules. And thankfully, course corrections in development are also always possible, albeit at some cost.

Whether correct or not, these rules that we construct include beliefs regarding simple associations as well as complex causal relationships. These are mental maps that draw connections between observations and outcomes desired, peppered with details regarding the constraints within which these nodes and connections exist. They also include details regarding the lags between action and response, though as humans we do have some difficulty remembering to account for these at times.

We also tend to hold more than one line of reasoning, more than one mental map, in our arsenals. After all, we are social creatures.We encounter others who have different views of causality, and no matter how much we might like to we never completely ignore these views.That of course does complicate how we make sense of our environment. Ultimately our choices of rule sets to go with rely on higherlevel meta-rules. Metaheuristics, and at the highest level something that would seem so basic but has long been far too ignored in management: preference.

There are other words you’ll encounter when discussing decision making with scholars. The rules that define how inputs are converted into decisions often fall into the category of “heuristics,” with metaheuristics again referring to those higher-level rules that help develop and select which heuristic best applies to

INPUT

Biases (w/ examples)

Econ: Loss Aversion, Reflection

Effect, Sunk-Cost Fallacy

Psych: Availability/Familiarity, Current Moment, Attribution

PROCESS

Heuristics (w/ examples)

Econ: Anchoring & Adjustments

Psych: “Follow the leader”

Bounded Rationality (exogenously driven) and Cognitive Limits (endogenously driven) influencing

OUTPUT

Decisions made in OM settings

figure 1.1 An Input–Process–Output View of Sensemaking (pro tem. omitting feedback)

a certain scenario. The term “bias” also tends to come up, although any quick examination of extant sources (including the more credible corners of Wikipedia) will reveal a good deal of blurring between what falls into the classification of bias versus heuristics. For the purpose of a concrete discussion, let’s borrow a page again from the Operations Management field and think about sensemaking from an input–process–output (IPO) perspective.

As shown in Figure 1.1, heuristics and metaheuristics are nicely positioned as processes by which inputs are converted into decisions. What are inputs then? Certainly the observations we are able to make or data are by other means provided regarding the context of the decision. However inputs also include personal tendencies to give more weight to certain kinds of data than to others. The biases are the adjectives to the nouns that comprise our decision-making environments, and often serve as adverbs to color the heuristics we apply in decision making. Some examples of these biases, drawn from the elder disciplines of Economics and Psychology are provided in Figure 1.1. Although it isn’t the intent of this text to provide a comprehensive examination of biases and heuristics suggested by researchers, it benefits our present discussion to go through just some of these.

1.2 biases in everyday life

To be sure, biases and heuristics are not all bad. They have a history of being absolutely critical to decision making; without them many great decisions may never have been made in the timely fashion in which they were needed. But it should also be clear that their presence, persistence and acceptance is not without some very relevant risks. Sometimes the lemonade we make gives us an ulcer. It can even be lethal, and sadly not only to the decision maker.

On the input side, focusing on the various ways inherent biases might be influencing us, we’ll start our review with a big one. A bias that is embedded in many, if not most data interpretations and selections of/mediations of decision processes people face on a daily basis: Loss Aversion.

Loss Aversion

The tendency for individuals to strongly prefer (a) avoiding losses versus (b) acquiring gains. Made famous by Kahneman and Tversky (1981, 1984), and their investigation of the Allais Paradox, this bias has been studied extensively. The initial observations of this bias involved examining the following question posed to physicians:

The U.S. is preparing for the outbreak of an unusual Asian disease, which is expected to kill 600 people. Two alternative programs to combat the disease have been proposed. Assume that the exact scientific estimates of the consequences of the programs are as follows: If program A is adopted, 200 people will be saved. If program B is adopted, there is a one-third probability that 600 people will be saved and a two-thirds probability that no people will be saved. Which of the two programs would you favor?

72 percent of those asked chose option A, the certainty of 200 survivals over the possibility of 600 deaths. Interestingly the same options were provided in a somewhat different wording to examine how phrasing alone might impact choice:

The U.S. is preparing for the outbreak of an unusual Asian disease, which is expected to kill 600 people. Two alternative programs to combat the disease have been proposed. Assume that the exact scientific estimates of the consequences of the programs are as follows: If program C is adopted, 400 people will die. If program D is adopted, there is a one-third probability that nobody will die and a two-thirds probability that 600 people will die. Which of the two programs would you favor?

To their surprise only 22 percent selected option C (equivalent in lives saved to option A), with 78 percent preferring option D (exact wording as B). A dramatic swing in what one might view as apparent “preference.” The swing captures what is referred to as the Reflection Effect—the tendency for the preferences of individuals in Loss (or Loss framed) scenarios to appear to be the mirror opposite of those apparent in Gain (or Gain framed) scenarios.

A somewhat more striking comparison is the consideration of the following two choice scenarios. In one, the choice is one between a certain gain of $20 or a one-third chance of gaining $60. In the other the choice is between a certain loss of $20 and a one-third chance of losing $60. Two equal gains in one case, two equal losses in the other. Studies demonstrate a strong preference for certain gains in the first case, but a strong preference for uncertain losses in the other.

Again, a swing in preference?

Not technically, and this is something that scholars often get wrong. The fundamental nature of “preference” (the individual bias against loss) wasn’t swinging

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Wagoner, A., Hudson, N. Y., 97

Walker, D. L., Spencerport, N. Y., 109

Walter, C. P., Sandwich, Ill., 100

Ward, E. D., Lake Village, N. H., 131

Warren, M. C., Son & Co., Grand Rapids, Wis., 101

Watrous, C. W., Windsor Locks, Conn., 92

Watters, S., Leipsic, O., 121

Weed, T., Westfield, Ind., 123

Weir Bros., La Porte, Ind., 107

West, B. F., Beaver, O., 130

West Bros., Grand Ledge, Mich., 103

Wetherholt, G. J., Gallipolis, O., 135

Whisler & Cox, Marion, Ind., 129

Whitcomb Bros., Oxford, Mich., 95

Wicklin & McMackin, Fairfield, Ill., 98

Wilcox, J. E., Mt. Pleasant, Mich., 132

Wilen, Wm. & Son, Martinsburg, W. Va., 130

Willard, L. C., Belvidere, Ill., 104

Williamson & Son, Bradford, O., 130

Wilson, Wm., Bluffton, O., 132

Wilson, Wm. L., Rushville, Ind., 93

Winans & Platner, Pittsfield, Ill., 131

Wingerter, B., Waynesburg, O., 92

Wirt, Chas. L., Frankfort, Ind., 112

Witter, Geo., Storm Lake, Ia., 92

Wolcott, J. L., Bloomington, Ill., 105

Wood, Thos. H., Murfreesboro, Tenn., 98

Woodring Bros., Waverly, Iowa, 101 Wright, A., Kilbourne City, Wis., 135

Wright Bros., Rushsylvania, O., 127

Wright, N. C., Liberty Cent., O., 80

Wright & Phillips, Muncie, Ind., 134

Wuchter, J. H., Wadsworth, O., 98

Young, E. D., Knoxville, 132

Young, H. F., Necedah, Wis., 125

Young, J. F., Vinton, Ia., 93

Younger, J. W., Milan, Tenn., 129

Zaneis & Miller, Washington, 102

Zell, Geo. M. & Son, Waynesville, O., 95 Zimmerman, D. F., Geneseo, Ill., 128 Zimmerman, S., Ottawa, 103

Zimmerman, S. P., Mt. Pleasant, Pa., 124

TRANSCRIBER’S NOTES

1. Typos fixed; non-standard spelling and dialect retained.

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