managing the temporal geography of futures michael flaxman, mit

24
Managing the Temporal Geography of Futures Michael Flaxman, MIT

Upload: sharyl-stevens

Post on 13-Jan-2016

229 views

Category:

Documents


0 download

TRANSCRIPT

Page 1: Managing the Temporal Geography of Futures Michael Flaxman, MIT

Managing the Temporal Geography of Futures

Michael Flaxman, MIT

Page 2: Managing the Temporal Geography of Futures Michael Flaxman, MIT

Motivation

Page 3: Managing the Temporal Geography of Futures Michael Flaxman, MIT

Overview

Alternative Futures Methodology

Current Scenario & Impact Model Data Management

Four Problems

Two (Partial) Solutions

Page 4: Managing the Temporal Geography of Futures Michael Flaxman, MIT

Problem 1: Too Many Futures!

Page 5: Managing the Temporal Geography of Futures Michael Flaxman, MIT
Page 6: Managing the Temporal Geography of Futures Michael Flaxman, MIT

Problem 2:Logical Dependencies Are Important

Correct Interpretation often depends on understanding underlying assumptions

Large Update Problems

– Scenarios dependencies propagate

– If dependencies are not tracked, danger of false attribution

Page 7: Managing the Temporal Geography of Futures Michael Flaxman, MIT

Problem 2:Logical Dependencies Example

– Scenario 1 (S1) Impact of S1 on Hydrology Impact of Hydrology under S1 on Species Habitat

– Scenario 2 (S2) Impact of S2 on Hydrology Impact of Hydrology under S2 on Species Habitat

If Scenarios Change…

– Dependencies propagate i.e.above must recompute hydrology twice & habitat twice

– If dependencies are not tracked, danger of false attribution i.e. Species Habitat map not correctly updated to S1v17

Page 8: Managing the Temporal Geography of Futures Michael Flaxman, MIT

Problem 3:Sharing

Creating a single isolated system to manage spatiotemporal data is hard

Creating a networked system is much harder still!– Must track dependencies *between* systems– Must deal with broken connections, latencies, and

time changes

Page 9: Managing the Temporal Geography of Futures Michael Flaxman, MIT

Problem 3:Sharing Example

Parties Involved– Hydrologist at USGS (Ft. Lauderdale) – Land Use Modeler at UFL (Gainsville)– Habitat Specialists at FWS (Vero Beach & 2 Refuges)– Vegetation Specialist at Everglades National Park

Action– Land Use Modeler receives updated demographic estimate, updates land

cover model

One scenario change requires sequential notification to 5 distributed parties + manager

Page 10: Managing the Temporal Geography of Futures Michael Flaxman, MIT

Problem 4:Metaphors & Mechanisms Are Weak

Available object “metaphors” and mechanisms– Files (on disk)

Bundled by space, discrete for each time ‘slice’ NetCDF – multidimensional file format

– Supported by climate models, some GIS– Not well supported outside of science

– Layers (in GIS) User re-orderable with occlusion (for better and worse) Independent visibility toggle

– Hierarchical Folders (both) Allows development of hierarchical file or layer representations of time

Other Metaphors & Concepts– Time Line”

Understandable interface, but not sharable implementation– Dependency Diagram

Again, well understood, but each implementation separate

Page 11: Managing the Temporal Geography of Futures Michael Flaxman, MIT

MIT Prototypes

ScenarioCMS– A content management system for spatial scenarios organized as

“time slices”– Provides “ScenarioXML” language to document assumptions and

dependencies– Status: working prototype (Telluride), Phase 2 (BajaEcoInfo)

EverView2– Extension of ScenarioCMS for the Everglades– Visualizes & manages assumptions, choices and dependencies– Organizes “stories” within Scenarios

Stories are complex sub-scenarios with temporal sequencing – Status: early schematic

Page 12: Managing the Temporal Geography of Futures Michael Flaxman, MIT
Page 13: Managing the Temporal Geography of Futures Michael Flaxman, MIT

ScenarioCMS: Scenarios & Constraints

Page 14: Managing the Temporal Geography of Futures Michael Flaxman, MIT

ScenarioXML

Vendor-neutral, software-neutral Organizes scenarios logically

– Like HTML, separates presentation from data– Metadata for scenario (machine & human

readable)

Page 15: Managing the Temporal Geography of Futures Michael Flaxman, MIT

ScenarioCMS: Simple Time Slider

Page 16: Managing the Temporal Geography of Futures Michael Flaxman, MIT

ScenarioCMS: Dynamic Legend

Page 17: Managing the Temporal Geography of Futures Michael Flaxman, MIT

Florida EvergladesDis-integrated Management Systems

Water Manager’s View: Pipes Only

Refuge Manager’s View: Habitat & Species Observation Only

Page 18: Managing the Temporal Geography of Futures Michael Flaxman, MIT
Page 19: Managing the Temporal Geography of Futures Michael Flaxman, MIT

Scenario 1Conditions:Wet SeasonHurricane IV approachingLoxahatchee NWR and Miami-Dade at High Flood-Risk

Management Options:

A.

Miami-Dade Impact:Flood-risk reduced

Loxahatchee NWR/Everglades Impact:Flood-risk reduced

Caloosahatchee Estuary and St. Lucie Estuary Impacts:Water quality decreasesLow O2 levelsFish KillInundate Sea Grass

Urban Flood-Risk LevelsL M H

Conservation Areas Flood-Risk Levels L M H

Lake Okechobee Water Level 14.5’ 16.5’ 17.5’

release

Lock

timeline

Flood-risk high

Release to C-44 and C-43

Water reaches first locks, Port Mayaca, Moore Haven

Water reaches St. Lucie Lock

Water reaches Franklin Lock

Urban flood-risk reduced; Estuarine Impacts

A.

Release water toC-43 and C-44 St. Lucie Canal; Caloosahatchee Canal

B.

Release water toL-8 and L-10 STA 1-W and 1-E; WCA 1 (Lox. NWR)

Decision Impacts:

Preview Preview

Page 20: Managing the Temporal Geography of Futures Michael Flaxman, MIT

Scenario 1Conditions:Wet SeasonHurricane IV approachingLoxahatchee NWR and Miami-Dade at High Flood-Risk

Management Options:

Urban Flood-Risk LevelsL M H

Conservation Areas Flood-Risk Levels L M H

Lake Okechobee Water Level 14.5’ 16.5’ 17.5’

release

Lock

Release to L-8, L-10

A.

Release water toC-43 and C-44 St. Lucie Canal; Caloosahatchee Canal

B.

Release water toL-8 and L-10 STA 1-W and 1-E; WCA 1 (Lox. NWR)

Decision Impacts:

Preview Preview

B.

Miami-Dade Impact:Flood-risk reduced

Loxahatchee NWR/Everglades Impact:Apple snail population andwaterfowl nesting inundated

High Flood-Risk

Water reaches STA 1-W and 1-E

Water released to WCA 1, (Lox)

Water reaches WCA 2, WCA 3

Water reaches Everglades

Apple Snail pop. disturbed

Page 21: Managing the Temporal Geography of Futures Michael Flaxman, MIT

Scenario 2Conditions:Drought SeasonSFWMD Phase IV Drought “Critical”Loxahatchee NWR and Everglades National Park need water

SFWMD Drought Protocol: Water Restrictions Stages I II III IV

Flow RateNone Minimum Adequate

Severe Drought

timeline

Release water to L-8, L-10

Water reaches STA 1-W and 1-E

Water released to WCA 1, (Lox)

Water reaches WCA 2, WCA 3

Water reaches Everglades

Management Options:

A.

Miami-Dade Impact:Water restrictions remain Phase IV

Loxahatchee NWR/Everglades Impact:Minimum flows received, still dry

A.

Release min. flows to L-8, L-10, STA 1-W and 1-E, WCA 1 Loxahatchee NWR; Everglades

B.

Release water toL-15 and L-18 Miami-Dade and West Palm Beach

Decision Impacts:

Preview Preview

Page 22: Managing the Temporal Geography of Futures Michael Flaxman, MIT

Scenario 2Conditions:Drought SeasonSFWMD Phase IV Drought “Critical”Loxahatchee NWR and Everglades National Park need water

SFWMD Drought Protocol: Water Restrictions Stages I II III IV

Flow RateNone Minimum Adequate

Drought severe

Release water to L-15, L-18

Water reaches Miami-Dade County line

Water restrictions reduced Phase III

Management Options:

A.

Release min. flows to L-8, L-10, STA 1-W and 1-E, WCA 1 Loxahatchee NWR; Everglades

B.

Release water toL-15 and L-18 Miami-Dade and West Palm Beach

Decision Impacts:

Preview Preview

B.

Miami-Dade Impact:Water restrictions reduced to Phase III

Loxahatchee NWR/Everglades Impact:Apple snail population failsMandatory minimum flows not met

Min. flows to Everglades not met

Page 23: Managing the Temporal Geography of Futures Michael Flaxman, MIT

Conclusions

Spatiotemporal Scenario Management Needed– Typical scenario study generates 100+ layers– Logical dependencies important to preserve

Sharing is Nice– Single-application solutions inadequate– Many raw data ‘standards’ to pick from– Higher-level aggregations desirable

Page 24: Managing the Temporal Geography of Futures Michael Flaxman, MIT

Future Work

Telluride Prototype– Go live this summer– Kept simple

Time slices only Interface exposes dependencies as hierarchies Back-end ScenarioXML drives interface

Everview2– To be developed next academic year