# GeoCamera: Telling Stories in Geographic Visualizations with Camera
Movements

Wenchao Li  0000-0003-2605-7331 The Hong Kong University of Science and
TechnologyHong Kong SARChina<EMAIL_ADDRESS>,  Zhan Wang
0000-0003-3318-6473 The Hong Kong University of Science and Technology
(Guangzhou)GuangzhouChina<EMAIL_ADDRESS>,  Yun Wang
0000-0003-0468-4043 Microsoft Research AsiaBeijingChina<EMAIL_ADDRESS>,  Di Weng  0000-0003-2712-7274 Microsoft Research AsiaBeijingChina
<EMAIL_ADDRESS>,  Liwenhan Xie  0000-0002-2601-6313 The Hong Kong
University of Science and TechnologyHong Kong SARChina<EMAIL_ADDRESS>,
Siming Chen  0000-0002-2690-3588 Fudan UniversityShanghaiChina
<EMAIL_ADDRESS>,  Haidong Zhang  0000-0001-7411-8042 Microsoft
Research AsiaBeijingChina<EMAIL_ADDRESS>and  Huamin Qu
0000-0002-3344-9694 The Hong Kong University of Science and TechnologyHong
Kong SARChina<EMAIL_ADDRESS>
(2023)

###### Abstract.

In geographic data videos, camera movements are frequently used and combined
to present information from multiple perspectives. However, creating and
editing camera movements requires significant time and professional skills.
This work aims to lower the barrier of crafting diverse camera movements for
geographic data videos. First, we analyze a corpus of 66 geographic data
videos and derive a design space of camera movements with a dimension for
geospatial targets and one for narrative purposes. Based on the design space,
we propose a set of adaptive camera shots and further develop an interactive
tool called _GeoCamera_. This interactive tool allows users to flexibly design
camera movements for geographic visualizations. We verify the expressiveness
of our tool through case studies and evaluate its usability with a user study.
The participants find that the tool facilitates the design of camera
movements.

Visual storytelling, data video, geographic visualization, authoring tools

††copyright: acmlicensed††journalyear: 2023††doi:
10.1145/3544548.3581470††conference: Proceedings of the 2023 CHI Conference on
Human Factors in Computing Systems; April 23–28, 2023; Hamburg,
Germany††booktitle: Proceedings of the 2023 CHI Conference on Human Factors in
Computing Systems (CHI ’23), April 23–28, 2023, Hamburg, Germany††price:
15.00††isbn: 978-1-4503-9421-5/23/04††submissionid: 1136††ccs: Human-centered
computing Interactive systems and tools

##  1\. Introduction

Figure 1. The user interface of GeoCamera: (A) previewer of camera movements
in the geographic visualization with geospatial targets selection, (B) library
of camera shots classified by narrative purposes, (C) camera and visual
configurations and crafted examples, and (D) location-camera hierarchical
timeline.

Figure 1 describes the layout of the system interface.

Geographic data videos have been prospering for years as an intuitive
storytelling medium for geographic data. In practice, geographic data videos
increasingly comprise diverse camera movements (e.g., (vizmap, 2022; Xaquín
G.V., Graham Roberts, Ford Fessenden, Hannah Fairfield, Lisa Waananen and Amy
Schoenfeld, 2012; LEMMiNO, 2019)) to depict a sequence of geographic insights.
The use of appropriate camera movements is essential in the authoring of
geographic data videos. First, camera movements help present geographic
visualizations from suitable viewing angles. For example, a 3D bar chart
placed on a map needs to be read from a lower viewpoint, and the occlusions
among the bars can be reduced by animating the camera around the chart.
Second, these camera movements help change the narrative focus in the
geographic context smoothly, which is beneficial for driving the narration
(Tang et al., 2020b; Cheng et al., 2022). Third, the dynamic camera movements
naturally attract attention (Tversky et al., 2002) and engage the audience
(Amini et al., 2015). Finally, by changing the moving speed of the camera,
certain emotions can be delivered to the audience; for example, pulling the
camera very slowly away from objects to suggest their isolation and loneliness
(Lan et al., 2022).

However, it remains challenging for people without knowledge in filmmaking to
design camera movements in geographic data videos. On the one hand, although a
rich palette of camera movements is available (Katz and Katz, 1991), guidance
in choosing the most appropriate one for a particular geographic insight or
target under the story contexts is lacking; on the other hand, off-the-shelf
toolkits for authoring camera movements fail to strike a balance between
granularity (expressiveness) and agency (complexity in human operations)
(Méndez et al., 2018). For instance, kelper.gl (Vis.gl, 2022b), a geospatial
analytics platform, supports the animation of the temporal changes in
geospatial data points from a fixed point of view. Power Map (Microsoft Excel,
2013), a plugin for Microsoft Excel, enables users to fly between locations
with a predefined camera movement on a geographic visualization. To gain more
flexibility in editing camera movements, people often resort to complex video-
editing software (e.g., Adobe After Effect (Adobe Inc., 2022)) or programming
frameworks for geographic visualizations (e.g., deck.gl (Vis.gl, 2022a) and
Mapbox (Mapbox, 2022)). However, given the difficulty in configuring complex
low-level parameters, including the longitude, latitude, focal length, camera
distance, and movement duration, these approaches impose a substantial barrier
for average users. Setting up many scenes by manually adjusting these
parameters is laborious and time-consuming.

To facilitate the authoring of camera movements, we propose GeoCamera, a new
geographic data video authoring tool that seeks balance between granularity
and agency. GeoCamera is designed with two goals, namely, to produce
compelling camera movements for storytelling (G1) and to lessen the barriers
in authoring geographic data videos (G2).

To achieve the goals, this research first investigates how camera movements
have been used in practice to tell a geographic data story from different
perspectives. We collected a corpus that includes 66 geographic data videos
and 805 camera movements. By analyzing why camera movements are employed
(narrative purposes), what objects are the focus of narration (geospatial
targets), and how camera movements are designed (camera shots), we derived a
design space for the camera movements in geographic data videos. Based on our
preliminary findings, we modeled geographic data videos as a composition of
scenes and implemented GeoCamera to facilitate the creation of camera
movements driven by narrative purposes. We demonstrate the expressiveness of
GeoCamera with a case study and a reproduction study. GeoCamera was also
evaluated with a user study and received positive feedback on usability and
learnability from the participants.

The contributions of this study are summarized as follows.

  * •

A design space for camera movements in geographic data videos. It comprises
three dimensions: narrative purposes, geospatial targets, and camera shots.

  * •

A geographic data video authoring tool that facilitates average users to
narrate stories with flexible camera movements tailored for different
purposes.

##  2\. Related Work

###  2.1. Understanding Data Videos

Data videos are short data-driven motion graphics (Amini, 2020). It is one of
the basic genres in narrative visualizations (Segel and Heer, 2010).
Researchers have been interested in understanding how to create data videos
expressively and efficiently. Prior works often carried out a qualitative
examination to capture salient characteristics in existing pieces, including
content analysis, user studies, or formative interviews. Amini et al. (Amini
et al., 2015) analyzed 50 data videos and summarized visual representations
and attention cues of data video content. Upon the cinematic definition of
four major narrative categories (i.e., establisher, initial, peak and
release), they revealed common narrative structure patterns in data videos.
Shi et al. (Shi et al., 2021a) studied animation in data videos with regard to
cinematography and summarized 4 animation techniques and 8 visual narrative
strategies from 82 examples. Xu et al. (Xu et al., 2022) focused on the
openings of data videos and summarized 6 types of cinematic styles out of
hundreds of classic films. Amini et al. (Amini et al., 2018) conducted
crowdsourced studies and found that pictorial or animation representations
improved viewers’ engagement with data videos.

In addition to the cinematic aspects, recent works turned to narrative
methodologies in data videos. Cao et al. (Cao et al., 2020) analyzed 70 data
and presented a taxonomy, including 5 distinct genres, 4 narrative structures,
and 6 narrative attributes. Shu et al. (Shu et al., 2020) proposed a design
space for data-GIFs from 108 examples. They further studied the impact on the
understandability of each design dimension through interview and questionnaire
studies. Yang et al. (Yang et al., 2022) investigated 103 data videos to
understand how Freytag’s Pyramid, a well-received narrative structure, has
been utilized. Similarly, we applied content analysis to understand the
cinematic styles and animated transitions in data videos. Our concentration on
geographic data videos revealed a fruitful subset that encompasses richer
camera effects than general data videos. In addition, unlike previous work
that mostly contributed abstract design implications, our empirical findings
were directly applied in our authoring tool design.

###  2.2. Authoring Geographic Data Videos

Owning to the ubiquity of geospatial data, geographic data stories have been a
common category in data-driven storytelling (Tong et al., 2018), where maps
are essential to provide the spatial context (Caquard, 2013; Hogräfer et al.,
2020). Prior works have synthesized plenty of implications for designing a
geospatial data story. For instance, Nagel et al. demonstrated how staging
transitions could effectively explain steps from a high-level view to a fine-
grained view through a public exhibition design (Nagel et al., 2017). Mayr and
Windhager (Mayr and Windhager, 2018) suggested how standard spatiotemporal
visualization techniques affected narrative cognitive processing. Roth (Roth,
2021) proposed a design space of spatial narratives with three dimensions:
narrative elements, genres, and tropes. Furthermore, Latif et al. (Latif et
al., 2021) showed the textual narrative sequence and its relationship with
visual counterparts in geographic data stories.

However, we observe a barrier to crafting a geographic data video that tells a
story in a comprehensive and palatable manner. On the one hand, general-
purposed visualization authoring tools provide limited support for geographic
data (Cheng et al., 2022). Template-based or automatic tools (e.g., DataClip
(Amini et al., 2017), Flourish Studio (Flourish, 2022), and AutoClip (Shi et
al., 2021c)) ignored a rich palette of visual representations for geospatial
data, such as 3D globe visualizations. According to our corpus analysis, tools
that feature higher expressibility (e.g., Ellipsis (Satyanarayan and Heer,
2014), DataAnimator (Thompson et al., 2021), and Animated Vega-Lite (Zong et
al., 2023)) do not consider camera design, which is common in geographic data
videos. Some tools are tailored for specific scenarios (e.g., (Tang et al.,
2022; Chen et al., 2021; Lu et al., 2020; Shin et al., 2022)), yet none
applies to geospatial data. On the other hand, off-the-shelf visualization
software (e.g., Tableau (LLC., 2022), ArcGIS (ArcGIS, 2022), and Mapbox
(Mapbox, 2022)) or library (e.g., deck.gl (Vis.gl, 2022a), kepler.gl (Vis.gl,
2022b), and BigQuery (Google Cloud, 2022)) are flexible for analytical tasks
in geographic data, but they hardly address the need for storytelling or raise
high barriers. Video makers may need to screen-record their operations and
edit through general video tools or write external scripts programmatically.
GeoTime (Eccles et al., 2008) was one of the earliest works that integrated
storytelling into a data exploration tool. With several primitive features, it
helped capture the analysts’ insights and support later communication. The GAV
toolkit (Lundblad and Jern, 2013)) facilitated geographic storytelling within
an interactive web context. Most relevant to our focus on geographic data
videos, Power Map (Microsoft Excel, 2013) automatically generated map
transitions among consequent slides. Our work summarizes representative camera
movements in geographic data videos into a design space. We further develop an
authoring tool based on a design space that supports inexperienced users to
integrate various camera effects into their data videos by selecting their
narrative purposes.

###  2.3. Camera Effects in Data-driven Storytelling

Camera effects orient from cinematography, with typical examples, such as
trucking, tracking, zooming, rolling, and tilting (Katz and Katz, 1991;
Bordwell, 1985). With the pressing need to navigate audiences in the 3D space,
camera control and motion planning have been intensively studied in fields
related to computer graphics (Christie et al., 2008), including terrain
visualization (Serin et al., 2012), volume visualization (Hsu et al., 2013;
Zheng et al., 2011), game engines (Halper et al., 2001), robotics (Kavraki et
al., 1996), urban scene reconstruction (Zhou et al., 2020; Liu et al., 2021,
2022), and virtual cinematography (He et al., 1996; Xie et al., 2018). Prior
research has validated camera effects as an important construct of data
stories, which remain effective for narration guidance (Tang et al., 2020a),
aesthetic enjoyment (Shi et al., 2021a), and emotion delivery (Lan et al.,
2021). Segel and Heer (Segel and Heer, 2010) studied narrative visualizations
and decomposed general visual narrative tactics into visual structuring,
highlighting, and transition guidance. They regarded camera motions as a
strategy that offers transition guidance, and they found that camera zoom
contributed to highlighting. Amini et al. (Amini et al., 2017) summarized nine
major attention cues in data videos, including camera angle and zoom, that
helped engage the audience. Stolper et al. (Stolper et al., 2016) studied web-
based data stories and identified linking separated story elements through
animation as an emerging and recurring technique. Although their corpus was
based more on interactive webpages, our focus on data videos shares
similarities in the continuous transition between the sequences of data
stories. Most relevant to our interest in geographic data stories, Cheng et
al. (Cheng et al., 2022) examined the interplay of camera effects and
narrations. They concluded that map-based data clips extensively applied
camera animations to steer audiences’ focus, especially for insights into
locations and differences and background information.

From the authoring perspective, Thompson et al. (Thompson et al., 2020) listed
camera as a type of graphics object in the design space of animated data
graphics. Alternation of the camera’s configuration, such as its position or
projection properties, results in a view change, such as panning, zooming, and
rotating. Tang et al. (Tang et al., 2020b) proposed a taxonomy of narrative
transitions that classified camera motions as one of the five transition
types, with subtypes, including pedestal, truck, tilt, pan, dolly, zoom, and
rack focus. However, their design spaces failed to capture the relationship
between narratives and camera configurations. In this work, we attempt to
bridge the gap between narratives and camera configurations with empirical
correlations. We recommend camera configurations suitable for the users’
narrative goals, which alleviates users’ burden in tweaking relevant
parameters. Though prior works in other areas have contributed various methods
for creating camera effects easily, such as optimizing camera trajectories
(Joubert et al., 2015; Xie et al., 2018), controlling camera motions (Serin et
al., 2012; Huang et al., 2016), and selecting view-points (Assa et al., 2010;
Zheng et al., 2011; Hsu et al., 2013), our work differs from them in that we
do not concern low-level details of the camera configuration rather than a
high-level cinematic type because we focus on the coherence among given
geographic data insights.

##  3\. Characterizing the Design Space

In this section, we explain how we identified the design patterns of the
camera movements in geographic data videos. Our methodology involves several
steps: collecting a corpus of high-quality geographic data videos from online
sources, analyzing the corpus to develop an initial design space, and
validating the design space by two professional drone photographers. Next, we
present our derived design space at the end of this section.

###  3.1. Data collection

Based on the usage scenario and design considerations, we investigate the
design of camera movements for geographic data videos based on real-world
examples. To identify design principles, we first survey how existing hand-
designed geo-stories fascinate audiences immersed in different insights into
geographic visualizations. Previous research (Cheng et al., 2022; Amini et
al., 2017; Shi et al., 2021b; Tang et al., 2020b) collected many data videos
from various online resources to explore the common design patterns and
performance styles of this digital storytelling. These corpora cover a wide
range of high-quality data videos. By using these corpora, we filtered 66
videos with map-based visualizations. Although these videos may employ camera
movements in map-related and map-unrelated clips, we only focus on those map-
related clips in these videos. We split these clips according to the type of
camera effects and obtained 805 camera movements on the map in total. These
examples are not comprehensive, because one data video tends to reuse the same
camera movements to tell similar stories, and most of the data videos are only
concerned with two-dimensional maps. However, we aim to derive some versatile
design patterns of camera movements to guide our authoring tool’s
implementation rather than covering all camera techniques for geographic
videos.

###  3.2. Analysis and Validation

Our target audiences are general users without any experience in crafting
camera movements of videos, only their initial imagination for every story
segment of the final video. For example, videos typically introduce the brief
background at the beginning of the story and then delve further into details.
In other words, they have their own narrative purposes for different data
clips.

To identify an appropriate taxonomy of narrative purposes in geographic data
videos, we conducted a literature review from narrative visualization (Segel
and Heer, 2010; Stolper et al., 2016; Bach et al., 2018a), data graphics
design (Chevalier et al., 2016; Thompson et al., 2020; Li et al., 2020; Bach
et al., 2018b; Shu et al., 2020; Shi et al., 2021b), and cinematic
storytelling (Cutting, 2016; Cutting et al., 2011). After the literature
review, we collected a set of important narrative purposes that camera
movement can help describe key contents in a story. Taking this premise into
consideration, three researchers coded each segment to analyze camera
movements from the following aspects: (1) the type of movements (including
details of specific parameters, e.g., speed) all cameras used; (2) geographic
objects that camera movements describe; and (3) the narrative purpose of the
segment based on the narration context. We iterated on this coding process
until each segment of the camera movements could be coded consistently.
Through several iterations, we identified four geospatial targets that camera
movements mostly focus on and six narrative purposes served by different
camera movements to create stories to characterize our design space. We also
summarized eight basic camera shots commonly used in geographic data videos to
serve different narrative purposes and geospatial targets.

After initially extracting common patterns in geographic data videos, we
invited two professional drone pilots outside the data visualization domain to
validate the utility of our design space and refine the design space. The
newly introduced examples also demonstrated generalizability. Both of them
have been working in aerial filming for 3 to 4 years. We first let the two
drone pilots present their video work and introduce our design space to them.
Then, they were asked to use the design space to analyze their work. We
observed how they applied the design space and found all of their presented
work created by camera drones could be described along our three dimensions
(i.e., narrative purposes, geospatial targets, and camera shots).
Subsequently, we conducted an open-ended discussion with each expert to gain
their feedback and suggestions for creating new geographic videos using our
design space. Based on their advice, we modified our design space by merging
related narrative purposes. For example, merging the providing context and
changing viewpoint into supplementing information. Redundant or uncommonly
used camera shots were also removed to keep the items in the dimension
succinct.

The validation resulted in four geospatial targets and five narrative
purposes, which were described in subsection 3.3.

Figure 2. Statistical results of our coding on 66 geographic data videos and
805 camera movements: (A) camera shots and their corresponding narrative
purposes of different geospatial targets, and (B) the distribution of camera
movement number in a data video.

The left part of Figure 2 uses a table to show the distribution of the camera
movements in the collected geographic data videos. The right part of Figure 2
uses a histogram to show the distribution of camera movement frequency in one
data video.

###  3.3. Design Space

In this section, we describe a design space of geographic data videos. The
design space consists of three dimensions: narrative purposes, geospatial
targets, and camera shots. For different narrative purposes and geospatial
targets, we can recommend several usable camera effects of basic camera types
based on the investigation of existing geographic data videos. Figure 2 shows
the distribution of camera shots and the corresponding narrative purposes with
geospatial targets and the frequency of the number of camera movements in one
data video.

####  3.3.1. Narrative Purposes

The narrative purpose explains why a specific camera movement is used when
crafting a geographic data video. We summarized this taxonomy from existing
literature and refined it from experts. Notably, the same camera movement can
serve multiple narrative purposes, depending on its graphic parameters and
relation to geospatial targets. For example, a zoom-out shot is usually used
for revealing the surroundings of the focal object, whereas such a shot at a
slower speed commonly serves to increase the dynamic of the current scene.

Emphasizing a Target is a crucial criteria of narratology to make a narrative
a narrative. Placing those most important parts at the center of the story is
required (Fludernik, 2009). In geographic data videos, it is used to highlight
geospatial targets specifically by making them occupy the largest percentage
of the scene. Emphasizing a target is the most frequently used purpose in
geographic data videos. For example, if designers want to talk about or focus
on partial visualizations, then they will select an engaging way to attract
audiences to partial visualizations, such as zooming in to enlarge the
important parts, or by presenting such visualizations with a relatively longer
duration. When emphasizing a target, video authors sometimes adopt more
combinations of different camera shots to enhance engagement compared with
other narrative purposes.

Overviewing Multiple Targets is similar to emphasizing a target. The key
difference is that this purpose is aimed at observing multiple targets.
Although zoom is still a useful camera shot to present multiple targets as a
whole, it sometimes causes overlaying issues with a huge amount of data and
thus vision confusion. Therefore, video authors prefer to pan multiple targets
individually at a finer granularity.

Making a Comparison uses another thing to explain or justify the main topic
with similarities (i.e., analogy) or dissimilarities (i.e., contrast) in
narratology (Fludernik, 2009). It is also regarded as a common task for
complex objects in data visualization (Gleicher et al., 2011). Similar to
geographic data videos, this narrative purpose only serves multiple geospatial
targets. Although overviewing multiple targets and making comparisons can be
used to present multiple objects, we can differentiate them by the adjacent
scenes. A simple process of telling a geographic visualization is that
designers first overview the map to introduce the context. Then, the map is
zoomed in to emphasize an important region. Later, the map is zoomed out of
the scene to compare similar data attributes between the highlighted part and
the other regions.

Supplementing Information refers to circumstances that provide context
information, such as adding additional descriptions and changing the viewing
angle. Success in data visualization begins from building the context in the
need of communication (Knaflic, 2015). In addition to presenting data
insights, video creators sometimes need to add information that cannot be
encoded in geographic visualizations to establish the context of the story.
For example, if the author wants to deliver the information that Italy is a
member of the European Union, they might add the icon of the European Union
logo next to Italy on the map. In most cases, such context information is
inserted into the scene at the blank regions without information in the
visualizations. Hence, a pan shot is the most used technique for this
narrative purpose to save space for newly added information.

Increasing Dynamics refers to circumstances when no target is selected, and
camera effects are used to give energy to the scene and create an atmosphere.
Satisfying this narrative purpose can be very simple but greatly increase the
continuity of data videos. For example, zooming in/out the map subtly or just
randomly moving the map. Such scenes always happen at the beginning of the
story or in a new section to introduce the relevant story and bring out the
focus of the next story.

####  3.3.2. Geospatial Targets

The dimension of geospatial targets describes what type of visual object is
commonly presented with the camera movements. Visual objects can be presented
in single mode or in groups on the map. We totally define six types of
geospatial targets. To describe the single object, we summarize three types of
targets (e.g., location, region, and path) based on the geometric
characterization of objects in geographic visualization. In most scenes,
including many geospatial targets, such design usually aims to explore the
relationship among these geospatial targets regardless of their respective
types. Based on such findings, we group multiple targets (i.e., locations,
regions, and paths) into a different type (i.e., multiple targets). Notably,
the geospatial target can be None for a specific camera movement (i.e.,
Increasing Dynamics).

Location has a point-like geometry respectively on the map. The type of target
is the smallest one in the scene. Many camera shots can be used to present
such a single location (e.g., a building or a bridge). For example, a zoom-in
shot is a commonly used technique to change the narration topic from the last
scene to the next single target.

Region has area-like geometry on the map (e.g., a country or a state). The
region contains an additional area attribute than the location. Sometimes, the
region can ignore its area attribute. Thus, region and location can be
interchanged by visual appearance and camera movements. We can design similar
camera movements for display. For example, if New York City is just marked to
indicate where some events happened, then we can only zoom in on this
visualization to emphasize its importance. However, if we want to visualize
the city’s boundaries, then a pan shot, which is seldom used for presenting a
single-location target, is a much more recommended technique.

Path has a line-like geometry on the map and is specific in the geographic
domain (e.g., a river or the border). A path can show how connections among
multiple locations. Thus, specific camera movements are used to have an
overview and follow the cues of the whole path.

Multiple Targets aims to describe a set of geospatial targets regardless of
their respective types. When the designer presents many geospatial targets in
a scene, they use the camera to construct the overview of or build a
relationship (like comparison) between targets, which is related to the
quantity rather than the type of targets. To be more specific, the scene
should be designed to cover all the geospatial targets of this set. For
example, if a set of locations and another set of paths have a comparable
geospatial distribution of visualizations (e.g., they are both within the
borders of China), then it is highly likely to present two sets of targets on
the map with similar scale and similar camera movements. Limited camera
movements can serve multiple targets, such as panning among adjacent targets
for comparison or zooming out to expand the scale of the map to overview all
related targets.

####  3.3.3. Camera Shots

We summarized 9 types of camera movements regarding the camera’s positioning,
orientation, focal point, and moving path. Some types are commonly used in
real-world videos, and others are expanded from cinematography in films. Our
goal is to give inspiration for the design of camera movements. Notably, one
camera shot can serve many narrative purposes and geospatial targets with
different parameter settings, such as the camera’s moving speed.

Static: A static shot does not move the camera, hence resulting in an
unchanged scene. We still identified this type as a camera shot because this
technique is frequently used in videos. The static scene can avoid audiences’
confusion due to excessive many animations and direct their attention to the
main content of the scene.

Push In: A push-in shot alters the positioning of the camera to become closer
to an object. It is probably the most commonly used camera movement. It can
reduce the map’s scale to a specific geospatial target to draw the audience’s
attention toward the target. Especially, a push-in shot with a slow speed can
increase the duration of displaying the target and leave audiences to expect
what might happen ahead.

Pull Out: As opposed to push-in shots, a pull-out shot changes the camera
itself to make it further away from an object. In this way, the map’s scale
enlarges to cover the adjacent objects and surrounding environment to provide
a brief context of the target.

Pan: A pan shot refers to the camera that moves from one place to another. In
this way, the pan shot can change the focus of the map and thus change the
targets in the scene. Hence, it is usually used to change the topic of the
story and emphasize the targets in the panned scene. Panning multiple objects
one by one can build an overview of all these objects. Especially, this camera
movement is identified as a whip pan when panning the camera with a quick
speed. Different from panning at a normal speed, the whip pan ignores the
information in the panning process. Therefore, it is handy for transitions
that express the meaning of moving a large distance or time elapsing.

Tilt: Similar to pan movements, a tilt shot moves the camera shot vertically
upward or downward. The tilt shot, as an unveiling technique, is helpful
either to reveal from top to bottom or the reverse. For example, it can be
used to describe the bar height from its bottom to the top to emphasize the
data values encoded by the bar.

Camera Roll: A camera roll rotates the camera pointed at the same object on
its long axis. The rolling camera can emphasize the object from different
perspectives.

Arc: An arc shot moves the camera around the same object in an arcing orbit.
It is typically used to add dynamics to a static object for emphasis because
of its longer duration.

Tracking: A tracking shot describes any shot that moves alongside a subject
for a period of time. It can be used to simply follow a path and thus display
detailed information, such as nearby cities and the surrounding
transportation.

##  4\. GeoCamera Overview

###  4.1. Design Considerations

The authoring tool is designed to simplify the process of crafting geographic
data videos for the general users. We summarize a set of high-level design
considerations for an authoring tool that empowers average users to craft the
camera movements in geographic data videos quickly based on literature survey
and design space study. We assume that the video makers have attained
sufficient insights into geographic data, and their task is to organize these
insights into a coherent story.

(C1) Facilitate easy camera authoring with narrative purposes. Average users
may not be familiar enough with diverse camera shots to create a compelling
geographic data video. To assist them in the authoring processes, narrative
purposes, such as emphasis and comparison, should be defined to empower users
to design camera movements at the semantic level. For example, when a user try
to compare two regions, they simply select the comparison purpose and zoom
into the regions they want to compare. Narrative purposes abstract away the
details in configuring camera shots and offer users an easy and intuitive way
to convey geographic insights with cameras.

(C2) Suggest appropriate camera parameters adaptively. Different from general
data videos, videos in geographic visualization are more concerned about the
performance stage (i.e., the proportion of objects in the scene and the
viewing perspective (Shi et al., 2021a)). Suppose the designer wants to show
the highest bar in a bar visualization on the map. In that case, the scene
will contain the whole bar visualization as the indispensable context (e.g.,
(Xaquín G.V., Graham Roberts, Ford Fessenden, Hannah Fairfield, Lisa Waananen
and Amy Schoenfeld, 2012)). Non-experts easily get confused about editing
obscure parameters of camera movements to choose appropriate performance
stages for the selected geospatial targets. We should recommend adaptive
graphics parameters for camera movements on the basis of geospatial targets
themselves and the related geographic environments on the map.

(C3) Depict the narrative timeline of a geographic data video. In geographic
data videos, designers always focus on exploring the spatial relationship and
presenting spatial context. General video authoring tools (e.g., Adobe After
Effects (Adobe Inc., 2022)) typically support keyframe-based specifications
for authoring the animation. However, traditional keyframes cannot guide users
about the spatial context in geographic visualizations. We aim to preserve
every spatial context users tend to narrate to highlight geographic
visualizations. We propose a hierarchical timeline, including time, spatial
context, and camera movements. Such a timeline allows users to recognize the
current narration sequence and edit the camera and its duration based on the
spatial context.

###  4.2. Video Modeling and System Workflow

As illustrated in Figure 3, a geographic data video can be defined as a series
of scenes. Each scene comprises one or more camera designs combined by using
layouts, such as side-by-side and picture-in-picture. For each camera design,
a camera shot or a combination of camera shots is chosen to present one or
multiple geospatial target(s) with one of the narrative purposes.

Figure 3. The video model in GeoCamera. A geographic data video is a series of
scenes. Each scene comprises one or multiple camera designs. Each camera
design includes one or two camera shots. Each camera shot serves a geospatial
target.

Figure 3. Fully described in the text.

Given the geographic visualization, GeoCamera first needs to generate a list
of camera movements used for specific geospatial targets. Users first select
the geospatial targets to be presented in the scene and then choose a set of
camera movements for each target. GeoCamera records the selections and
visualizes them into a location-camera hierarchy timeline. GeoCamera supports
flexible interactions to edit semi-automatically generated concrete camera
effects individually in the data video from the graphics parameters and the
timeline. Lastly, GeoCamera assembles the list of camera movements into a data
video.

###  4.3. Creating Camera Movements

####  4.3.1. The main previewer of interactive geographic visualizations.

GeoCamera provides a canvas (Figure 1A) to visualize the geographic
visualization and preview camera movements in the map individually and
sequentially. In the beginning, this canvas only contains an interactive map
without any encoded data as an initial hint of geographic visualizations.
After users import their geographic data and select a geographic visualization
type (e.g., hexagon), the tool will draw the selected visualization on the map
with predefined visual attributes (e.g., color). Users are allowed to adjust
the perspective and scale of the map (Basic Control) and select their
preferred geospatial targets (Interactive Selection) through basic controls
and target selections.

Basic control. Consistent with most map authoring tools (e.g., deck.gl
(Vis.gl, 2022a)), we use an interactive map through the whole authoring
process. Users can zoom the map by scrolling. They can also pan and rotate the
map by clicking and dragging the cursor. These primary interaction handlers
allow users to adjust the map to a favorite state in a non-programming way.

Target selection. With basic controls, users can opt for a single object by
clicking it on the interactive visualization. The location of the object will
be recorded based on the underlying geographic map. The user can also select a
specific region or multiple targets by using a lasso selection (Figure 1A).
Besides, we found users would probably alter the map mistakenly with
imperceptible mouse operations, thereby making it challenging for the users to
select targets under the same viewpoints. Therefore, we designed an error-
tolerance mechanism to ensure that the viewpoints stays in the same state as
it was before. Users can save the current viewpoint by clicking the “snapshot”
button and then go back to the previously saved map state by clicking the
“reset” button (Figure 1A).

####  4.3.2. Library of camera shots.

Based on the previous investigation of real-world videos, we designed a
library of camera shot templates (Figure 1B). To facilitate the camera effect
authoring for users, we create different catalogs for various narrative
purposes defined in the design space. Each catalog includes several camera
shots collected and summarized from the examples. Note that different camera
movements that belong to the same type can serve different narration purposes
with various parameters. Additionally, we expand the current categories
inspired by films (Katz and Katz, 1991; Vineyard, 2000) to enrich camera
shots, such as combined camera movements (e.g., Arc with Tilt shot). For the
same camera shot serving different narrative purposes, the system provides
different configuration settings for users (Figure 4) to achieve their
narrative goals.

Based on the observation from the example videos that a central object can be
served by different camera movements, GeoCamera enables users to craft camera
movements one by one for specific geospatial targets. After users determine
their targeted objects, they can select a camera shot to describe these
targets for storytelling.

For example, if a user wants to build an overview of multiple objects in the
geographic visualization, then they need to select a camera shot under the
narrative purposes of “Overviewing multiple targets” in the library. A default
camera shot will be added to the timeline after the user selects the target in
the geographic visualization and the narrative purpose. The default camera
shot is determined on the basis of the frequency statistics of the collected
real-world examples. As shown in Figure 2A, the most frequent camera shots
will be chosen as default when the narrative purpose and geospatial target is
confirmed. The default camera movement for the targets will be automatically
created on the timeline (Figure 1D) after a few clicks and will be documented
in the camera list (Figure 1C). The user can change the camera shot type for
the camera movements by clicking the buttons on the camera shot list. By
repeating this process, users can build their whole narration with a sequence
of camera movements using their corresponding geospatial targets.

Figure 4. Different suggested options for a _Push In_ shot that serve
different narrative purposes: (A) Emphasizing a Target and (B) Increasing the
Dynamic. Different intensities of the movement influence the final effects of
the camera movement.

Figure 4. Fully described in the text.

####  4.3.3. Adaptive parameter setting for camera movements.

The camera movement in our system depends on the narrative purpose, the camera
shot, and the geospatial target selected. It is achieved by interpolating from
the initial state to the final state of the camera. However, configuring the
states of a camera is a challenging and time-consuming task for a lay person.
Thus, we introduce an automation service that adaptively sets up camera
movement parameters for the chosen geospatial target(s) after the user
determines the camera shots under a specific narrative purpose. This step
waives the user’s setting of a camera’s states, which largely simplifies the
camera movement creation with only a few clicks.

The initial camera state of a camera movement can be set by considering the
current viewpoints or the final camera state of the last movement. Meanwhile,
the final state of the camera is dynamically adjusted by the three aspects of
the selected geospatial target(s): the centroid, the bounding box, and the
related data of the selection. The centroid is generally used to define the
final location and the focus point of a camera. Moreover, the bounding box and
the data of the selected target(s) decide the altitude of the camera.

If the user selects a location target, then the centroid is its geographic
coordinates. For the region target case that is selected by a lasso selection
tool in the UI, we calculate the centroid of the polygon. Especially, for a
path target or multiple targets, we compute the bounding box first and then
obtain the centroid based on the bounding box.

After deciding the location and focal point of a camera, we need to achieve an
appropriate viewport for the target. The core idea is to ensure that the
viewport covers the target(s) the user wants to observe. For instance, for a
push-in shot, the final state of the camera movement will be set to vertically
focus on the target or the centroid of the bounding box. At the same time, the
altitude of the camera after the push-in movement will be guaranteed to a
value that satisfies a margin space for each side at 10% or above. Similarly,
we heuristically define a set of rules for the eight camera shots to adjust
the viewport of the camera dynamically based on different geospatial targets.

####  4.3.4. Location-camera hierarchy timeline.

In addition to playing a single camera movement, GeoCamera also supports
playing all camera movements sequentially. In the timeline panel (Figure 1D),
we visualize two timelines: a general timeline and a location-camera hierarchy
timeline. In general, we use a simple slider to show the playing process of
all cameras in the camera list. After users click the triangle button above
the general timeline, the previewer will play the camera on the geographic
visualization, and the general timeline will visualize the current playing
position.

However, the general timeline only shows the temporal information of the
entire camera sequence. We design a location-camera hierarchy timeline to
ensure the temporal information of each camera movement and each geospatial
target. We draw a timeline for every camera in the camera list, including a
fixed range encoded for the duration of the camera. To help users obtain
geospatial targets for each camera movement, we aggregate cameras with the
same geospatial targets and add a location-level timeline to show how long the
video focuses on the same targets. All the location-level timelines are
arranged chronologically. Therefore, camera-level timelines are in the same
order. Users can drag marks on camera-level timelines to change the cameras’
duration. When creating a story, having a break before starting a new topic is
common, but continuous sentences are used within a topic. Based on such
considerations from the narrative aspect, we identify a rule for continuity
between two continuous timelines when editing duration. An interval without
cameras between two continuous location-level timelines is possible. However,
the interval between two continuous camera-level timelines under the same
location layer is restricted. We also design a semantic overview of all
timelines. Location-level timelines are named with their related locations. If
we cannot obtain the exact name from the import data, the tool will show the
longitude and latitude of the location. Camera-level timelines are named as
their corresponding camera movements with their narrative tactics.

GeoCamera smooths transitions with linear interpolation among camera
movements. When users craft the camera movements, they adjust the duration of
movements at a single level. In terms of the final data videos, we set the
rule to avoid the temporal contradiction of camera movements for different
locations. However, an interval can appear between the ending state of the
last camera movement and the initial state of the next movement. Considering
the videos’ coherence, we provide a special camera movement called “linear
interpolation” to fill in the gaps in time. Different from other camera
movements, this movement focuses on no geospatial targets. Its performance in
the scene is the same as “fly to,” an automatic map animation that is commonly
used in map visualization tools (e.g., Mapbox (Mapbox, 2022)).

###  4.4. Configure Visual Effects

Figure 5. The user can edit a Tilt shot with two single previewers: (A) one is
used for editing the initial state of the camera movement, and (B) another is
for the final state. Then, the camera movement is interpolated between the two
states.

Figure 5 shows the editing interface for the initial and the final states of a
camera movement.

####  4.4.1. Camera configuration with single previewers.

Although GeoCamera recommended adaptive camera movements, users may be not
satisfied with the results. Based on the demand for flexible refinement,
GeoCamera enable users to edit every camera movement’s graphic parameters in
the camera list (Figure 1C). At the start, all camera parameters are folded
up. If users want to edit a camera movement, then they will click its menu to
expand its parameterization panel. These parameters are professionally related
to the map, such as zoom, pitch, and bearing. Users who are not familiar with
the geographic domain can easily get stuck in understanding these parameters.
Therefore, GeoCamera provides a single previewer approach to help users skip
this understanding process of refining a camera movement. By clicking the
“edit state” button, GeoCamera displays a small view with two canvas (Figure
5). One canvas shows the state of the geographic visualization in the
previewer when the camera starts moving, and another is for the ending. Both
canvases have similar basic interaction handlers as the main previewer. Users
adjust the states of two canvases with the mouse until they are satisfied.
GeoCamera records the adjusted states and resets the parameters of the camera
movement based on the recording.

To guide users to have a preliminary concept of how the camera movement works,
GeoCamera allows them to play each camera movement individually. If users want
to watch a camera movement while not completing a story, they just need to
click the small triangle button in the parameterization panel of this camera.
Then, in the previewer, GeoCamera plays the change in the geographic
visualization using the camera movement from the initial state to the ending
state.

####  4.4.2. Visualization authoring.

Our tool aims to provide an easier way to craft the camera animations in the
presentation of geographic visualizations. This method requires an
accomplished geographic visualization as the input. However, our target
audiences include those non-experts in the geographic data analysis domain.
They are unfamiliar with selecting the proper visualization types and drawing
the selected visualization on the map. With this in mind, GeoCamera wraps
graphing functions to support the authoring of several common visualizations
from the cleaned geographic data. Corresponding to the summarized geospatial
targets before, these visualizations include point, three-dimensional
rectangular, and line map. After importing geographic data (i.e., every object
has its exact latitude and longitude), users select an appropriate geographic
visualization according to data type. Users change the input of visualization
type with the drop-down menu in the Configuration Panel (Figure 1C), and then
GeoCamera displays the selected visualization in the Preview Panel (Figure
1A). In this way, users only need to import the geographic data rather than a
geographic visualization, which reduces the threshold of using GeoCamera.

The output of GeoCamera is a geographic data video that assembles all camera
movements. However, a successful data video contains visual and auditory
stimuli (Amini et al., 2017). Camera animation is a small component of visual
design. Although our work focuses on camera movements in geographic
storytelling, GeoCamera provides an additional layer for annotations for
design enhancement. Users can click the “Annotation” button in the
Configuration Panel (Figure 1C), and the panel will display all the current
textual annotations that correspond to each camera movement aligned by the
time sequence. The default annotation of a camera movement is none. The User
can choose a camera movement and edit the text. The tool will add an
annotation layer for this camera. We align the time of the added annotation
layer with its camera.

Figure 6. The data visualization about gun violence from 2013 to 2017 in the
United States.

Figure 6 shows a snapshot of the data visualization about gun violence.

##  5\. Evaluation

We evaluate GeoCamera through (1) two example cases with a range of camera
shots to showcase its expressiveness and (2) a user study to verify its
usability.

###  5.1. Example Cases

To demonstrate the generalizability of GeoCamera in different geographic
visualizations, we select a 2D and a 3D geographic data videos as our use
cases. The geographic visualization in the first example is a combination of
heatmap and scatter plot, showing more than 140,000 data points of people
being killed or injured in America. This visualization shows location-based
and region-based geographic data story. The data video is created based on the
geographic data story. Another example is a visualization showing COVID-19
cases in Hong Kong. We replicate a designer-crafted real-world data video,
exaplaining the case distribution. The video contains railway network map and
scatter plots on map and utilizes camera movements (e.g., Tilt and Arc shots)
for diverse geospatial targets of locations, paths, and regions that are
rarely observed in the 2D environment, thereby complementing the types of
camera movements in the previous example.

####  5.1.1. US Gun Violence

In this case, we attempt to use GeoCamera to create a data video to present
insight from the data about people killed or injured by guns in the United
States from 2013 to 2017. The visualization contains a scatter plot for every
single point in the dataset and a heatmap for the clustered points (Figure 6).
At the beginning of the video, we provide an overall introduction to the
dataset. We apply the frequently used _Push in_ shot in the Increasing
Dynamics category (Figure 2) to gradually immerse the audience in the
visualization scene. The camera movements for this category are mild, thereby
suggesting a long duration _Push in_ for a short-distance. The camera movement
is set to last 10 seconds with the annotation showing “The data are about
recorded gun violence incidents in the US between 2014 and 2017.”
Subsequently, we observe that the eastern United States has a relatively
higher chance of shooting than the other areas of America. We select the
eastern part to present this observation. We again use a _Push in_ shot, but
in a different category of Overviewing Multiple Targets. This time, the camera
pushes in relatively quickly and focuses on the selected region of America
within 2 seconds, and then remains for another 5 seconds to show the
annotation “The visualization indicates that eastern America has a higher
chance of shooting incidents than the other parts of America.” Then, we
compare the overall shooting rate between eastern America and middle America.
We select the middle part in the visualization, and the system automatically
suggest a _Push out_ shot in the timeline after we select the Making a
Comparison category to show the two regions together at the same time. The
camera movement takes about 2 seconds, and remains for another 2 seconds with
the annotation showing “However, the middle part of America is relatively
safe.” Subsequently, we aim to show another observation that the number of
gun-related violence outstands in California. We replace the default camera
shot (i.e., _Push In_ shot) with the _Camera Roll_ in the Emphasizing a Target
category to roll the camera while maintaining its focus on the center of
California. This eight-second camera movement is about “The number of gun-
related violence evidently outstands in California,” and the audience is
provided sufficient time to understand the surroundings of the mentioned
State. What follows next is the most dangerous city with the highest number of
shootings. We further use another _Push In_ shots in the same category to draw
attention to Chicago. The camera gradually moves to Chicago for an additional
3 seconds to show that “Unexpectedly, Chicago is the most dangerous city with
the highest number of gun-related violence.” Finally, we select a _Pull out_
shot in the Increasing Dynamics category to leave the visualization scene in
10 seconds, “Overall, there was nearly 226 thousand gun-related cases were
recorded in the US, and 60 thousand people were killed.” The total duration
time of the final video with the six camera movements is 43 seconds. The time
distribution of the corresponding six narrative purposes (i.e., Increasing
Dynamics, Overviewing Multiple Targets, Making a Comparison, Emphasizing a
Target, Emphasizing a Target, and Increasing Dynamics) is shown in the
timeline, indicating the story flow of the generated video. More details are
presented in the supplementary material.

####  5.1.2. Reproducing Camera Movements

To demonstrate the expressiveness of the camera movements in our system, we
reproduce the camera movements in a data video introducing the COVID-19 status
in Hong Kong during February 2022. We use the manual mode that does not
require specifying the narrative purposes for the camera movements, given the
circumstance that the camera effects have already been provided by the sample
video. The manual mode is designed for the detailed control of generating
specific camera effects from scratch without considering the narrative
purposes. The data video contains 32 camera movements covering the _Push in_ ,
_Arc_ , _Camera roll_ , _Zoom out_ , and _Pan_ shots (Figure 7). The set of
camera movements form a three-minute data video. More details about the
reproduction results are shown in the supplementary material.

Figure 7. The snapshots of the sample camera movements taken from the real-
world geographic data video. The camera shots identified in these camera
movements: Push In shot, Arc shot, Pull Out shot, and Camera Roll shot.

Figure 7. Fully described in the text.

###  5.2. User Study

We conducted a user study to validate whether or not nonprofessionals could
easily create different camera movements for a data story and examine if there
were any usability issues for improving the system.

####  5.2.1. Participants

We recruited eight participants (two females and six males) with knowledge in
geographic visualization, denoted as P1-P8, for this study. They involve
graduate students studying data visualization and employees that work at an IT
company in a role related to data visualization. All the participants took no
part in any activity related to the system design or preliminary study and
reported that they had no or limited experience in camera movement design.

####  5.2.2. Visual Materials and Data

We provided the participants with slides that introduced each category of our
design space with examples. The slides were used as the teaching material, and
the participants were encouraged to browse the slides when creating camera
movements with our system.

The data used to create data videos in the user study include personal injury
road accidents in the United Kingdom from 1979. The data are aggregated and
visualized with a hexagon-based heatmap (Figure 1(a)). The color and height of
a hexagon are determined based on the objects it contains. In addition to data
visualization, we also provided the participants various pre-extracted story
insights (e.g., “London has the most road accidents” and “The road accident in
Scotland area is very low”) and their corresponding visualization to maintain
the focus of the user study on planning and creating camera movements.

####  5.2.3. Procedure

The user study contains three sessions, as follows: (1) a tutorial session to
familiarize with GeoCamera, (2) a creation session to experience the authoring
process, and (3) a post-study evaluation to measure their subjective
preference on the utility of the system.

Tutorial. We started the user study with a 15-minute introduction explaining
our design space. Subsequently, we provide a 20-minute demonstration on the
use of the GeoCamera system, including its functions for setting up camera
movements and related interactions in camera movement editing with an example
dataset. Then, the participants were asked to freely explore every function as
well as the interaction of the system and raise questions whenever necessary.
After familiarizing with the tool, we introduced the formal dataset for the
user study, the encoding scheme of the proposed visualization, and the
corresponding story insights in detail for building camera movements with
GeoCamera.

Creation. After the tutorial, we asked the participants to use GeoCamera to
make their own geographic data videos based on the pre-extracted story
insights. The participants could browse the slides as a reference, and they
can seek guidance for using the system. When finished, each participant shares
and explains his or her data video. The creation phase lasts for approximately
15-30 minutes.

Post-study Survey and Interview. When the exploration and creation of the
camera movements were completed, the participants were asked to answer a post-
study questionnaire using a 5-point Likert scale (1 for strongly disagree and
5 for strongly agree). The questionnaire intends to assess the usefulness,
ease of use, and satisfaction(Lund, 2001) of GeoCamera. Finally, we conducted
a semi-structured interview to collect qualitative feedback from each
participant.

All participants completed the entire study at approximately 75-90 minutes and
were compensated with a gift card worth $15 at the end of the interview
session.

Figure 8. Video snapshots from the user study generated with GeoCamera. Each
camera movement contains two snapshots of the initial and the final state.

Figure 8 shows the snapshots of a user study example with six camera movements
generated with GeoCamera.

####  5.2.4. Results and Findings.

Figure 9. Ratings for system usability on a 5-point Likert scale (N=8). The
middle column shows the detailed questions. The right column displays the
average and standard deviations.

Figure 9 shows the questions for system usability and their rating
distribution from the user study.

All the participants can complete the authoring tasks with minimal guidance.
Figure 8 shows the snapshots of an example video generated by a participant
with GeoCamera.

Then, we collected the participants’ subjective ratings in the form of a
10-question survey and qualitative feedback for GeoCamera from the semi-
structured interview. Figure 9 presents the questions and average user
ratings. Generally, all participants agree that GeoCamera is a useful tool to
create expressive geographic data videos with intuitive guidance and is easy
to learn. They show a strong willingness to use GeoCamera to simplify the
prototyping process.

Usability. All participants agreed that our tool eases manual efforts in the
geographic data video creation process. P4 mentioned that “It does not require
writing code or setting complex parameters.” Our tool provides a code-free
procedure to allow video creators to be more productive. Furthermore, the
participants reported the effectiveness of the automatic parameter setting for
the camera movements. P1 stated that “Taking five minutes to generate a one-
minute video is very efficient. It can save a lot of time.” P5 described the
tool’s benefit for nonprofessionals: “I feel the tool is efficient for the
nonprofessionals given the story to be told.”

Two participants showed their preference of the Timeline Panel. P1 described
how the timeline provided a general understanding of the video’s structure:
“The timeline can show the distribution of the cameras for telling a story.
Thus, I can have a general idea of the video’s purpose.” P5 mentioned, “The
timeline control is easy to use because it follows the design of regular video
editing software.”

Learnability. Most of our participants agreed that learning and using the
system and the integrated functions for camera movements are easy. P1
responded very positively to learning our tool as a novice user: “I can
quickly learn the visual effects of different camera movements after exploring
the tool.” We also heard some descriptions of how our tool satisfies their
needs to create camera movements with automatic parameter settings and self-
controlled functions. P7 commented that “The recommended parameters become
more useful when the complexity of the camera movement increases.” P8 also
stated that “The system provides the user with automation and control at the
same time.” For the whole creation process with the tool, two participants (P4
and P8) suggest that our system ensures a smooth flow in creating and editing
operations in GeoCamera: “The overall (camera movement creation) process is
smooth and natural.”

Expressiveness. Overall, the participants agreed on the expressiveness of our
tool. They indicated that the proposed design space of the camera movements in
geographic data videos satisfies their requirements for crafting videos. The
participants also suggested that GeoCamera could help discover new camera
movements and learn the narrative purpose that the camera movements could
serve.

First, all participants suggested that our design space for camera movements
in geographic data videos is “clear and easy to understand”. P4 stated that
“Novice user may not know where to start to generate different camera effects
for geographic data visualization.” They described how the design space helped
them create geographic data videos. P6 noted that our design space “makes
sense” and is “reasonable to describe from the what, why, and how
perspectives.” In particular, the narrative purposes in our tool can provide
guidance to obtain a “more logical” video structure. P8 emphasized the usage
of narrative purposes: “The narrative purposes basically satisfy my intent to
tell stories through camera shots.”

Second, our participants expressed their satisfaction with the diversity of
the camera movements generated by our system. P1 stated that “The current
camera effects are close to the common camera movements, including those in
the data videos.” We observed that some participants attempted different
camera shots under a specific narrative purpose prior to the final decision,
such as P6: “I did not know many of the camera shots before. The system
organizes the camera shots by purpose and teaches me how to use them.” P5
mentioned the final performance of the crafted videos and agreed that the
final video is “good enough for general purposes, such as reporting findings
and showing insights.” P8 showed great interest in “some unrealized camera
shots” suggested in the left panel. He commented that the camera shots
organized for narrative purposes could increase creativity in the video
authoring process.

Flexibility. Many participants appreciated the flexibility of our tool for
editing camera movements. P3 agreed that our tool provides sufficient degree
of freedom to create camera movements: “The tool with camera module I
previously used only provides limited choices of camera and usually fixed
path.” Another participant (P1) also likes the user interface for camera
movement editing: “When editing the initial and final states, the camera
movements can be accurately controlled.” P7 liked the exporting and importing
functions in the tool because they “make the camera movements reusable”.

Mixed-initiative Authoring. In the survey, we found that many participants
preferred the default mode with adaptive camera movement settings. In the
interview session, we also asked the participants about their preference for
the default mode with adaptive parameter settings and the manual mode for
crafting camera movements. The participants stated that “the adaptive camera
movements are convenient” (P2) and that they would love to “use auto mode
first and then fine-tune the details” (P4). However, we also discover that the
adaptive parameter setting is not always the first option, especially for
proficient users. P7 preferred to edit camera movements manually: “For simple
camera movements, I would use the manual mode.” As a proficient user, P8
commented that “When I already know what effect I want to achieve, I use the
manual mode more frequently.”

Future Usage. In our interview, the participants expressed their strong
willingness to use the tool in the future. P1 said “This technique can be used
to generate highlight replays in e-sports. It greatly reduce efforts to show
the exciting moments in a virtual space.” P4 said “After modeling and editing
in a 3D scene, the method can simplify the demonstration process.” P8 has
experiences in drone photography, and he mentioned that “Shooting with a drone
is not easy. Maybe I could use this tool for planning and previewing before
shooting from my drone.” The participants also provided suggestions for future
system improvement from visual design and camera creation. P5 noted that “The
annotation could be better by matching the length of the text and the duration
of the camera movements.” P3 said “Combining different camera movements by the
dragging and dropping interactions would be helpful.” P2 and P3 voiced their
confusion about the camera’s moving trajectory: “It would be clearer to have
an overview of the camera’s moving trajectory in the visualization.” P4 stated
that the camera system could be integrated into a real-time monitoring system
for “…presenting anomaly information and tracking.” P7 suggested “building a
fully automatic system from detecting the insights and creating a fast-
preview.” P2 inspired us to improve camera movements by “considering and
optimizing the overall speed.” P3 and P7 suggested making the intermediate
process of a camera movement more configurable (e.g., changing the path or
setting an ease function for the motion) and storing the customized ones in
the system. P6 and P7 expected that the system could consider artistic aspects
of the camera effects. P8 suggested that “Whether we can change the focus of
the camera and exit the current scene using blur effects.” The detailed
comments about the user study are listed in the supplementary material.

##  6\. Discussion

In this section, we discuss the current limitations of our study and recommend
future directions.

Understanding the best practices in authoring geographic data video. We treat
our design space as a probe for camera movements in geographic data videos
rather than a comprehensive characterization. First, a corpus can never be
comprehensive. More instances may expand the sub-categories of each dimension.
Second, we only analyzed videos lasting 3-10 minutes. Therefore, the design
space may not be valid for long videos or short-form videos, such as GIFs. In
addition, the camera movement recommendations in GeoCamera are based on the
statistical frequencies of the combinations from the corpus. However, this
approach is not always optimal for creating a compelling and persuasive camera
movement given a narrative purpose. Further investigation on global
optimization for camera movement configurations of different narrative
purposes is promising to improve the overall engagement of the generated data
videos.  Last, when contextualizing existing taxonomies into geographic data
videos, we found that not all items fit and require adaptations. For instance,
not all established camera effects were identified in the corpus, e.g., dolly
zoom. And the “Increasing Dynamics” intent does not fit in the narratology. We
anticipate the future studies to evaluate and extend the design space. For
example, in-depth interviews with practitioners may reveal emerging tactics of
camera movements that cover other dimensions to transit or surface geographic
data insights. As the design space categorizes general narrative purpose,
geospatial target, and camera shots in geographic data stories, a closer
examination of a particular category remains promising.

Extending GeoCamera to multifaceted authoring scenarios. While the usability
of GeoCamera is recognized by the target users in the user study, its design
might have overlooked diversified authoring scenarios (Kosara and Mackinlay,
2013; Lee et al., 2015; Brehmer and Kosara, 2021). First, our design
considerations were largely based on a profile of an average user. We strive
to reduce the difficulty for amateur video makers in crafting camera
movements, which is also beneficial for rapid prototyping when designing
formal presentations. However, professionals, such as data journalists, may
require more creativity support in different design phases (Frich et al.,
2019). For instance, the interoperability of GeoCamera should be improved
given that practitioners often iterate between tools to achieve higher
expressibility (Bigelow et al., 2016). Second, our assumption on the workflow,
i.e., to connect a given sequence of story pieces covering data insights, can
be overly simplified. Prior research (Lee et al., 2015; Chen et al., 2018)
suggested that data storytelling is a much more complicated process,
encompassing stages including exploring the dataset and selecting and
organizing the findings. Thus, recommending data insights and story structures
to alleviate human labor remains promising (Chen et al., 2022a).

Enriching editorial layers for storytelling. Thus far, the research in
authoring tools for data videos is still in its infancy (Cao et al., 2020). We
initially contributed an approach for average users to author the camera
movements in geographic data videos. Notwithstanding, many other design
features also constitute a successful geographic data video. We envision
future tools to encapsulate a broader set of editorial layers for producing
more engaging, persuasive, and compelling results. For instance, visual
embellishments enhance the aesthetics and imply the narrative topic (Chen et
al., 2022b). Animated narratives can better illustrate a concept (Shi et al.,
2021a) or express certain emotions (Lan et al., 2021, 2022). In terms of the
cinematic effect, our current model of geographic data video supports picture-
in-picture by splitting the video into halves from the middle. The model can
be further extended to enable more flexible scene arrangements. Aligning the
video with background music also remains interesting (Xu et al., 2022).

##  7\. Conclusion

This paper presents GeoCamera, which is a geographic data video authoring tool
that empowers users to tell appealing geographic stories with tailored camera
movements. Based on a design space that summarizes diverse narrative purposes,
geospatial targets, and camera shots, GeoCamera facilitates easy creation of
coherent camera movements by allowing users to simply select the objects on
the map and specify an appropriate narrative purpose. GeoCamera has been
evaluated with case and user studies, showing promising expressiveness and
usability in helping its users to author diverse camera movements
effortlessly. In the future, we would like to extend GeoCamera to cover more
complex authoring scenarios with exploration and creativity support, while
providing enriched editorial layers for more engaging, persuasive, and
compelling geographic storytelling.

###### Acknowledgements.

The authors would like to thank the experts and participants for their help in
the project, as well as the anonymous reviewers for their valuable comments.
This work is partially supported by Hong Kong RGC GRF Grant (No. 16210321), a
grant from MSRA, and NSFC (No. 62202105).

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