Antoni Moore, Marc Russwurm, Mike BrickerSchool of Surveying, University of Otago, NZ
Technical University of Munich, GermanyPorirua City Council, NZ
Your work as a landscape: Adventures in Virtual
Geographic Environments
University of Otago
• New Zealand’s oldest university– Founded in 1869
• Over 20,000 students enrolled
• 4 faculties: Commerce, Science, Humanities and Health Sciences
Welcome to the School of Surveying
Surveying13 staff in teaching & ...... research areas: GIS,
remote sensing, geodesy / GPS, land / urban development, hydrographic, land tenure and cadastre.
A bit about Otago itself
Introduction• Time is precious and we don’t have enough of it• In the workplace good time management is
important– Often dealing with many project activities– Linked to enhanced productivity, efficiency,
effectiveness, dealing with pressure, image…• Tools exist to manage time (Gantt charts,
checklists, diaries, calendars, planners…)• But what about a spatial tool?
– Transferring projects and time-taken to abstract space– Uses a visual geographic metaphor
Time-is-space metaphors• Uses some familiar phenomenon
to explain something less understandable / tangible– e.g. Time-is-space: “Christmas
is close to New Year”• Visual metaphors make
representations more effective– e.g. fog metaphor - uncertainty– Time geography (time-is-space)
Spatialisation• “…a data transformation…based on spatial
metaphors… generating cognitively adequate graphic representations.” Fabrikant and Skupin
• e.g. Exploring Geovisualisation book “map”– Similar chapters close in space; hills convey frequency – Draws on naïve familiarity with maps and landscapes
Spatialisation of time• A mapped representation of project space
– Each project represented by a point– The point has duration, difficulty and uncertainty
attributes– Point locations are evenly distributed and are used
to generate multiplicatively-weighted Voronoi project areas; duration is the weight
• Calculate weighted distance = dist / weight from each project point
• This will create a weighted distance raster for each project• Calculate the minimum distance across all distance
rasters
MW-Voronoi
Spatialisation of time (2)
• The project difficulty attribute is expressed as height – Each project area contains a hill and the
difficulty height is the summit– Euclidean distance from Voronoi boundary to
centre of polygon calculated – Normalised to a range of -5 to +5– Sigmoidal ‘hill’ surface generated:
= (1 / (1 + e –norm_dist)) * height
Time is spatialised through area; difficulty through height
Virtual Environment (OpenSim)
Path object also spatialises time and marks progress
Spatialisation of time: Further concepts
• Use of cartographic variables• Usability testing• Representing other project aspects in the
‘landscape’– Subdivided areas
(and paths) for subtasks
– Smoothness = uncertainty…– Adding semantic meaning
to location
Spatialisation of time: cartography• Representing other project aspects in the
‘landscape’ using Bertin’s variables– Colour (hue)
= project ID– Variation in greyscale
or saturation to represent subtasks
• Spatialised projects experienced through a VE gives the user a cognitively strong impression of entire workload
• Usability testing needed to establish this– Ideation and prototyping of interface– Heuristic evaluation
completed…– But more needed
• Spatialisation vs. virtualdelivery
Usability Testing
Spatialisation of time: Further concepts
• Use of cartographic variables• Usability testing• Representing other project aspects in the
‘landscape’– Subdivided areas
(and paths) for subtasks
– Smoothness = uncertainty…– Adding semantic meaning
to location
• duration spatialized through area
subdivision project - task
• duration spatialized through area
subdivision project - task
Project 1
Task 1 Task 2
Task 3
Project n
Task 1
Task 2
seed placement
• initially shapefile -> csv table• project - seeds randomly (but spatially
balanced) generated• task-seeds generated based on
importance value
task’s importance i
task’s importance i
projects + taskssurface projects
surface tasks
Project characteristics to spatial characteristics
SIZ
E =
DU
RAT
ION
STEEP = DIFFICULT GENTLE = EASY
ROUGH = CERTAIN
SMOOTH =UNCERTAIN
BIG =LONG
SMALL = SHORT
SLOPE = DIFFICULTY
roughness
• rough terrain is more realistic• add information with roughness
– certainty/uncertainty (level of detail/vagueness)
– stress, pressure (exams vs private study)– urgency (temporal)
roughness
nbickford.wordpress.com
Fractal Brownian Roughness: 2Parameter: h and
Time is spatialized through area; difficulty through height;
certainty through roughness
projects / tasks
islands / hills
Surveying Yr.2 spatialisation map
Meanwhile, in OpenSim…
Surveying Yr.2 spatialisation VE
Surveying Yr. 3 spatialisation
Surveying Yr. 4 spatialisation
and…
Semantic proximity
Conclusions• A simple VE prototype for project time management
– Add modelling of semantic similarity and cartographic representation
– Knowledge elicitation for concepts like “difficulty”• Use of spatialisation in a virtual environment gives
a cognitively-rich representation of the intangible– But this needs to be tested– Synoptic view but loss of a unified time structure
• Online VE affords collaboration within and between projects (facilitated by semantic proximity)
• Specifics on workplace tasks and time management to be explored further
Cartography and Art: Kea Art-Map
With Diana Marinescu
Scribbles of dolphin tracks
N
S
EW
Dolphin232
Dolphin67
Season 7 Season 8
With Judy Rodda
Agent-based image classification
With Kambiz Borna & Pascal Sirguey
Thanks!
• Christina Hulbe, Greg Leonard and Emily Tidey for providing data
• Peter George from InfoSci for OpenSimsupport
Moore, A B and Bricker, M. 2015. “Mountains of work”: Spatialization of work projects in a virtual geographic environment. Annals of GIS, DOI:10.1080/19475683.2015.1057227Russwurm, M and Moore, A. 2015. “Visualising the project landscape” – A spatialisation describing workload attributes as terrain. Environmental Earth Sciences, submitted.