Visualizacao de dados geoespacial por professor toni moore

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Antoni Moore, Marc Russwurm, Mike Bricker School of Surveying, University of Otago, NZ Technical University of Munich, Germany Porirua City Council, NZ Your work as a landscape: Adventures in Virtual Geographic Environments

Transcript of Visualizacao de dados geoespacial por professor toni moore

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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

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University of Otago

• New Zealand’s oldest university– Founded in 1869

• Over 20,000 students enrolled

• 4 faculties: Commerce, Science, Humanities and Health Sciences

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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.

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A bit about Otago itself

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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

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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)

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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

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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

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MW-Voronoi

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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

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Time is spatialised through area; difficulty through height

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Virtual Environment (OpenSim)

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Path object also spatialises time and marks progress

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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

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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

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• 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

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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

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• duration spatialized through area

subdivision project - task

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• duration spatialized through area

subdivision project - task

Project 1

Task 1 Task 2

Task 3

Project n

Task 1

Task 2

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seed placement

• initially shapefile -> csv table• project - seeds randomly (but spatially

balanced) generated• task-seeds generated based on

importance value

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task’s importance i

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task’s importance i

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projects + taskssurface projects

surface tasks

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Project characteristics to spatial characteristics

SIZ

E =

DU

RAT

ION

STEEP = DIFFICULT GENTLE = EASY

ROUGH = CERTAIN

SMOOTH =UNCERTAIN

BIG =LONG

SMALL = SHORT

SLOPE = DIFFICULTY

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roughness

• rough terrain is more realistic• add information with roughness

– certainty/uncertainty (level of detail/vagueness)

– stress, pressure (exams vs private study)– urgency (temporal)

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roughness

nbickford.wordpress.com

Fractal Brownian Roughness: 2Parameter: h and

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Time is spatialized through area; difficulty through height;

certainty through roughness

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projects / tasks

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islands / hills

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Surveying Yr.2 spatialisation map

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Meanwhile, in OpenSim…

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Surveying Yr.2 spatialisation VE

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Surveying Yr. 3 spatialisation

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Surveying Yr. 4 spatialisation

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and…

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Semantic proximity

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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

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Cartography and Art: Kea Art-Map

With Diana Marinescu

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Scribbles of dolphin tracks

N

S

EW

Dolphin232

Dolphin67

Season 7 Season 8

With Judy Rodda

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Agent-based image classification

With Kambiz Borna & Pascal Sirguey

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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.