2022

[1]Scott Pezanowski

[1]Information Sciences and Technology, The Pennsylvania State University,
Westgate Building, University Park, 16802, Pennsylvania, United States of
America

2]Department of Geography, The Pennsylvania State University, Walker Building,
University Park, 16802, Pennsylvania, United States of America

# Exploring Descriptions of Movement Through Geovisual Analytics

<EMAIL_ADDRESS>Prasenjit Mitra<EMAIL_ADDRESS>Alan M. MacEachren
<EMAIL_ADDRESS>*  [

###### Abstract

Sensemaking using automatically extracted information from text is a
challenging problem. In this paper, we address a specific type of information
extraction, namely extracting information related to descriptions of movement.
Aggregating and understanding information related to descriptions of movement
and lack of movement specified in text can lead to an improved understanding
and sensemaking of movement phenomena of various types, e.g., migration of
people and animals, impediments to travel due to COVID-19, etc. We present
GeoMovement, a system that is based on combining machine learning and rule-
based extraction of movement-related information with state-of-the-art
visualization techniques. Along with the depiction of movement, our tool can
extract and present a lack of movement. Very little prior work exists on
automatically extracting descriptions of movement, especially negation and
movement. Apart from addressing these, GeoMovement also provides a novel
integrated framework for combining these extraction modules with
visualization. We include two systematic case studies of GeoMovement that show
how humans can derive meaningful geographic movement information. GeoMovement
can complement precise movement data, e.g., obtained using sensors, or be used
by itself when precise data is unavailable.

###### keywords:

geographic movement, geovisual analytics, machine learning, natural language
processing, big data analytics

##  1 Introduction

Automated methods proposed by the natural language processing and information
retrieval communities often form the basic building blocks in an application.
However, in this paper, we argue that such automated tools, even though they
have achieved some level of maturity, are not enough for the needs of the end-
users especially for domains that require higher-level information
assimilation and cognition like foraging and sensemaking over spatial
information. For example, Lai et al (2022) have recently used natural language
processing (NLP) to understand context in the extraction and geocoding of
historical floods, storms, and adaptation measures. They extend the state-of-
the-art for low-level information extraction, e.g., named entity extraction
and geocoding, but do not provide a holistic understanding of the story
underlying these events.

We posit that our research community needs to “see the forest for the trees.”
Sensemaking is an integral part of information processing, and tighter
coupling between the lower levels (information extraction) and the higher
levels (information understanding and sensemaking) can improve the state-of-
the-art. Specifically, we call for the community to look more at “higher-level
tools and systems” that enable end-users to complete tasks. Towards this goal,
we study the case of extracting geospatial information from text using visual
analytics (VA) Andrienko et al (2020); Yuan et al (2021) to perform tasks over
the extracted data.

Since text is unstructured data and the information within the text is often
messy, the output from computational techniques includes associated errors and
is not sufficient to explore mentions of movement in text without human
expertise. VA can address this issue through human-in-the-loop strategies that
enable analysts to work iteratively with computational methods that extract
knowledge from messy data, cope with uncertainties in computational results,
and improve those results over time (Endert et al, 2014; Robinson, 2017). VA
is especially suitable for big, diverse, messy data that can be interpreted
differently (Tapia-McClung and Silván-Cárdenas, 2021; Angelini et al, 2018;
Ninkov and Sedig, 2019; Snyder et al, 2020; MacEachren et al, 2011).

Our research objective was to determine if computational techniques and
geovisual analytics can leverage large volumes of movement statements to
enable an end-user to understand the movement described quickly. If
successful, research can then take advantage of the wealth of movement data
found in written descriptions about people, wildlife, goods, and other things
moving throughout our world. Text statements about movement can be used to
understand what is moving, when it is moving, why it is moving, and how it is
moving.

For our research, “geographic movement” refers to the movement of people,
animals, objects, goods, information, natural physical processes, and similar
things through spaces ranging in size from multiple buildings to the whole
earth. We applied computational methods to identify and extract movement
statements, and present them in GeoMovement, a human-in-the-loop web-based
geovisual analytics system for identification, processing, and exploration of
descriptions of movement. GeoMovement involves computational 1) cleaning of
the messy text, 2) predicting the statements that describe movement using a
machine learning (ML) model, 3) applying Geographic Information Retrieval
(GIR) techniques to identify places mentioned, and 4) predicting statements
that describe restricted movement or desired movement that is not possible
(hereafter referred to as “impaired movement”). While there is substantial
research on some of these subtasks, integrating these techniques with VA and
demonstrating its success in our chosen domain is the main contribution of
this paper.

While some progress has happened in processing descriptions of movement in
text, there is very little work on detecting and understanding descriptions of
impaired movement. For example, the COVID-19 pandemic prompted us to focus on
impaired movement due to the importance of disruptions and restrictions in
global movement patterns of people and, perhaps equally important, the
movement of goods like food and medicines. Therefore, another important
contribution of this work is that we show an adaptation of an existing
approach developed to detect negation in picture descriptions (van Miltenburg
et al, 2016) can successfully uncover impaired geographic movement in text
documents.

Specifically, we integrated (and adapted or extended) many existing
computational and VA methods to produce a system that supports information
foraging related to geographic movement as reflected in text statements.
GeoMovement is unique in identifying movement statements and filtering them by
place and time.

Figure 1 shows GeoMovement’s user interface. Users can search and filter based
on search terms, the statements’ dates, and impaired movement status. The
statements originate from three sources ingested into GeoMovement to
demonstrate its capabilities and utility for investigating movement:

  * •

398 thousand News articles from August 2019 to May 2020,

  * •

328 million Twitter tweets from February 2020 to May 2020,

  * •

15.6 thousand Scientific articles from August 2019 to November 2020.

Over 520 million total statements contain diverse movement patterns, things
moving, geographic coverage, and temporal differences.

Figure 1: The GeoMovement user interface: A) Main mapping interface with
hexagons selected for place aggregation B) Connection lines between co-
occurring place mentions from an area near New York City selected C) Free text
key term search D) Text data source buttons E) Normal movement vs. impaired
movement buttons F) Geo-binning buttons G) Classification method for bins and
connection lines colors and the number of classes chooser H) Statements that
match the search I) Most common bi-grams that match the search J) Two-sided
Temporal Bar Chart that shows the number of statements, by date, with a
divided bar depicting normal movement on the right (green) and negated
movement on the left (purple) K) Time range slider.

Existing geographic movement research has improved analysis methods (Dodge et
al, 2012; Dodge, 2016a; Dodge et al, 2016; Dodge, 2016b; Graser et al, 2021,
2020, 2020; Soares Junior et al, 2017; Huang, 2017) and shown how these
methods can derive valuable information about human movement and wildlife
movement (Wang et al, 2020a; Dodge et al, 2014; Miller et al, 2019; Zhu et al,
2021; Li et al, 2021). However, partly due to the challenges and because
computational techniques to address them are relatively new, this prior
research focused on geographic movement in precise movement trajectories from
sensors such as GPS and mostly ignored movement described in text documents.
The limited existing research to analyze movement described in text has only
focused on narrow tasks like mapping route descriptions. This work fills a gap
in the research to support the study of geographic scale movement by
developing and demonstrating methods for analyzing movement described in text
documents at a broad scale. We anticipate that our effort will spur more
research to leverage this under-utilized geospatial movement information in
text documents.

We present two case studies (and a third case study in a supplementary video
demonstration) related to the global pandemic that demonstrate the potential
of our approach and shows, despite the messy data and imprecise computational
predictions, a human can derive meaningful and essential information about
geographic movement described in text with GeoMovement. The case studies also
demonstrate that GeoMovement can support multi-scale information foraging
through vast volumes of messy text statements about geographic movement.
Furthermore, the space-time concept/attribute filtering methods implemented
effectively narrow in on information relevant to an analyst’s objectives.

##  2 Related Work

First, we review related efforts to analyze various geographic movement types
described in text documents. Existing research into studying descriptions of
geographic movement has primarily focused on reconstructing route
descriptions. These routes (driving directions (Jaiswal et al, 2010; Drymonas
and Pfoser, 2010), hiking and general route descriptions (Moncla et al, 2014b,
a, 2016; Piotrowski et al, 2010), and historical exploration routes (Bekele et
al, 2016; Blank and Henrich, 2015)) form a constrained subset of movement
statements that simplify and thus do not address many of the challenges with a
broader set of movement statements. Other research took the opposite approach
of textualizing (convert routes to description) to take advantage of the
benefits of text (Chu et al, 2014; Al-Dohuki et al, 2017). Additionally, Huang
et al (2020) showed how geovisual analytics could improve the retrieval of
trajectories in a search. But, their focus on text analytics was only on
users’ queries of the data (which was precise sensor-based trajectories).
Furthermore, in addition to addressing narrow domains, most of this research
has restricted the data to a small amount of text.

The GeoCAM project (Jaiswal et al, 2010) created an application that
identified, extracted, and generated maps of route directions found on
webpages providing textual (often formatted) directions to reach a location.
While the GeoCAM project complements the research we present here, their
problem is simpler since route directions are just a small subset of movement
descriptions. Furthermore, route directions typically follow a semi-structured
pattern that allows for simpler ML models and rules-based approaches. In a
precursor to the GeoCAM project, Drymonas and Pfoser (2010) used similar
techniques to map route directions. These projects encouraged future work like
ours to go beyond route directions to general movement descriptions (Klippel
et al, 2008).

Second, we describe related efforts that use geovisual analytics on geographic
movement described in text documents. The complicated nature of analyzing
place in text documents, especially the need to represent spatial
relationships best rendered visually on a map, has prompted other researchers
to take a geovisual analytics approach. SensePlace was a system to analyze
place mentions in text and pull information from other sources to aid analysis
(Tomaszewski et al, 2011). SensePlace2 developed and applied geovisual
analytics to methods that focus on the extent to which Twitter users’ tweet
location compared to the places they discuss in their tweets (MacEachren et
al, 2011). Robinson et al (2013) performed a user study with experts that
showed both the advantages, usefulness, and difficulties of such a system for
crisis management. SensePlace3 extended this effort by advancing the geovisual
analytics techniques and scaling the system to work on millions of tweets per
month, thereby improving analysis (Pezanowski et al, 2017).

The SMART system complements the SensePlace versions’ focus by providing a
visual interface enabling human analysts to explore text’s spatial, temporal,
and topical components (Snyder et al, 2020; Karimzadeh et al, 2019b). SMART
implemented advanced geovisual analytics techniques, including a tweet
classifier to filter semantically and a cluster lens to visualize keywords at
a large scale. However, like SensePlace3, their system did not focus on
analyzing movement. Finally, the NewsStand system (Teitler et al, 2008; Samet
et al, 2020) mapped places where news articles are written compared to the
places they discuss but focuses less on geovisual analytics for analysis and
more on correctly mapping the text.

A few other efforts use geovisual analytics and mapping systems to analyze
places mentioned in tweets (Thom et al, 2012; Bosch et al, 2013, 2011; Felmlee
et al, 2020) and show the potential to take advantage of this geographic data
source, albeit mainly focusing on tweets with a geocoded location that makes
the challenge different. Mapping and geovisualizations have also been combined
with topic analysis and network graph analysis to show similarities between
cities (Hu et al, 2017).

Jamonnak et al (2020) combined location information associated with videos and
the narrations of those videos to show their locations on the map and the
topics and sentiment discussed at those locations. Xu et al (2018) created a
system to explore Yelp business reviews in areas and their change over time.
Ma et al (2020) showed that geovisual analytics is vital to understanding
critical local places that need immediate help in a disaster from 911 call
transcripts and clusters of specific crime types from police reports. Although
these systems successfully demonstrated geovisual analytics on text, they
focused on particular topics and did not consider movement. Therefore, they
could not be applied to our goal of analyzing wide-ranging types of movement
described in text. Since impaired movement detection is not the primary focus
of this research, we describe work related to it in Section 3.3.

##  3 Text Computational Processing Predictions

We first acquired three different sets of documents consisting of 398 thousand
news articles, 328 million tweets, and 15.6 thousand scientific articles. Our
document sources and the keyword and time parameters used to obtain them are
described in detail in Appendix B.1. We cleaned the documents using typical
text pre-processing methods and applied computational techniques to identify
places in the text, predict the statements that describe movement, and predict
statements that describe impaired movement.

###  3.1 Predicting Movement

In Pezanowski and Mitra (2020)’s work, humans label the statements with a
binary class as either describing geographic movement or not. Since they took
this initial step and created a corpus to train a model, we can use this model
to predict statements that describe movement. The prediction of this ML model
is a probability value between zero (no movement) and one (movement). We set
an arbitrary cut-off for GeoMovement to only show statements with a
probability greater than 0.6 that the statements are about movement. This
relatively loose threshold was arrived at by trial and error and is acceptable
for all three sources.

###  3.2 Predicting Geographic Location

We used the GeoTxt system to perform GIR on our text sources (Karimzadeh et
al, 2013, 2019a). We chose this because GeoTxt performs comparably or better
than other state-of-the-art geoparsers and performs best without case
sensitivity, which is common in Twitter data (one of our data sources) (Gritta
et al, 2018). A small evaluation corpus had an F1-score of 0.78 for geoparsing
place names and an accuracy of 0.91 in resolving those place names correctly.
Moreover, it performed even better on higher-order administrative places such
as countries and states, which are common in our statements. For enterprise
projects, paid commercial sources also exist from software companies such as
Esri, Google, and Microsoft. We chose not to use these products because they
do not allow for customization compared to GeoTxt, which is open-source
software and allows adjustments to the software in the future.

###  3.3 Predicting Impaired Movement

The global pandemic of 2020-2021 brought attention to global movement and how
it spreads, and how the pandemic disrupted or prevented regular global
movement. Because the pandemic highlighted the importance of analyzing
disruptions to movement, we investigated potential strategies for detecting
statements about impaired movement.

To detect impaired movement in our statements, we looked to adapt existing
methods of negation detection in text. Negation detection strategies can
potentially uncover statements about formal restrictions on movement (of the
sort imposed by governments), decisions not to move taken by individuals for
their safety, and impediments to movement created by limited public/commercial
transport such as canceled flights due to the lack of passengers or ill crew.

In this section, first, we review existing related research on negation
detection and its everyday use cases. Second, we describe how we adapted an
existing approach from the literature that detects negation in picture
descriptions (van Miltenburg et al, 2016) to our challenge of detecting
impaired geographic movement described in text. Third, we show how we improved
upon our initial attempt to detect impaired movement using our geovisual
analytics methods to analyze initial mistakes in predictions and then modify
the rules specifically to detect impaired movement.

####  3.3.1 Existing Approaches to Negation Detection

Addressing negation in text has been identified as a problem in several
existing works. For example, Fialho et al (2020) had remarked that “when a
negation was involved in a sentence, the classifiers found more difficult to
return the appropriate label” in the context of negation in sentences as
identified as part of discourse representation structures. Negation detection
in text is vital in challenges like automated summarization of medical reports
(Vincze et al, 2008; Slater et al, 2021), summarizing picture descriptions
(van Miltenburg et al, 2016), and as a hint in identifying sarcasm (Reyes and
Rosso, 2014). Hiremath and Patil (2021) show that sarcasm detection depends
upon detecting negative sentences in positive situations and positive
sentences in negative situations.

Much of the current state-of-the-art research on negation detection was
influenced by a Workshop titled Resolving the Scope and Focus of Negation
(Morante and Blanco, 2012), which also produced labeled datasets that continue
to be used in training and evaluating the success of new methods. Supervised
ML-based solutions such as the LSM Network (Zhao et al, 2021) have learned
negative terms while performing sentiment mining automatically from large-
scale training data. The current state-of-the-art method, NegBert, is based on
ML (Khandelwal and Sawant, 2020; Khandelwal and Britto, 2020). Although this
ML approach is the current state-of-the-art, the challenge in using ML
approaches is the need for time-consuming labeling of large amounts of
training data. NegBert was trained and tested on datasets designed explicitly
for negation detection evaluation and therefore could not be used for our
tangential challenge of detecting impaired movement. In the absence of
training data, we show that rules-based approaches can still be used.

van Miltenburg et al (2016) and van Son et al (2016) have used rules to detect
negation. They had humans annotate Flickr picture descriptions for negations
and define categories of negations. This annotation exercise produced simple
clues for negation, thereby allowing their rules-based approach to be
effective on picture descriptions. In general, rules-based approaches have
been proven to work in negation detection when the domain is relatively narrow
and, like most rule-based systems typically provides high precision but low
recall. Rule-based methods can work fine in our application, where a sample of
the negative sentences suffices, but having false positives can result in
incorrect conclusions.

####  3.3.2 Applying Negation Detection to Descriptions of Geographic Movement

Detecting impaired movement is similar to previous negation detection using
specific key terms. However, what constitutes a negated word is ill-defined
and varies from domain to domain and problem to problem since terms, like
canceled or diverted, would not always be considered negated. But, when
applied to movement, they are. This ambiguity complicates the task. We
investigated if we could adapt the current methods that are focused on the
negation of words (ex. She does not have cancer. The alarm clock did not have
the feature I wanted.) to detect impaired geographic movement (ex. Our flight
to England was canceled. The fruit was stuck in Brazil because of initial
fears early in the pandemic.).

We used van Miltenburg et al (2016)’s rules-based approach for negation
detection. As discussed above, we adapted this rules-based approach, as
opposed to an ML approach, to 1) avoid costly labeling of training data, 2)
achieve transparency in how the results are obtained, and 3) because our needs
in detecting impaired movement are relatively narrow, which the literature
suggests (van Miltenburg et al, 2016; van Son et al, 2016; Slater et al, 2021)
is a good fit for a rules-based approach.

van Miltenburg et al (2016)’s method tags part-of-speech in text (such as
verbs, nouns, adjectives, etc.) and then searches for a list of negation
keywords, prefixes, and suffixes to detect negations. Some negation rules
examples are a) exact negated words like “no” and “not,” b) verbs that start
with “de,” “mis,” “dis,” or c) adjectives that end with “less.” Overall, all
rules are relatively simple and are easily understandable and reproducible. If
the input sentence matches a rule, it containing negation.

We first applied van Miltenburg et al (2016)’s exact rules for negation to our
statements previously predicted to describe movement (as described in Section
3.1) to predict impaired movement. After 800,000 movement statements were
predicted, we stopped and obtained a summary count. The model predicted about
28% of these movement statements as describing impaired movement.

We selected a stratified random sample of 50 predicted impaired movement
statements and 50 predicted normal movement statements. The initial model did
not do very well to predict impaired movement correctly. There were 23 true
positives where the statement was correctly predicted as impaired movement
compared to 27 false positives where the statement was predicted to be
impaired movement, but it was normal movement. It did slightly better in
correctly predicting normal movement with 42 true negatives as opposed to
eight false negatives. Table 1 shows the confusion matrix for these
predictions. These values equate to a precision of 0.46, a recall of 0.74, an
F1-score of 0.57, and an accuracy of 0.65 on the stratified sample.

Table 1: The confusion matrix for prediction of negated movement with unmodified rules from general negation. | Actual impaired | Actual normal
---|---|---
Predicted impaired | 23 (TP) | 27 (FP)
Predicted normal | 8 (FN) | 42 (TN)

####  3.3.3 Improving the Detection of Impaired Movement

To improve our detection of impaired movement, we either added new rules or
removed existing rules. Since there were more false positives than false
negatives, this hints that rules should mostly be removed so that fewer
statements are predicted to have impaired movement.

Not only did we analyze the errors in statement predictions, but we also
consulted a list online of verbs common to movement see Appendix A and
https://archiewahwah.wordpress.com/2019/04/16/movement-verbs-list/). First, we
removed rules specific to verbs that had the prefixes “de,” “mis,” and “dis.”
These prefixes could rarely negate the verbs we found related to movement.
Second, based on an online list of adjectives related to movement (see
Appendix A and
https://dspace.ut.ee/bitstream/handle/10062/18053/adjectives_and_adverbs_of_movement.html),
we removed the adjectives’ rules beginning with “a” and “dis” because they are
much too broad and did not make sense with those commonly related to movement.
Third, we added the specific lemmas “cancel,” “postpone,” “prevent,” and
“avoid” because they relate to impaired movement. Lemmas allow us to match
many different synonyms of these words that all mean the same.

These rules modifications improved predictions on the existing stratified
random sample with a precision of 0.74, a recall of 0.76, an F1-score of 0.75,
and an accuracy of 0.79. Therefore, the F1-score improved from 0.57 to 0.75.

Since we used the first set of 100 sampled statements to determine some of our
rules, it would not be fair to rely on these improved metrics alone to
conclude we improved our method. The rules may over-fit the sample. Therefore,
we selected a second unseen test set of 100 statements using the same random
stratified technique. On this unseen set, the original rules’ prediction
F1-score was 0.59, while our new rules were again much better with a 0.65
F1-score. Overall, our minor modifications to the rules produced substantial
improvement.

Finally, since we selected the two sets of 100 statements from new articles,
as a final assessment, we sampled 100 statements from both the tweets and
scientific articles using the same stratified random sampling technique.
Predictions using our modified rules for impaired movement on the tweets
resulted in an F1-score of 0.61 and an F1-score of 0.60 on scientific
articles. Therefore, our predictions for impaired movement performed slightly
less accurately for these two sources than news articles, but still
respectable. This lower accuracy is likely because both sources contain
language more common for that audience (i.e., slang and other informal
languages in tweets and technical language in scientific articles). Based on
our experience modifying the rules for news articles, we estimate that
additional rule changes would also improve predictions for tweets and
scientific articles. However, based on our experience and the literature, we
surmise that given resources to label a large amount of training data, using
an ML approach as in Khandelwal and Sawant (2020) would likely produce more
accurate predictions. In summary, we are using a rules-based approach as a
proof-of-concept prediction that is important to show the potential benefits
of detecting impaired movement statements and use this as an attribute to
analyze geographic movement described in text.

##  4 Geovisual Analytics to Find Meaning in Descriptions of Movement

GeoMovement is a web-based geovisual analytics system that allows users to
explore descriptions of geographic movement. GeoMovement serves as an
interface between the human and the data described above and is summarized in
Figure 1. The statements’ content, place mentions, date of creation, and
impaired movement prediction are all searchable. The map visualizes place
mentions in multiple levels of aggregate geo-bins. A geo-bin is many smaller
places (e.g., cities) aggregated and displayed as one larger place that they
all reside within (e.g., country). Geo-bins allow for a clear summary of
location-based data. By choosing particular places of interest on the map, the
user can visualize the co-occurrences of places. The individual statements
view completes the overview-first + detail approach. This workflow matches the
information-seeking mantra of overview-first to gain an awareness of the
information and details of interest on-demand (Shneiderman, 1996). This
section is divided into three subsections that follow the information seeking
mantra that starts with the overviews, then options for the user to search to
filter to statements of interest, then detailed views of the filtered
statements.

For detailed information on GeoMovement, Appendix B.1 describes the sources
and nature of the data sources and statements. In addition, Appendix B.2
includes technical details of the application development that allow for fast
user queries on large volumes of text, thereby enabling efficient sensemaking.
An important technical component of our approach is our use of Elasticsearch
(https://www.elastic.co/)) as the primary information storage and retrieval
software for GeoMovement. Elasticsearch is a search engine that accepts many
search parameters like free text and time, and returns matching results
ordered by most relevant to the user’s search. In addition, it can group
results by attributes like place mentions in the text. Most impressively,
Elasticsearch does all of this and returns results very quickly, most often in
the matter of milliseconds.

###  4.1 Overviews

There are two primary overview means to explore the movement descriptions. The
first overview is the map (Figure 1 at point A) that displays the number of
statements spatially aggregated by their place mentions. Since we have a large
number of statements from multiple sources, GeoTxt extracted over 98,000
unique place mentions from them. Displaying this large number of places on a
map using points would likely be very confusing for users. Many places would
overlap and quickly seeing overall patterns along and comparing quantities
between places would be difficult. This is the primary reason for aggregating
the statements place mentions. Coloring the polygon bins that contain places
mentions a lot in the statements darker than those that are not mentioned
frequently in the statements. Users get a clear overview of place mentions in
the statements and a way to visually compare places.

We chose five shapes to spatially aggregate the number of statements by their
place mentions. The geo-bins scales include continent, country, administrative
1 (the worldwide equivalent of a state in the United States), and two sizes of
a hexagonal pattern. Each scale allows users to explore different types of
movement data, like long-range bird migrations by continent, traded goods by
country or administrative level, and detailed movement through the hexagons
that do not adhere to political boundaries like the spread of disease. We
chose hexagons as an aggregation shape since they tessellate and will distort
values less than squares (Birch et al, 2007; Esri, 2021). The user interface
provides an option shown in Figure 1 at point F where the user can choose the
aggregate level.

The bin counts are divided into classes to ease visual comparisons between
bins. The user has control of the bin count classification technique and
number of classes, as shown in Figure 1 at point G. The user has five choices
on how to classify the aggregate counts. Jenks Natural Breaks, Equal Interval,
Standard Deviation, Arithmetic Progression, and Quantile are options, and they
can enter the number of classes between two to seven. These classification
techniques are well accepted statistical methods to make the data more
understandable.

We used the geostats JavaScript library (https://github.com/simogeo/geostats)
to calculate the class breakpoints for the chosen users’ classification
method. Each classification method is valuable depending on the user search
and resulting data. Darker colored bins represent areas that have a larger
number of place mentions within the bin. The color scheme is a sequential
color scheme chosen from ColorBrewer (https://colorbrewer2.org/) (Brewer et
al, 2003) to ensure the classes of statement counts are easily
distinguishable.

The second overview in GeoMovement is a Two-sided Temporal Bar Chart. Research
has shown that there are differences in how people understand geographic
movement such as the those who think more spatially than others (Liben and
Downs, 1993; Ishikawa, 2016). Based on this research, GeoMovement provides the
user multiple views of the data. The Two-sided Temporal Bar Chart shows
aggregate counts of statements grouped by the month they were published
(Figure 1 at point J). The oldest month is at the top and the youngest month
is at the bottom. The Two-sided Temporal Bar Chart allows for visualization of
the amount and changes over time, both in impaired movement on the left side
and normal movement on the right side. When hovering the mouse over the chart,
the number of statements that match the current user search are shown in bars
in the foreground while the total number of statements in GeoMovement are
shown in bars in the background. Figure 6 shows an example of the number of
statements that match the user search in the foreground once the user has
hovered the cursor over the chart.

###  4.2 Search

After the overviews give the user an understanding of the data, they can begin
filtering it through search options. The five key ways to search the data are
1) free-text search (Figure 1 at point J), 2) buttons to select from the three
sources for statements — news articles, tweets, and scientific articles
(Figure 1 at point J), 3) buttons to select impaired movement statements or
normal movement statements (Figure 1 at point J), 4) a time-range slider
(Figure 1 at point J), 5) and clicking location(s) on the map to filter by
location. Multiple features can be chosen by holding the Ctrl-key on their
keyboard and selecting the next feature with a mouse-click. After any of the
first four searches are performed, the geo-bins and Two-sided Temporal Bar
Chart update to show counts of statements that match the searches. After the
fifth search, the detailed views appear.

###  4.3 Detailed Views

The detailed views of statements and their attributes include a) connecting
great circles drawn on the map between place mentions in the statements, and
co-occurring place mentions in the same statement, b) the five most common bi-
grams for the set of statements matching the search, c) and a table showing
the actual statements that match the search. All connections between places
are aggregated to show places commonly used together. The aggregate count
classes adhere to the users’ currently chosen map classification method and
total class number in a sequential green color scheme that is color-blind
friendly, also selected from ColorBrewer (Brewer et al, 2003).

It is important to note that the connection lines are completely accurate in
showing movement between the places since they are drawn solely by the places’
co-occurrence in a statement. For example, the statement below has three place
mentions and therefore three possible movement pairs: Sydney–New York,
Sydney–London, New York–London. The first two are correct concerning movement
references in the statement, but the third is not correct because there is no
direct movement between New York and London. One could argue that the movement
could be from New York to Sydney and then next to London from Sydney, but this
is not probable. However, these connection lines to give the user overall
patterns of interest that they can confirm through inspection of the
statements.

> But this will be the first time a commercial flight is flown from Sydney to
> New York, and just the second time from Sydney to London, Qantas said
> (Garber, 2019).

Second, the ten most common bi-grams are shown for the set of statements that
match the search and have place mentions in the chosen location bin. This
provides details about the actual statements behind the overviews by showing
the most common words and topics in the selected statements. Since many of the
statements contain place mentions and the search location is an essential
parameter for the matching statements, two-word place names are often in this
bi-grams list. These place mentions may be valuable, but we also found that it
was often more important to see bi-grams about topics and not necessarily
places when exploring the data. Therefore, we allow users to double-click a
bi-gram to remove the bi-gram from the list, and the next most common bi-gram
appears, up to the 20th most common bi-gram. To re-populate the bi-grams list,
the user can re-run the search. The user can also select multiple bins by
holding the Ctrl-key and clicking another map bin. Users can then compare the
most common bi-grams for both map bins to see differences between statements
with place mentions in each bin.

To complete the overview first + detail approach to analysis, once the user
found sets of statements of interest in the overviews and chose a map bin, the
user can see the actual statements matching the search in a paged list. The
displayed statements match all search parameters (when selecting multiple
bins, statements can have place mentions in either bin). The user can scroll
through the pages of statements. Each statement’s published date is also
shown. If there is a particular statement of interest, holding the Ctrl-key
while clicking on the statement will open the original document on the Web in
a new browser window.

GeoMovement’s tight use of modern search engine technologies and Information
Retrieval (IR) allows for extremely fast human-in-the-loop sensemaking for the
most relevant information on movement. Again, Appendix B provides further
details, and our supplemental video showcases a real-time live demonstration
of efficient knowledge discovery using GeoMovement.

##  5 GeoMovement Assessment

First, we show the challenge of interpreting movement described in statements
without GeoMovement by discussing summary statistics of the data. Second, we
present two case studies that show how our approach can retrieve information
about movement from vast quantities of statements. A third case study is
included in a supplemental video. It is recorded in real-time to show that a
user can quickly extract meaningful information about movement despite the
challenges posed by the large quantity of messy text, ambiguity in text
descriptions, and imprecise computational predictions. Third, we show how
these case studies also generated future GeoMovement needs. Finally, in
Appendix C, we discuss the skill level and hardware and software requirements
for GeoMovement users.

###  5.1 Illustrative Data Summary Statistics

To show the value of GeoMovement, we created summary statistics of the data to
clearly illustrate how it is unreasonable to think a human can analyze and
understand large quantities of movement statements without such a system. We
chose three keywords related to our case studies and three prominent place
names: one being a country, one a state, and one a city. The number of our
statements that match these keywords and place names is shown in Table 2. To
relate these statistics to our first case study in Section 5.2, we filter
GeoMovement’s 520 million statements (36 thousand of those contain the term
smuggling, 275 thousand of them include the term gold and 201 thousand mention
India) and efficiently identify important gold smuggling patterns around and
in India. To produce these statistics, we used our GIR extracted place names
to determine the number of statements that contain each selected place name.
And, to find the number of statements containing each of the keywords, we
searched the statements in a Postgresql database using a full-text search
(https://www.postgresql.org/docs/14/textsearch.html) so that different
variations of the same word will be matched (e.g. smuggling, smuggled,
smuggle).

Table 2: Summary statistics of the number of statements that contain selected key terms and place mentions. smuggling | gold | sports | London | California | India | statements
---|---|---|---|---|---|---
36 K | 275 K | 336 K | 589 K | 165 K | 201 K | 520 M

This exercise to generate summary statistics is meant to show that without
such a geovisual analytics system like ours, it would be extremely difficult,
if not unfeasible, to perform geographic, temporal, and attribute sensemaking
of the described movement. Although GeoMovement’s 520 million statements are
substantial, it is still a fraction of the accessible text available that
could be included and analyzed in GeoMovement, given more development and
computational resources.

###  5.2 Case Studies

To confirm our claim that the geovisual analytics interface helps users
understand and make sense from the statements and multiple computational
predictions, we provide two detailed case studies below from different types
of (prototypical, fictitious) potential users. The case studies presented
provide evidence of usability. A third case study, given only in the video
supplement (due to space limitations in the text), adds additional evidence
about the flexibility and utility of GeoMovement to explore the mix of text
data sources from different perspectives.

####  5.2.1 Understanding International Crime Affecting India

Jennifer Lang is a college student who wants to write a class report about
different types of international crime affecting India. She opens her web
browser to GeoMovement and types smuggling to begin her search. She notices
that many statements involve smuggling are in October 2019, despite that month
having fewer statements overall. She adjusts the time range slider to filter
statements to that month and sees a hotspot of activity in England. She clicks
the hexagon bin in England and is reminded of a significant human smuggling
event in that month where many people lost their lives after being trapped in
a truck that was smuggling them (Figure 2). Although this is a significant
smuggling event from a British perspective and also highlighted in the U.S.
news, she decides to look for other ways to focus on India.

Figure 2: By filtering the search results for smuggling to only show
statements from October 2019, she sees many statements from a significant
human trafficking event in England where many people lost their lives.

As her next step, she chooses to aggregate place mentions by the country level
and select India. Figure 3 shows the results, and she sees that most results
related to smuggling are affecting India from the neighboring countries of
Pakistan and Bangladesh, and a few more countries. In the bigrams list, she
sees many references to drugs and gold being smuggled into India.

Figure 3: A search for smuggling shows important location sources for India
and a spike of activity in October 2009.

Next, after reading some individual statements, she finds out more about
multiple cases of gold smuggling from both outside and within India (Figure
4). To get a more detailed analysis of the movement, she types gold in the
search box and chooses to aggregate place mentions by the state level. By
clicking the neighboring state of Sindh, Pakistan, which has many place
mentions in the statements, connections with many states in India are
highlighted, including a state in southeast India.

Figure 4: A search for gold produces many statements about gold smuggling and
possible routes.

She clicks this state in India and discovers that the state is Tamil Nadu,
where the large city of Chennai is located. From reading a few statements, she
knows that gold smuggling in Chennai is arriving at the airport and through
the Chennai Express train from Mumbai. The connection with Mumbai through the
train is confirmed on the map by the strong connection with Mumbai’s state,
Maharashtra, on the west coast closer to Pakistan (Figure 5). She can now
clearly visualize and report on some of the prominent drugs and gold smuggling
sources into and throughout India and read more detailed descriptions about
individual incidents.

Figure 5: While looking for more detail about gold smuggling, statements
suggest a smuggling route from Mumbai to Chennai along the Chennai Express
train.

####  5.2.2 Examining the Impact of the Pandemic on Travel for Tourism

Arti Reddy is a travel agent in India. She uses GeoMovement to understand the
pandemic’s impact on global travel and travel related to India. As of May
2020, like other countries in the World, India was dealing with a global
pandemic. Since Arti had previously planned to advertise to potential
customers traveling for sporting events, she chose this topic to investigate.
She loads GeoMovement and enters the term sports in the text search box. With
the geospatial hexagon geo-bins as a layer, she quickly sees a hotspot of
discussion in Japan. She selects to filter statements for impaired movement.
While mousing over the Two-sided Temporal Bar Chart, she sees that the
impaired movement statements are more prevalent in recent months (except May,
where there is less data), as seen in the Two-sided Temporal Bar Chart in
Figure 6.

Figure 6: Mousing over the Two-sided Temporal Bar Chart shows the number of
filtered statements for impaired movement (purple bars to the left of the
axis) involving sports has been increasing since the pandemic began (bars are
aggregate counts of statements by month, with the oldest month at the top).
Filtered statements by the search are shown in the foreground, while all
statements are shown in the background on the mouse hover.

Arti switches to the country geo-binning, chooses to view both types of
movement again, and moves the timeline from July 2019 to December 2019, and
Japan is still a popular location. After selecting Japan on the map, she sees
in the bi-grams list that the upcoming Olympics in Japan are prominent, and on
the map, she sees that there are connections between Japan with many other
places in the World (Figure 7). There is much discussion about the potential
impact of the pandemic on the Olympics. She is intrigued to see that there are
connections between Japan and her home country that implies her business will
be affected.

Figure 7: Sports have been disrupted during the pandemic, including the
Olympics, where athletes travel from around the World. Mousing over the Two-
sided Temporal Bar Chart shows that the filtered statements for impaired
movement have increased since the pandemic began.

Since she is most familiar with India’s geography, she changes the geo-binning
to the state level for more detail and selects two states in India with much
discussion (Figure 8). A quick look through the statements shows that the
Olympics are in jeopardy, and a closer-to-home event of the under-17 women’s
soccer World Cup scheduled to be in India was unfortunately postponed. Teams
would have come from around the World for this event. This sad news prompts
her to follow up on the story by clicking the statements to view the original
articles in her web browser to see if it will be rescheduled and thus if there
will be a future need for travel. From reading the statements, she sees that
an auto show, the AP World Indoor Sporting Championships, and the Australian
Grand Prix auto racing’s China leg are three events postponed in China.
Cricket in Australia was also severely affected, with teams planning to come
from India, England, and many other countries to compete. Major sporting
leagues in the U.S., like the NBA basketball league, were also interrupted.
The disruptions to nearby events like cricket are particularly concerning
given the sport’s popularity with Indians and their potential spectator travel
related to her business.

Figure 8: Sports in India have also been affected, including competitions
where local teams compete against other teams from around the World.

Lastly, to see how widespread the pandemic’s impact is on global sporting
events where Indians may travel, Arti explores other countries and sees that
the pandemic has significantly impacted professional soccer in Spain and other
European countries. Madrid, Milan, and cities in Germany where prominent
soccer clubs play all show up clearly on the map as having their games
affected (Figure 9).

Figure 9: Europe is another example where the pandemic postponed many of their
beloved soccer matches.

These use cases highlight the potential for information foraging and
acquisition from statements describing movement. Geovisual analytics that
combines multiple views of the data and overview + detail capabilities allow
users to quickly identify important locations, connections between locations,
time periods, and topics of interest. We showed how the map view could show
critical hotspots, and the Two-sided Temporal Bar Chart can show essential
time periods, prompting the user to investigate details.

In this section, our use cases show GeoMovement’s current sensemaking
capabilities. Moreover, the examples of computational prediction errors and
suggested solutions highlight how geovisual analytics is well suited for
allowing user interactions to either explicitly or implicitly improve
GeoMovement’s computational predictions. Improved prediction accuracy can make
the sensemaking process more efficient by reducing the human effort to sift
through incorrect intermediate results.

###  5.3 Case Studies Needs Assessment

In addition to our case studies demonstrating the effectiveness of GeoMovement
in extracting information about movement, we used them as a needs assessment
to propose additional functionality. In the first case study, Jennifer
identified statements from an event that she was not interested in. Although
she quickly found what she was looking for, other problems may require much
further analysis, and future results should not include statements deemed not
relevant. Therefore, a future addition to GeoMovement can consist of a
mechanism to either mark statements as completely “not relevant” and not show
them or as “less important.” If user accounts were added to GeoMovement, these
preferences in statements could be stored. If certain types of statements will
never be relevant to that user, their marked statements can be used to affect
their future search results. Such user input can be used as feedback to the
system and in the future, statements that are very similar to those that are
marked not of interest will also be filtured out; i.e., the system learns from
the feedback. Elasticsearch allows for on-the-fly criteria provided in
searches that promote or demote statements or exclude them, allowing for these
criteria to be personalized if user accounts were added and changed for each
each search.

In the second case study, Arti found that impaired movement became more
prevalent in recent months. It would be a fair assumption that this is because
of travel restrictions from the pandemic. However, Arti may ask for more
detail about “How is impaired movement changing over time?” To do this, she
should easily access statements by month. Currently, she would have to change
the time slider filter to each month and select the impaired movement button.
A straightforward way to answer this question would be to choose any bar in
the Two-sided Temporal Bar Chart to update the time range and impaired
movement filters. For example, clicking on the impaired movement purple bar
for January 2020 would update all views to show these statements. Similarly,
when Jennifer found a spike in smuggling activity in India in October 2009,
she would likely ask herself why this is and want a quick way to filter to
those statements.

Finally, both Jennifer and Arti were interested in India. A question they both
may have is: “What other locations have similar problems as India?” Currently,
GeoMovement allows selection on the map of multiple locations to show bi-grams
lists for both locations’ statements next to each other. However, there are
automated ways to give users hints on locations with similar statements. For
example, Elasticsearch provides a “percolate” query where, after the user
already selects a location, they could choose a user interface control and
click a second location. The statements from the first location can be used in
the percolate query. The result would be statements like the first set, and
therefore other places like that place will show.

Our evaluation of the sensemaking capabilities of GeoMovement includes a
statistical summary illustrating the challenge in extracting meaningful
information without it, case study demonstrations, a needs assessment, and a
list of user requirements. We show that despite the large volume of messy data
that is at times ambiguous, GeoMovement can quickly extract meaningful
information about geographic movement.

##  6 Results and Implications

Analyzing a large volume of text describing geographic movement requires
imprecise computational processing and predictions on already messy data that
different people can perceive differently, resulting in some errors. We
present the data in a geovisual analytics web application that follows the
overview-first + detail mantra (Shneiderman, 1996). Users can find exciting
patterns in the overviews and areas that need further investigation. They can
also search the data and eventually drill down to the actual statements and
articles. Results that appear to be erroneous or merely uninteresting can be
hidden so that it is easier for the analyst to focus on more helpful
information.

Our approach tightly integrates geovisual analytics with behind-the-scenes
computational predictions for statements describing movement from most text
that does not, GIR methods to extract geography for mapping and analysis and a
novel prediction for impaired movement. We show the value of GeoMovement
through case studies where humans quickly learn about and visualize geographic
movements. Furthermore, we demonstrate that a multi-scale system that applies
a space-time-concept/attribute filtering process can effectively make sense
from large volumes of messy movement statements.

Because of the challenges of utilizing messy big text data for understanding
geographic movement, previous work has either focused only on movement data
from precise sensors or addressed narrow domains of movement described in
text. However, the need for improved methods to utilize big text data about
movement is illustrated in work described in Section 2, and Hultquist and
Cervone (2020), Hultquist and Cervone (2019), and Janowicz et al (2019), where
geography in text complements sensor data or stands alone to solve real-world
problems. MacEachren (2017) also encouraged such advances in his positional
paper. GeoMovement 1) tightly integrates computational predictions and
geovisual analytics, and 2) is built with modern web computing technologies
that return results from user queries in milliseconds. These features allow
users to make sense of and uncover movement patterns quickly. We show how this
efficient and broad movement exploration is unfeasible without computational
and visual means to assist a human. Moreover, this completes our research
objective to show that modern advances in NLP and IR can leverage large
volumes of movement statements to understand the movement described quickly.

Although we stopped collecting text for GeoMovement, our techniques can be
adapted in a straightforward technical way to accommodate a continually
updating source given modest computational and storage requirements. In
addition, all of our code is available with an open-source license to prompt
future advances (Pezanowski, 2022).

We anticipate GeoMovement or a production version to be beneficial for
scientists, journalists, or even the general public to find information about
the movement of humans, wildlife, goods, disease, and much more. Advances to
analyzing movement statements can provide similar knowledge gains as movement
recorded by sensors with precise geospatial and temporal data.

##  7 Conclusions and Future Work

Our research shows that modern computational techniques can be combined with a
human-in-the-loop geovisual analytics system to overcome significant
challenges and identify, process, and explore large volumes of movement
statements to quickly obtain an overview of movement patterns and forage for
detailed information of interest. Future research should take advantage of the
wealth of context information found alongside geographic movement in text
documents about why, when, and how the movement is occurring that is not often
present in precise GPS data. Our research methods can likely be adapted to
analyze statements involving other attributes like time and more complex
spatial analysis like correlation. In addition, future research should explore
the integration of precise geospatial movement data with movement in text. One
initial way is to link them spatially and temporally. For example, the
following steps can involve connecting entities in the text to other
information using the Semantic Web (Berners-Lee et al, 2001) and linked
geographic data (Stadler et al, 2012; Janowicz et al, 2012). Future research
needs to identify what makes movement statements different from other
statements. (Pezanowski et al, 2022), took initial steps towards this goal by
identifying vital characteristics of movement statements that humans use to
differentiate the movement described.

Also, since we chose to illustrate the effectiveness of GeoMovement through
the case studies and other assessments in Section 5, future work should
include a more thorough evaluation of user needs, especially focusing on the
additions suggested in this Section.

Moreover, prior research shows that a geovisual analytics system combined with
ML predictions can continually improve the accuracy of the ML predictions by
having users correct machine errors (Snyder et al, 2020; Andrienko et al,
2022). An extension of GeoMovement can make it an intermediary between
computational predictions and humans. As more humans use GeoMovement, humans
can iteratively correct any errors they encounter, thereby improving the
predictions. As an example, in Section 3.3, we discuss how an ML model would
likely be superior to a rules-based approach detecting impaired movement but
requires a large amount of training data. GeoMovement can show initial
predictions of normal movement and impaired movement from an ML model that
used a small amount of training data. A simple tool can allow users to correct
errors in the predictions. Once many users are using GeoMovement, the training
data set can proliferate. This technique has also shown success in commercial
production mapping systems like Google Maps, where users reach their
destination, and Maps asks about driving directions’ accuracy.

Two overall ways that GeoMovement can improve the computational predictions
are, first, explicitly asking for feedback on any incorrect predictions and
second, through implicit user actions. For example, in GeoMovement’s
statements view, a button can be added to explicitly mark any statements that
do not describe movement. Once a sufficient number of users mark a statement
to conclude that it is incorrect, the corrected statement can be added to the
corpus of statements used to train the model. As an example of implicit
actions, the statements’ list is currently returned in order by the search
engine software that roughly corresponds to how closely they match the search
terms entered and how frequently those search terms appear in the statement.
Skipped statements can be recorded when users page through results to find
what they are looking for. If many users skip certain statements, they can be
deemed less valuable and given a weighting that lets them be listed lower in
the order they are returned, thereby promoting more critical statements. There
are many other possibilities to obtain either explicit feedback (users correct
place mentions that were assigned to the incorrect location using the GIR
techniques (Karimzadeh and MacEachren, 2019)) or implicit feedback from users
(identifying important locations to highlight based on where previous users
navigated to on the map) to improve GeoMovement’s sensemaking ability.

Additionally, none of the existing GIR systems have ideal performance metrics.
There is often a trade-off between different geoparsers’ false positives and
false negatives and other ways to rank geocoding results on large datasets.
Future work can allow user controls in GeoMovement to choose different
geoparsing back-ends. Also, the open-source GIR GeoTxt can be modified to
allow GeoMovement users to set a tolerance to allow more false-positives in
situations where missing relevant statements are most critical or allow fewer
false-positives in situations where the key is a quick overview and having
every relevant statement does not matter. Our use of geovisual analytics for
GeoMovement adds substantial potential for future improved analysis of
movement statements.

Finally, it is important to revisit that GeoMovement only supports English.
Therefore, a valuable extension to our research would be the addition of other
languages. The higher-level software, programming languages, and APIs
(discussed in Section B) we used in our research to harvest, process, store,
retrieve, and visualize data are all capable of handling many languages and
character sets. However, incorporating documents in other languages into
GeoMovement would require significant work on the three main computational
predictions we used. First, identifying statements that describe movement
would require a large amount of labeled training data in the added language.
Also, for higher accuracy in predictions, language models like ELMo would need
to be re-trained for that language like the research of Che et al (2018) and
Fares et al (2017). Second, our geoparsing methods to extract place mentions
and geocode them to their correct location on Earth would need to be adapted
(Mandl et al, 2009). Third, our negation detection techniques would also
require adaption to support other languages (Morante and Blanco, 2021). These
changes are possible but need significant work as existing research is less
robust than the equivalent in English.

The World is a dynamic place, and understanding geographic-scale movements of
things is essential in many domains like business, public health, and
environmental science. Leveraging information about movement found in text can
complement precise sensor-based movement data. Also, movement described in
text is valuable since precise movement data is often unavailable because of
high costs or impracticality to deploy sensors. GeoMovement and the
combination of computational and visual methods it integrates are steps toward
that objective.

Supplementary information

We include a supplementary video of GeoMovement to show how geovisual
analytics helps derive valuable patterns about geographic movement from text
documents.

Acknowledgments

We would like to thank the Information Technology group in the College of
Information Sciences and Technology at The Pennsylvania State University for
providing computing resources to host GeoMovement. Specifically, Adam
McMillen, a Systems Administrator in the group, provided his expertise in
establishing a virtual server and deploying GeoMovement to it.

## Statements and Declarations

No funds, grants, or other support was received.

The authors have no competing interests to declare that are relevant to the
content of this article.

Author Contributions: Conceptualization: Scott Pezanowski, Prasenjit Mitra;
Methodology: Scott Pezanowski, Prasenjit Mitra, Alan M. MacEachren; Software:
Scott Pezanowski; Data curation: Scott Pezanowski; Formal analysis: Scott
Pezanowski; Validation: Scott Pezanowski, Prasenjit Mitra; Resources:
Prasenjit Mitra; Visualization Design: Scott Pezanowski, Alan M. MacEachren;
Visualization Implementation: Scott Pezanowski; Project administration: Scott
Pezanowski; Writing - original draft: Scott Pezanowski; Writing - review &
editing: Scott Pezanowski, Prasenjit Mitra, Alan M. MacEachren; Supervision:
Prasenjit Mitra, Alan M. MacEachren

##  Appendix A Terms Related to Movement

Table 3 shows the list of verbs related to movement used in our negation
detection techniques as retrieved from
https://archiewahwah.wordpress.com/2019/04/16/movement-verbs-list/ on January
21, 2022.

Table 3: List of verbs related to movement used in our detection of impaired
movement.

Advance - move forward. | Pootle [informal] - proceed casually.
---|---
Aim (for) - go in the direction of. | Pop (in, to, over etc) - quickly visit/pass etc.
Amble - walk casually. | Potter - move in an unhurried way.
Angle (towards etc) - turn one - s steps towards. | Pound - proceed with fast, heavy steps.
Back (out, away etc) - move in reverse. | Prance - move flamboyantly, with effected grace.
Barrel - move in a forceful, uncontrolled way. | Progress - advance.
Beetle - hurry like an insect. | Proceed - go forward.
Belt - move swiftly. | Promenade - take a leisurely walk.
Bez [informal, dialect] - zip around. | Prowl - move in a shifty or predatory manner.
Bluster - move forcefully yet ungraciously. | Race - move quickly, in competition.
Bolt - move swiftly. | Ramble - walk far and wide.
Bounce - move with elastic motions. | Regress - go back.
Bound - move quickly with large steps. | Return - go back.
Bumble - proceed in a clumsy fashion. | Roam - proceed with no direction in mind.
Canter - move fairly, like a horse. | Roll - [literal] proceed in turning motions like a wheel / [figurative] move steadily.
Careen - speed forward uncontrollably | Rove - wander far and wide.
Career - speed forward with little control. | Run - proceed quickly, both feet leaving the floor.
Charge - move aggressively towards something. | Rush - move with haste.
Crawl - [literal] go on all fours / [figurative] proceed slowly. | Sashay - move in a confident and flamboyant way.
Creep - move sneakily or slowly. | Saunter - walk arrogantly, confidently.
Dance - move rhythmically. | Scamper - run like an agitated animal.
Dart - go swiftly. | Scarper - run away.
Dash - run quickly. | Scoot - proceed at a fair pace / shuffle to one side.
Dawdle - proceed slowly and reluctantly. | Scud - move quickly as if blown by the wind.
Dive - descend quickly. | Scuff - walk in a careless, friction-producing way.
Dodder - move unsteadily, as if elderly. | Scurry - hurry like a small animal.
Dogtrot - move at a brisk, comfortable pace, like a dog. | Scuttle - hurry like an insect.
Emerge (from) - come out of. | Seethe - proceed like oozing liquid.
Escape - move out of danger/confinement. | Shuffle - walk slowly, without lifting one - s feet.
File (in) - [of multiple people] go one-by-one. | Skedaddle - depart in haste.
Flee - run away from. | Skip - proceed bouncing from one foot to the other.
Flounce - move in a flamboyant way. | Skitter - move hurriedly.
Flop - move loosely. | Slide - move frictionlessly.
Fly - [literal] move through the air / [figurative] proceed swiftly. | Slink - go smoothy/sensuously.
Footslog - march a long distance. | Slip - move frictionlessly / make an accidental movement.
Forge (on, ahead) - proceed strongly and steadily. | Slither - slide forward like a snake.
Gallop - move quickly, like a horse. | Slope - as sneak.
Gambol - proceed in a playful, energetic manner. | Sneak - Proceed surreptitiously.
Glide - move frictionlessly. | Speed - move very fast.
Go - basic movement verb / depart. | Split - depart.
Hare - proceed extremely quickly, like the animal. | Sprint - run at top speed.
Hasten - move with haste. | Stagger - move unbalanced, unsteadily.
Head (towards, for, to etc) - proceed in the direction of. | Stalk - move as though hunting.
Hie [archaic] - go quickly. | Stampede [multiple people] - progess chaotically /in agitation.
Hightail [informal] - move quickly. | Steam - power forward.
Hike - go a long distance. | Step - move with the feet.
Hop - [literal] proceed on one foot / [figurative] make a short journey | Streak - move quickly, as if leaving a line of light behind you.
Hurtle - move quickly, violently and recklessly. | Stride - walk purposefully.
Issue (from) - come out of. | Stroll - walk in a brisk, leisurely manner.
Jog - move at a medium pace/half-run. | Strut - walk stiffly / arrogantly.
Jump - propel oneself through the air. | Swagger - move arrogantly.
Jaunt - go on a short trip. | Sweep - proceed swiftly.
Journey - travel a distance. | Tank - progress swiftly and forcefully.
Labour - move with difficulty, requiring force. | Tiptoe - proceed lightly, silently on the toes.
Leap - jump far. | Traipse - walk a distance.
Leg (it) - run (away). | Tramp - walk a distance.
Limp - proceed unevenly / with an injured leg. | Trample - walk without precision or care.
Lollop - proceed in ungainly bounds. | Travel - move a distance.
Lope - move in large strides. | Tread - move using the feet.
Lunge - jump forward to attack. | Trek - travel a long time / distance.
March - move steadily/forcefully/with purpose. | Trip - proceed lightly, gaily.
Meander - proceed in an indirect way. | Tromp - walk heavy-footed.
Mooch [informal] - go around in a skulking manner. | Troop - march with effort.
Mosey - walk in a leisurely manner. | Trot - move briskly like a horse.
Move - basic verb of movement. | Trundle - move arduously like a cart.
Nip (into, across, over etc) - quickly go. | Tumble - fall / spiral forward
Pace - walk steadily. | Undulate - proceed in wavy motions
Pad - walk casually/softly/steadily like an animal. | Waddle - Walk in ungainly fashion, from side to side.
Parade - proceed in an extrovert manner. | Walk - go by foot.
Patrol - walk around in order to guard. | Wander - travel without a direction in mind.
Patter - go with a light tripping sound. | Wend - travel by a circuitous route.
Pass - move beyond. | Whizz - go speedily.
Pelt - move quickly, like a hurled stone. | Wobble - move unsteadily.
Perambulate [formal, rare] - walk. | Zip - move swiftly.
Plod - move with heavy, laborious motions. |

Table 4 shows the list of adjectives related to movement used in our negation
detection techniques as retrieved from
https://dspace.ut.ee/bitstream/handle/10062/18053/adjectives_and_adverbs_of_movement.html
on January 21, 2022.

Table 4: List of adjectives related to movement used in our detection of
impaired movement.

In the text, some nouns are qualified with adjectives (sharp, sudden) and some
verbs with adverbs (dramatically, significantly).
---
Adjectives used to describe movement include: slow, slight, moderate, gradual,
steady, quick, rapid, significant, sharp, substantial, dramatic.
Used to show a small change: slight
Used to show a regular movement: gradual, steady
Used to show considerable, striking or unexpected change: significant,
substantial, dramatic (both positive and negative change), sharp, sudden
Adverbs are formed by adding -ly to the adjective, and sometimes one or two
other letters change as well.
Degree of change:
dramatically, considerably, significantly, substantially, sharply, moderately,
slightly
Note that “dramatically” can refer to both good and bad changes.
Speed of change:
rapidly, quickly, suddenly, gradually, steadily, slowly

##  Appendix B Detailed Description of GeoMovement

###  B.1 Text Data Sources

First, we purchased news articles from Webhose.io (https://webhose.io/).
Webhose.io collects text documents from various Web sources and distributes
them in a clean semi-structured XML format. To save costs and reduce the
number of irrelevant statements, we used their web archive tool to obtain text
documents that match the key term travel, that is in the English language, and
that was published on a website categorized as news. We downloaded all
articles in their archive between August 1, 2019, and May 10, 2020, that fit
these filters (397,919 articles).

Second, we acquired and incorporated tweets using the GeoCov19 dataset
spanning February 1, 2020, to May 2, 2020 (Qazi et al, 2020). This dataset
contains hundreds of millions of tweets related to the Covid-19 pandemic. We
harvested only the tweets in English, which resulted in 328 million tweets.

The third source for statements is the Covid-19 Open Research Dataset
(CORD-19) (Wang et al, 2020b). This dataset contains scientific articles about
the Covid-19 pandemic that were harvested and placed in a clean semi-
structured XML format. The collection has over 50 thousand articles retrieved
from PubMed Central (https://www.ncbi.nlm.nih.gov/pmc/), bioRxiv
(https://www.biorxiv.org/), and medRxiv (https://www.medrxiv.org/) that match
search terms related to all Coronaviruses and is updated daily. We downloaded
the dataset on November 14, 2020, and selected documents from that date back
until August 1, 2019, which totaled about 15,600 documents.

###  B.2 Application Development for Efficient Sensemaking

GeoMovement is an entirely web-based application. We built the web client
using the React JavaScript Library (https://reactjs.org/) as its framework. We
also used Material UI (https://material-ui.com/) React Components to make
development efficient and provide a friendly and familiar user experience. We
used Mapbox GL JS (https://docs.mapbox.com/mapbox-gl-js/api/) in the map
portion of GeoMovement and Deck GL Libraries (https://deck.gl/) (Wang, 2017)
to provide advanced mapping and geovisualization capabilities along with
state-of-the-art rendering speeds for large amounts of data. The web client
application is a visual interface for humans to search the server’s data in
Elasticsearch. Other software used to pre-process the data includes Postgresql
(https://www.postgresql.org/) for data storage and PostGIS
(http://postgis.net/) for geospatial computations. The result of this
application design is a web application accessible in any major modern web
browser, both desktop, and mobile that produces rapid results to user searches
of hundreds of millions of descriptions of geographic movement. Almost
instantaneous responses to searches enable users to more easily forage through
data in the sensemaking process and identify important information. Figure 1
shows the user interface of GeoMovement.

##  Appendix C GeoMovement User Requirements

In Section 5.2, we address the utility of GeoMovement through two case study
scenarios that demonstrate the ability of GeoMovement to support information
foraging through the large volumes of text coming from multiple kinds of
sources. The scenarios make specific assumptions about the required
capabilities, skills, and prior knowledge of users.

User Capabilities

Currently, GeoMovement works only with text in English; thus, users are
expected to be English speakers. Given the focus on interactive maps and
graphical displays, users need manual dexterity sufficient to handle
interaction with the map and various controls and adequate visual acuity to
read the maps and graphs. Although we did not test GeoMovement on many
computer configurations, based on our internal testing, web development
expertise, and established guidelines and recommendations for the development
technologies, we can estimate that the user needs modest computing
capabilities. The user should have a computer with a minimum monitor screen
resolution of $1024\times 768$ pixels. A modern JavaScript-enabled browser
(Chrome, Firefox, Safari, and Microsoft Edge) is required, and minimum
computer hardware specifications to support these browsers. GeoMovement was
tested and worked on a modern smartphone with a mobile Chrome browser;
however, GeoMovement’s sensemaking capabilities are restricted because of the
small touch screen. Although GeoMovement’s design and architecture provide
swift search responses from the server software, performance depends on the
users’ Internet connection speed because it is web-based. Therefore,
GeoMovement will function, but user Internet connection speeds under the
typical broadband minimum of 25 Mbps will likely produce a lag in user search
results that inhibits the analysis.

User Skills

GeoMovement expects familiarity with web browsers, graphs, and maps. We assume
some experience with thematic maps (maps that depict data geographically), but
no particular expertise is required. Thus, users familiar with the kinds of
maps routinely found on news sites like those of the New York Times of the
Guardian are sufficient. An ability to understand simple statistical graphs
(bar charts) is assumed.

User prior knowledge

Since GeoMovement is designed for user-led information foraging, it does not
provide suggestions to users about what to explore or specifically what to
look for. The assumption made is that each user has some knowledge of the
topic they are interested in, that they can apply, to leverage the visual
interface and computational methods included in GeoMovement. That is, in fact,
the point. This is a human-in-the-loop system intended for users who have
domain expertise that is complemented by the ability of GeoMovement to process
very large volumes of text and potential for interactive filtering tools to
support quick narrowing in on items of interest through the use of that domain
knowledge to decide what is interesting and what is not interesting. Beyond
domain knowledge, however, users also need some understanding of the
limitations of the computational methods that underlie GeoMovement.
Specifically, they should understand that any system like this will generate
some false positives and some false negatives; thus, it will show them some
irrelevant information (which their domain expertise is likely to allow them
to ignore) and miss some relevant information. Therefore, they need to
understand that absence of evidence from GeoMovement does not necessarily mean
the absence of the phenomena they are interested in.

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