Article

Tangible Landscape: cognitively grasping the flow of water

Abstract

Complex spatial forms like topography can be challenging to understand, much less intentionally shape, given the heavy cognitive load of visualizing and manipulating 3D form. Spatiotemporal processes like the flow of water over a landscape are even more challenging to understand and intentionally direct as they are dependent upon their context and require the simulation of forces like gravity and momentum. This cognitive work can be offloaded onto computers through 3D geospatial modeling, analysis, and simulation. Interacting with computers, however, can also be challenging, often requiring training and highly abstract thinking. Tangible computing – an emerging paradigm of human-computer interaction in which data is physically manifested so that users can feel it and directly manipulate it – aims to offload this added cognitive work onto the body. We have designed Tangible Landscape, a tangible interface powered by an open source geographic information system (GRASS GIS), so that users can naturally shape topography and interact with simulated processes with their hands in order to make observations, generate and test hypotheses, and make inferences about scientific phenomena in a rapid, iterative process. Conceptually Tangible Landscape couples a malleable physical model with a digital model of a landscape through a continuous cycle of 3D scanning, geospatial modeling, and projection. We ran a flow modeling experiment to test whether tangible interfaces like this can effectively enhance spatial performance by offloading cognitive processes onto computers and our bodies. We used hydrological simulations and statistics to quantitatively assess spatial performance. We found that Tangible Landscape enhanced 3D spatial performance and helped users understand water flow.
TANGIBLE LANDSCAPE: COGNITIVELY GRASPING THE FLOW OF WATER
B. A. Harmona
, A. Petrasovaa, V. Petrasa, H. Mitasovaa, R. K. Meentemeyera
aCenter for Geospatial Analytics, North Carolina State University - (baharmon, akratoc, vpetras, hmitaso, rkmeente)@ncsu.edu
KEY WORDS: embodied cognition, spatial thinking, physical processes, water flow, hydrology, tangible user interfaces, user experi-
ment, 3D
ABSTRACT:
Complex spatial forms like topography can be challenging to understand, much less intentionally shape, given the heavy cognitive load
of visualizing and manipulating 3D form. Spatiotemporal processes like the flow of water over a landscape are even more challenging
to understand and intentionally direct as they are dependent upon their context and require the simulation of forces like gravity and
momentum. This cognitive work can be offloaded onto computers through 3D geospatial modeling, analysis, and simulation. Inter-
acting with computers, however, can also be challenging, often requiring training and highly abstract thinking. Tangible computing
– an emerging paradigm of human-computer interaction in which data is physically manifested so that users can feel it and directly
manipulate it – aims to offload this added cognitive work onto the body. We have designed Tangible Landscape, a tangible interface
powered by an open source geographic information system (GRASS GIS), so that users can naturally shape topography and interact
with simulated processes with their hands in order to make observations, generate and test hypotheses, and make inferences about
scientific phenomena in a rapid, iterative process. Conceptually Tangible Landscape couples a malleable physical model with a digital
model of a landscape through a continuous cycle of 3D scanning, geospatial modeling, and projection. We ran a flow modeling exper-
iment to test whether tangible interfaces like this can effectively enhance spatial performance by offloading cognitive processes onto
computers and our bodies. We used hydrological simulations and statistics to quantitatively assess spatial performance. We found that
Tangible Landscape enhanced 3D spatial performance and helped users understand water flow.
INTRODUCTION
Understanding physical processes
Physical processes like the flow and dispersion of water are chal-
lenging to understand because they unfold in time and space, are
controlled by their context, and are driven by forces like gravity
and momentum. The flow of water across a landscape is con-
trolled by the morphological shape and gradient of the topogra-
phy. It is challenging to understand how water will flow across a
landscape because one must not only understand how the shape
and gradient of the terrain control the flow and dispersion of water
locally, but also how water will flow between shapes and gradi-
ents – how the morphology is topologically connected. Under-
standing a physical process requires thinking at and across multi-
ple spatial scales simultaneously.
This cognitive work can be offloaded onto computers through 3D
geospatial modeling, analysis, and simulation. Interacting with
computers, however, can also be challenging, requiring training
and highly abstract thinking that adds a new cognitive burden.
Tangible interfaces for GIS
Tangible interfaces are designed to make interaction with com-
puters easier and more natural by physically manifesting digital
data so that users can cognitively grasp it as an extension of their
bodies, offloading cognition onto their bodies. In embodied cog-
nition higher cognitive processes are built upon and grounded in
sensorimotor processes – upon bodily action, perception, and ex-
perience – so thinking can be performed subconsciously through
action and extended with tools (Kirsh, 2013). Tangible interfaces
have been developed for geographic information systems (GIS) to
ease the cognitive burden of complex spatial reasoning, spatial vi-
sualization, and human-computer interaction by embodying these
Corresponding author
processes. Theoretically tangible interfaces for GIS should help
users understand environmental processes by giving multidimen-
sional geospatial data an interactive, physical form that they can
cognitively grasp and kinaesthetically explore in space and time.
In a seminal paper Ishii and Ullmer envisioned tangible user in-
terfaces that would ‘bridge the gap between cyberspace and the
physical environment by making digital information (bits) tangi-
ble’ (Ishii and Ullmer, 1997). They described ‘tangible bits’ as
‘the coupling of bits with graspable physical objects’ (Ishii and
Ullmer, 1997). Tangible interfaces like Urp (Underkoffler and
Ishii, 1999), Illuminating Clay (Piper et al., 2002a), and Sand-
Scape (Ratti et al., 2004) enriched physical models of urban spaces
and landscapes with spatial analyses and simulations like wind
direction, cast shadow, slope, aspect, curvature, and water direc-
tion in order to enhance and streamline spatial thinking, design,
and decision-making.
Many of the analyses used in Illuminating Clay were adapted
from the open source GRASS GIS project (Piper et al., 2002b)
and eventually Illuminating Clay was coupled with GRASS GIS
to draw on its extensive libraries for spatial computation. The aim
of coupling Illuminating Clay with GRASS GIS was to ‘explore
relationships that occur between different terrains, the physical
parameters of terrains, and the landscape processes that occur in
these terrains’ (Mitasova et al., 2006). The effort to couple a
physical landscape model with GRASS GIS led to the develop-
ment of the Tangible Geospatial Modeling System (Tateosian et
al., 2010) and Tangible Landscape (Fig. 1) (Petrasova et al., 2014,
Petrasova et al., 2015).
Tangible Landscape Tangible Landscape – a tangible user in-
terface powered by GRASS GIS – couples a physical and digital
model of a landscape through a continuous cycle of 3D scanning,
geospatial modeling, and projection so that users can intuitively
interact with the modeled landscape in near real-time (Fig. 2).
The physical model is often made of polymer-enriched sand so
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B2, 2016
XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XLI-B2-647-2016
647
Figure 1: Tangibly modeling the flow of water with Tangible Landscape
that users can easily sculpt forms in a medium that will hold its
shape, has good plasticity, and has a familiar feel and aesthetic.
As users sculpt the physical model the model is 3D scanned and
interpolated in GIS as a digital elevation model. The digital el-
evation model is used to compute geospatial analyses, models,
and simulations, which are then projected back onto the physical
model – all in near real-time. Conceptually, this enables users
to hold a GIS in their hands – feeling the shape of the earth,
sculpting its topography, and directing the flow of water. This
should enable users to naturally model topography and interact
with simulated physical processes in a rapid, iterative process of
observation, hypothesis generation and testing, and inference.
GRASS GIS
geospatial computation
3D scanningprojection
interaction
point cloud
processing
Figure 2: How Tangible Landscape works: a near real-time
feedback cycle of interaction, 3D scanning, point cloud
processing, geospatial computation, and projection
Cognitively grasping spatial processes
The aim of this research was to study how tangible interfaces
for GIS mediate spatial thinking about landscape processes like
water flow. Theoretically a tangible interface for a GIS that en-
ables intuitive digital sculpting while providing analytical feed-
back should allow users to dynamically explore how topographic
form influences landscape processes. With a physical model one
can cognitively grasp topographic form, offloading the cognitive
work of understanding and shaping topographic form onto the
body. A GIS can offload the cognitive work of simulating com-
plex physical processes like the flow of water through computa-
tion. With these physical and computational affordances com-
bined in a tangible interface for GIS users should be able to more
easily understand and shape spatial processes. We empirically
tested how a tangible interface for GIS mediated spatial perfor-
mance in a water flow modeling experiment.
METHODS
We conducted a terrain and water flow modeling experiment in
which participants tried to recreate a given landscape. We quan-
titatively assessed their spatial performance using hydrological
simulation, summary statistics, and cell-by-cell statistics.
Flow modeling experiment
In the experiment 11 participants were asked to sculpt a given
landscape using different technologies – first using Vue, a trian-
gulated irregular network based 3D modeling program designed
for intuitive terrain sculpting, and then using Tangible Landscape.
The participants were asked to model a real landscape – a region
of Lake Raleigh Woods in Raleigh, North Carolina – using each
technology. We selected a region of the landscape with distinc-
tive, clearly defined landforms – a central ridge flanked by two
stream valleys (see Fig. 3). The digital elevation model (DEM)
for this region was derived from a 2013 airborne lidar survey us-
ing the regularized spline with tension interpolation method.
In the 1st exercise each participant had 10 minutes to digitally
sculpt the topography of the study landscape in Vue’s terrain ed-
itor using a physical model as a reference (see Fig. 4a). Partic-
ipants were given a 2 minute introduction to terrain sculpting in
Vue and then 10 minutes to experiment and become familiar with
the interface before beginning the exercise. Vue’s terrain edi-
tor was designed to emulate sculpting by hand – there are tool
brushes that are analogous to basic actions in sculpture including
pushing, pulling, and smoothing.
In the 2nd exercise each participant had ten minutes to sculpt the
study landscape in polymer enriched sand using Tangible Land-
scape’s water flow simulation as a guide (see Fig. 4b). As par-
ticipants sculpted they could switch between projected maps of
either the a) simulated water flow across the given landscape that
they were trying to replicate b) or the simulated water flow across
the scanned landscape.
Figure 3: Our reference landscape with simulated water flow
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B2, 2016
XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XLI-B2-647-2016
648
(a) (b)
Figure 4: (a) Digitally sculpting with Vue in the 1st exercise and (b) tangibly sculpting with Tangible Landscape in the 2nd exercise
Implementation
We used GRASS GIS both for geospatial computation with Tan-
gible Landscape and for the analysis of the results of the exper-
iment. The algorithms used in GRASS GIS are based on peer-
reviewed scientific publications and have been transparently im-
plemented with free, publicly available, version controlled source
code. Open, transparent algorithms are needed to fully reproduce
computational science (Rocchini and Neteler, 2012). In this ex-
periment we used the overland flow hydrologic simulation using
a path sampling method (SIMWE) implemented in GRASS GIS
as the module r.sim.water.
Shallow overland flow We simulated shallow overland water
flow controlled by spatially variable topography, soil, landcover,
and rainfall parameters using the SIMWE model to solve the con-
tinuity and momentum equations for steady state water flow with
a path sampling method. Shallow water flow can be approxi-
mated by the bivariate form of the St Venant equation:
∂h(r, t)
∂t =ie(r, t)− ∇ · q(r, t)(1)
where:
r(x, y)is the position [m]
tis the time [s]
h(r, t)is the depth of overland flow [m]
ie(r, t)is the rainfall excess [m/s]
(rainfall infiltration vegetation intercept)
q(r, t)is the water flow per unit width [m2/s].
By integrating a diffusion term ∝ ∇2[h5/3(r)] into the solution
of the continuity and momentum equations for steady state water
flow diffusive wave effects can be approximated so that water can
flow through depressions.
ε(r)
22[h5/3(r)] + ∇ · [h(r)v(r)] = ie(r)(2)
where:
ε(r)is a spatially variable diffusion coefficient.
This equation is solved using a Green’s function Monte Carlo
path sampling method (Mitasova et al., 2004).
Data collection and analysis
We used summary and cell-by-cell statistics to compare the re-
sults of each exercise using the reference landscape as a base-
line. The digital models sculpted in the 1st exercise were manu-
ally georeferenced and imported into GRASS GIS as point clouds
for modeling and analysis. The final state of the physical models
sculpted in 2nd exercise were automatically 3D scanned, georef-
erenced, and imported into GRASS GIS as points clouds with
Tangible Landscape for modeling and analysis. All of the point
clouds were randomly resampled and interpolated as DEMs us-
ing the regularized spline with tension interpolation method (Mi-
tasova et al., 2005). For each model we simulated shallow over-
land water flow, identified depressions, and computed the differ-
ence between the modeled and reference water flow depth. To
compute the difference we subtracted the modeled values from
the initial, reference values. We identified and computed the
depth of depressions by generating a depressionless DEM and
then calculating the difference between the DEM from the de-
pressionless DEM. Then we computed the mean, sum, and max-
imum of the water depths, the depressions, and the difference in
water depths for each exercise in the experiment. We visually as-
sessed the spatial pattern of water flow and continuity using these
maps. In order to quantitatively compare how well the modeled
streams matched the reference stream we computed the distance
between cells with concentrated water flow in the reference depth
map and mean of the modeled depth maps. First we extracted
points for each cell with high water depth values (>= 0.05 ft) in
the reference water depth raster and the mean water depth raster
for each exercise. Then we calculated the minimum distance be-
tween the reference points and the nearest points from each ex-
ercise. In order to quantitatively compare the continuity of flow
we computed the percentage of cells with depressions for each
exercise.
Code and data
As a work of open science we invite you to replicate or build
upon this experiment by using or adapting our tangible inter-
face, experimental methodology, code, and data. The python
scripts for data processing and analysis used in this experiment
are available on GitHub at https://github.com/baharmon/
tangible-water-flow released under the GNU General Pub-
lic License (GPL). See the documentation in the GitHub reposi-
tory for detailed instructions for replicating this experiment. The
data used in this experiment are available on the Open Science
Framework at osf.io/zv3t6 under the Creative Commons Zero
license.
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B2, 2016
XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XLI-B2-647-2016
649
GRASS GIS is available at https://grass.osgeo.org/ un-
der the GNU GPL. Tangible Landscape is available at https://
github.com/ncsu-osgeorel/grass-tangible-landscape
under the GNU GPL. To build your own Tangible Landscape
see the documentation in the GitHub repository and refer to the
book Tangible Modeling with Open Source GIS (Petrasova et al.,
2015).
While the rest of the software used in this research is free and
open source, Vue 1is proprietary software. We used Vue’s terrain
editor for this study because it was designed specifically for in-
tuitive terrain modeling, but open source 3D modeling programs
like Blender 2could be substituted for Vue.
RESULTS
The models sculpted with Tangible Landscape more accurately
replicated the flow of water over the study landscape. The digi-
tally sculpted models tended to have more diffuse water flow and
more water pooling in depressions, whereas the tangibly sculpted
models tended to have more concentrated flow in stream channels
and less pooling in depressions. Furthermore, the spatial distribu-
tion of water flow on the tangibly sculpted models more closely
fit the distribution of water flow on the reference landscape.
Exercise Mean Max Sum
Reference 0.01 0.47 786.25
Digital 0.21 1.83 3.60
Tangible 0.27 2.31 4.60
Table 1: Highest mean, maximum, and summed water depths (ft)
Exercise Mean Sum
Digital 21.13 29244
Tangible 18.59 25724
Table 2: The minimum distances from reference stream cells to
the nearest modeled cell with concentrated flow (ft)
Exercise Max Sum
Reference 0.10 0.62
Digital 24.5 53
Tangible 22.2 44
Table 3: Highest max and summed depth of depressions (ft)
Exercise Cells
Reference 0%
Digital 44%
Tangible 17.66%
Table 4: Percent of cells with depressions (3 ft 2)
The models sculpted with Tangible Landscape had more concen-
trated flow in stream channels with higher mean, maximum, and
summed water depths (see Table 1). Fig. 6 shows that water flow
in the 2nd exercise was spatially concentrated in the upper stream
channel, the lower stream channel, and the nascent swale to the
right.
Furthermore the water flow across models sculpted with Tangible
Landscape more closely fit the reference. Fig. 8 and the details in
1http://www.e-onsoftware.com/products/vue/
2https://www.blender.org/
Fig. 9 show that water flow in the 1st exercise was more spatially
diffuse and dispersed, whereas water flow in the 2nd exercise was
a better fit. Fig. 10 - 11 show how well the flow of the digitally
and tangibly sculpted models fit the reference flow. The digi-
tally sculpted models tended to loosely fit the upper stream with
fewer, more dispersed clusters of concentrated flow at further dis-
tances from the reference on average. They also tended to have
very few clusters at considerable distance from the swale on the
right and small, dispersed clusters that tightly fit the lower stream.
The tangibly sculpted models, however, tended to more densely,
tightly fit the upper stream and the swale on the right. The tangi-
bly sculpted models tended to miss or misplace the lower stream
channel (siting it too far southeast). The near-real time water
flow analytic was not very useful for modeling the lower stream
segment because there was not enough contributing area on the
model to generate much concentrated flow. Water flow over the
sculpted models was only computed over the model, whereas the
reference water flow was computed over the entire watershed.
Overall the tangibly sculpted models fit the reference flow 8.32%
better than the digitally sculpted models (see Table 2).
The digitally sculpted models had higher max and summed water
depth in depressions (see Table 1). Furthermore the depressions
covered 26.34% more area of the digitally sculpted models than
the tangibly sculpted models (see Table 4, Fig. 5, and Fig. 7). The
presence of large depressions on the central ridge in the 1st exer-
cise reveals concave topographic morphology where there should
be convex morphology. The pervasive presence of depressions in
the stream channels in both exercises reveals disrupted flows with
broken continuity where water should be flowing.
DISCUSSION
The modeling results show that participants tended not to clearly
understand how topography directs water flow, but began to learn
about the importance of curvature and continuity with the aid of
the tangible interface.
When digitally sculpting participants typically focused on mak-
ing the streams lower than the surrounding topography, but not
on draining water into the streams or directing a continuous flow
downstream. In the 1st exercise participants made low points ap-
proximately where the streams should be, but tended not to grade
smooth slopes with appropriate curvature. The participants use
of concave rather than convex morphology on the central ridge
reveals that some did not understand how topographic curvature
controls water flow. The extensive depressions distributed widely
across the terrain demonstrate that participants tended not under-
stand the importance of topographic curvature and continuity for
water flow.
With the tangible interface they typically focused on directing
water into streams, but did not tend focus on directing a contin-
uous flow downstream. In the 2nd exercise participants tended
to grade smoother slopes with fewer depressions whose curva-
ture directed water towards channels, concentrating it in streams.
They, however, did not tend to grade the channels to continu-
ously slope downstream. While participants performance im-
proved substantially in the 2nd exercise, the pervasive presence
of depressions in the stream channels in both exercises demon-
strates that participants did not fully understand the morphology
of streams by the end of the experiment.
We observed participants using an iterative modeling process with
the tangible interface – they a) sculpted, b) studied the updated
water flow simulation, c) critiqued the form of their model, d) and
continued to sculpt. The substantial improvement in participants’
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B2, 2016
XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XLI-B2-647-2016
650
performance in the 2nd exercise shows that they successfully inte-
grated the tangible interface’s water flow analytic into their mod-
eling process. To improve their performance they must have been
able to successfully manage the added cognitive load of the an-
alytical feedback. The tangibility of the interface may have let
them offload some of the cognitive work of sensing and shaping
3D form onto their bodies so that they could focus on water flow.
While their performance did improve in the 2nd exercise, it may
still have been adversely affected by emotions like frustration. If
the cognitive load of simultaneously modeling 3D form and wa-
ter flow became too great – if participants were unable to fluidly
connect cause (topographic form) and effect (flow) – they might
become frustrated and demotivated. Future research should in-
vestigate the role of affect, motivation, and metacognition in tan-
gible interaction.
More analytics may enhance participants’ understanding of wa-
ter flow and stream morphology. Identifying and computing the
depth of depressions highlighted the role of topographic curva-
ture and continuity in water flow. While topographic parameters
such as curvature and slope would highlight important aspects of
the morphology, an additional step of reasoning and imagination
is required to link these parameters to water flow. The ponding of
water in depressions directly links topographic controls and wa-
ter flow and thus should be a more intuitive analytic. We propose
combining shallow overland water flow with ponding in depres-
sions as an analytic to help users understand how water flows.
CONCLUSION
Tangible Landscape’s water flow analytic enabled an iterative cy-
cle of form-finding and critical assessment that helped partici-
pants learn how form controls process. Seeing the water flow
simulation update in near real-time enabled participants to gen-
erate hypotheses, test hypotheses, and draw inferences about the
way that water flows over topography. As one participant said,
‘seeing the flow takes away the mystery of topography.’ Phys-
ically manifesting topographic data enables users to cognitively
grasp the terrain. Coupling tangible topography with the simu-
lated flow of water lets users understand the simulation with their
body.
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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B2, 2016
XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XLI-B2-647-2016
651
(a) (b) (c)
Figure 5: (a) The depth of simulated water flow across the reference landscape, (b) the mean depth using digital modeling, (c) and the
mean depth using tangible modeling
(a) (b) (c)
Figure 6: (a) The depth of depressions in the reference landscape, (b) the maximum depth of depressions using digital modeling, (c)
and the maximum depth of depressions using tangible modeling
(a) (b) (c)
Figure 7: (a) The difference of the reference landscape, (b) the mean of digitally sculpted models, (c) and the mean of tangibly
sculpted models from the reference landscape
(a) (b)
Figure 8: Detail of the difference of (a) the mean of digitally sculpted models and (b) the mean of tangibly sculpted models from the
reference landscape
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B2, 2016
XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XLI-B2-647-2016
652
Figure 9: The lines represent the minimum distance between blue points with high concentrated flow from the reference and red
points with high concentrated flow from the mean depth of digitally sculpted models
Figure 10: The lines represent the minimum distance between blue points with high concentrated flow from the reference and red
points with high concentrated flow from the mean depth of tangibly sculpted models
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B2, 2016
XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XLI-B2-647-2016
653
... We report the experiment procedure, present the results, and finally discuss them in terms of knowledge building, user experience, and implications for education. This research is unique as although previous studies have shown how students' 3D spatial performance can be enhanced with tangibles [6,26,42], there remains a paucity of research investigating how to design, implement, and assess the effectiveness of tangible teaching methods-and usability of the associated tangible interface-for geospatial learning. ...
... Several Tangible User Interfaces (TUIs) which enable geospatial modeling already exist. Specifically, there are actuated pin tables (e.g., XenoVision Mark III Dynamic Sand Table, Northrop Grumman Terrain Table, Relief, Recompose, Tangible CityScape, inFORM), augmented architectural models (e.g., Urp, Collaborative Design Platform), augmented clay (e.g., Illuminating Clay, Tangible Geospatial Modeling System), and augmented sandboxes (e.g., Sandscape, Tangible Landscape, Inner Garden, etc.) (see [6] for an overview of existing TUIs). Actuated pin tables, such as Relief [14], can be categorized into three distinct categories: transformable tangible interfaces [9], dynamic shape displays [22], or shape changing interfaces [25]. ...
... One solution involves using tangibles in the classroom, as they have previously been shown to enhance spatial ability by affording embodied interaction and improving perception through visual and haptic feedback [42]. Although there are indications that incorporating TUIs in school curricula is useful, the evidence is sparse [6,26]. As such, there is a need to combine the use of TUIs to deliver tangible teaching methods developed from core curriculum requirements to help students improve their spatial skills and learn more naturally and effectively. ...
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This paper presents novel and effective methods for teaching about topography–or shape of terrain–and assessing 3-dimensional spatial learning using tangibles. We used Tangible Landscape–a tangible interface for geospatial modeling–to teach multiple hands-on tangible lessons on the concepts of grading (i.e., earthwork), geomorphology, and hydrology. We examined students’ ratings of the system’s usability and user experience and tested students’ acquisition and transfer of knowledge. Our results suggest the physicality of the objects enabled the participants to effectively interact with the system and each other, positively impacting ratings of usability and task-specific knowledge building. These findings can potentially advance the design and implementation of tangible teaching methods for the topics of geography, design, architecture, and engineering.
... TL has proven to be an effective tool for accurately modelling topography, to shape ideas, to perform quantitatively testing and to use an iterative design process to model topography using a digital reference . It increases the understanding of geomorphology, of damming on historical flood landscapes (RENGIFO 2018), the course of waterways (HARMON et al. 2016, WOODS et al. 2016, changing hydrological conditions (RAHMAN et al. 2017), and the understanding of the difference between real and abstract representations (JERMANN & DILLENBOURGH 2008). Using TL, waterways could be designed by providing near real-time analyses and feedback. ...
... Notwithstanding the proven qualities of using TL in an educational setting, such an approach has not been applied. Regarding such a TL some specific topics were not yet addressed, (HARMON et al. 2016, RENGIFO 2018, MILLAR et al. 2018, RAHMAN et al. 2017) which can be answered by the following research questions: 1) How to create a reusable and valid near-realistic elevation model which has not to be constructed after every design session within a design atelier? ...
Article
This study proposes the specification of a Tangible landscape (TL) to be used in the educational setting of a landscape design atelier. The main learning outcomes are to design alternative waterways in a riverine area taking into consideration the impact of waterflow on the landscape as a result of the designed waterways. TL is a projection-augmented sandbox powered by a Geographical Information System (GIS) for real-time geospatial analysis and simulation by coupling a mock-up with a digital model in near-real time by using 3D sensing, enabling users to apply geospatial simulation and visualisation. Notwithstanding the proven qualities of using TL in an educational setting, some obvious topics are addressed regarding the role of TL in educating waterway design. These topics are; how to realize a reusable and semi-realistic mock-up, in what way could waterways be designed without disrupting the mock-up, and how to give appropriate feedback during the design of waterways regarding the impact of waterflow change. The specified TL includes a thread-based approach that enables the student to design without disrupting the mock-up. Besides near real-time iterative results – both visually and numeric – it shows the hydrologic impact of the designed waterway. The expected outcomes of the current TL in education may stimulate topological thinking and insight into riverine geomorphology, it fosters representative and integrative thinking because of the near real-time relation between the design (form, scale, size) decisions and hydrologic process impact which makes the impact of design decisions on hydrological processes more illustrative and tangible.
... Studies by Harmon et al. found that users were able to sculpt more accurate topographic models with more distinct landforms using Tangible Landscape than they were using digital 3D modeling or analog modeling by hand. The study also found that users worked in a rapid, iterative process learning from real-time geospatial analytics ( Harmon et al. 2016;Harmon 2016;Harmon et al. 2018). Millar et al. studied the effectiveness of Tangible Landscape as a tool for teaching about grading (i.e. ...
Book
This book provides an overview of the latest developments in the fast growing field of tangible user interfaces. It presents a new type of modeling environment where the users interact with geospatial data and simulations using 3D physical landscape model coupled with 3D rendering engine. Multiple users can modify the physical model, while it is being scanned, providing input for geospatial analysis and simulations. The results are then visualized by projecting images or animations back on the physical model while photorealistic renderings of human views are displayed on a computer screen or in a virtual reality headset. New techniques and software which couple the hardware set-up with open source GRASS GIS and Blender rendering engine, make the system instantly applicable to a wide range of applications in geoscience education, landscape design, computer games, stakeholder engagement, and many others. This second edition introduces a new more powerful version of the tangible modeling environment with multiple types of interaction, including polymeric sand molding, placement of markers, and delineation of areas using colored felt patches. Chapters on coupling tangible interaction with 3D rendering engine and immersive virtual environment, and a case study integrating the tools presented throughout this book, demonstrate the second generation of the system - Immersive Tangible Landscape - that enhances the modeling and design process through interactive rendering of modeled landscape. This book explains main components of Immersive Tangible Landscape System, and provides the basic workflows for running the applications. The fundamentals of the system are followed by series of example applications in geomorphometry, hydrology, coastal and fluvial flooding, fire spread, landscape and park design, solar energy, trail planning, and others. Graduate and undergraduate students and educators in geospatial science, earth science, landscape architecture, computer graphics and games, natural resources and many others disciplines, will find this book useful as a reference or secondary textbook. Researchers who want to build and further develop the system will most likely be the core audience, but also anybody interested in geospatial modeling applications (hazard risk management, hydrology, solar energy, coastal and fluvial flooding, fire spread, landscape and park design) will want to purchase this book.
... We implemented a real-time water flow simulation for Tangible Landscape so that users can sculpt topography and immediately see how that changes the simulated flow and dispersion of water across the landscape. After a pilot study 37 we conducted an experiment to study how effectively landscape architects can use this real-time, tangible water flow simulation. This experiment was designed to study whether participants could link form and process using the water flow simulation -to assess how well they could understand the relationship between topographic form and the flow of water when using Tangible Landscape. ...
Article
Full-text available
We present Tangible Landscape-a technology for rapidly and intuitively designing landscapes informed by geospatial modeling, analysis, and simulation. Tangible Landscape is a tangible interface powered by a geographic information system that gives 3D spatial data an interactive, physical form so that users can naturally sense and shape it. It couples a physical and a digital model of a landscape through a real-time cycle of physical manipulation, 3D scanning, spatial computation, and projected feedback. Natural 3D sketching and real-time analytical feedback should aid landscape architects in the design of high performance landscapes that account for physical and ecological processes. We conducted a series of studies to assess the effectiveness of tangible modeling for landscape architects. Landscape architecture students, academics, and professionals were given a series of fundamental landscape design tasks-topographic modeling, cut-and-fill analysis, and water flow modeling. Their performance was assessed using qualitative and quantitative methods including interviews, raster statistics, morphometric analyses, and geospatial simulation. With tangible modeling participants built more accurate models that better represented morphological features than they did with either digital or analog modeling. When tangibly modeling they worked in a rapid, iterative process informed by real-time geospatial analytics and simulations. With the aid of real-time simulations they were able to quickly understand and then manipulate how complex topography controls the flow of water.
Book
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This book presents a new type of modeling environment where users interact with geospatial simulations using 3D physical models of studied landscapes. Multiple users can alter the physical model by hand during scanning, thereby providing input for simulation of geophysical processes in this setting. The authors have developed innovative techniques and software that couple this hardware with open source GRASS GIS, making the system instantly applicable to a wide range of modeling and design problems. Since no other literature on this topic is available, this Book fills a gap for this new technology that continues to grow. Tangible Modeling with Open Source GIS will appeal to advanced-level students studying geospatial science, computer science and earth science such as landscape architecture and natural resources. It will also benefit researchers and professionals working in geospatial modeling applications, computer graphics, hazard risk management, hydrology, solar energy, coastal and fluvial flooding, fire spread, landscape, park design and computer games.
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We present a new, affordable version of TanGeoMS, a tangible geospatial modeling and visualization system designed for collaboratively exploring how terrain change impacts landscape processes. It couples a physical, three-dimensional model of a landscape with geospatial modeling and analysis through a cycle of scanning and pro- jection. Multiple users can modify the physical model by hand while it is being scanned; by sculpting the model they generate input for modeling of geophysical processes. The mod- eling results are then visualized by projecting images or animations back on the physical model. This feedback loop is an intuitive way to evaluate the impacts of different sce- narios including anthropogenic and natural landscape change. Integration with GRASS GIS, a free and open source geographic information system, provides TanGeoMS with a variety of easily accessible geospatial analysis and modeling tools. To demonstrate the environmental modeling applications of TanGeoMS, we will demonstrate how develop- ment can be planned based on feedback from landscape processes such as hydrologic simulation and wildfire modeling with variable fuel distribution.
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The theory of embodied cognition can provide HCI practitioners and theorists with new ideas about interac-tion and new principles for better designs. I support this claim with four ideas about cognition: (1) interacting with tools changes the way we think and perceive – tools, when manipulated, are soon absorbed into the body schema, and this absorption leads to fundamental changes in the way we perceive and conceive of our environments; (2) we think with our bodies not just with our brains; (3) we know more by doing than by seeing – there are times when physically performing an activity is better than watching someone else perform the activity, even though our motor resonance system fires strongly during other person observa-tion; (4) there are times when we literally think with things. These four ideas have major implications for interaction design, especially the design of tangible, physical, context aware, and telepresence systems.
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We introduce a system for urban planning - called Urp -thatintegrates functions addressing a broad range of the fieldsconcerns into a single, physically based workbench setting. The I/OBulb infrastructure on which the application is based allowsphysical architectural models placed on an ordinary table surfaceto cast shadows accurate for arbitrary times of day; to throwreflections off glass facade surfaces; to affect a real-time andvisually coincident simulation of pedestrian-level windflow; and soon.We then use comparisons among Urp and severalearlier I/O Bulb applications as the basis for anunderstanding of luminous-tangible interactions, which resultwhenever an interface distributes meaning and functionality betweenphysical objects and visual information projectively coupled tothose objects. Finally, we briefly discuss two issues common to allsuch systems, offering them as informal thought-tools for thedesign and analysis of luminous-tangible interfaces.
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We present TanGeoMS, a tangible geospatial modeling visualization system that couples a laser scanner, projector, and a flexible physical three-dimensional model with a standard geospatial information system (GIS) to create a tangible user interface for terrain data. TanGeoMS projects an image of real-world data onto a physical terrain model. Users can alter the topography of the model by modifying the clay surface or placing additional objects on the surface. The modified model is captured by an overhead laser scanner then imported into a GIS for analysis and simulation of real-world processes. The results are projected back onto the surface of the model providing feedback on the impact of the modifications on terrain parameters and simulated processes. Interaction with a physical model is highly intuitive, allowing users to base initial design decisions on geospatial data, test the impact of these decisions in GIS simulations, and use the feedback to improve their design. We demonstrate the system on three applications: investigating runoff management within a watershed, assessing the impact of storm surge on barrier islands, and exploring landscape rehabilitation in military training areas.
Conference Paper
This paper describes a novel system for the real-time computational analysis of landscape models. Users of the system - called Illuminating Clay - alter the topography of a clay landscape model while the changing geometry is captured in real-time by a ceiling-mounted laser scanner. A depth image of the model serves as an input to a library of landscape analysis functions. The results of this analysis are projected back into the workspace and registered with the surfaces of the model.We describe a scenario for which this kind of tool has been developed and we review past work that has taken a similar approach. We describe our system architecture and highlight specific technical issues in its implementation.We conclude with a discussion of the benefits of the system in combining the tangible immediacy of physical models with the dynamic capabilities of computational simulations.
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A path sampling method is proposed for solving the continuity equations describing mass flows over complex landscape surfaces. The modeled quantities are represented by an ensemble of sampling points which are evolved according to the corresponding Green function. The method enables incorporation of multi-scale/multi-process treatments. It has been used to develop simulation tools for overland shallow water flow and for sediment transport. The spatial pattern of sediment flow and net erosion/deposition is modeled using the closure relationship between sediment transport capacity and detachment developed for the USDA Water Erosion Prediction Project. The tools were recently implemented as modules in Open Source GRASS GIS. Their application is illustrated by the study of impact of land use and topography change on overland flow and sediment transport at North Carolina State University campus.
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