uniting traditional gis and mainstream it
DESCRIPTION
Presentation given by Jonathon W. Lowe, GIS Specialist, IBM Consulting Services.TRANSCRIPT
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Tonight’s Agenda, in no particular order:
What’s the problem? Enterprises such as DEFRA want to manage their spatiotemporal data using consolidation, quality and access strategies.
Who cares? Introducing a typical cast of stakeholders (and their diverse cultures) in a shared spatiotemporal data management programme…
Laws of the jungle. Pure “greenfields” are ever more rare – what technical constraints do implementors encounter in large projects?
Meet the Beast. What is IBM’s architectural approach when answering spatiotemporal data management and systems integration challenges?
Where next? With a baseline for data exchange in place, what happens next for an integrated spatiotemporal enterprise, and for our industry?
Questions? Heckle during the presentation, or save up for the end.
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What’s the spatial problem?
For GI practitioners, the DEFRA challenge is to make spatiotemporal data management more efficient for both data providers and data recipients. This increased
efficiency will in turn lead to higher quality data and improved policy making.
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Drawn from RASTER
DATA
Drawn from VECTOR
DATA
Drawn from NUMERIC,
TEXTUAL, or TEMPORAL
ATTRIBUTES
ORTHOPHOTO
CONTEXTUALBASE MAP
THEMATIC or CHOROPLETH
MAP
Spatiotemporal Data
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DEFRA’s 400+ thematic business datasets
Ordnance Survey’sMasterMap & Rasters
UK Perspectives’ Aerial Photography
Spatiotemporal Data at DEFRA
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Spatiotemporal Data Capture and Maintenance
Spatiotemporal Application Development
?Spatiotemporal
Data Management&
Systems Integration
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Spatiotemporal Data Capture and Maintenance
Spatiotemporal Application Development
Spatial Database(PostgreSQL, Oracle, Informix, DB2, MySQLMicrosoft SQL Server)
Traditional GIS
Traditional GIS
Mainstream IT applied to a
traditional GIS?
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Data Provide
r
Data Provide
r
Data Provider
Data Provide
r
Data User
Data User
Data User
Data User
Repository
Data Provide
r
Data Provide
r
Data Provide
r
Data Provider
Data User
Data User
Data User
Data User
Data Provider Pain Remedy Benefit
Repeatedly distributing spatial data to multiple internal and external users in a point-to-point fashion takes time.
Deliver the data once to a central Repository from which multiple internal and external users download data on demand.
Time formerly spent managing our data’s distribution now goes toward improving and maintain our data instead.
Spatiotemporal Data Consolidation
Data User Pain Remedy Benefit
I have to find and manage the data I need for my application, and must ensure that I keep up to date data.
The Repository supplies all the required current data from one access point.
By pulling data from the Repository, I save time formerly spent managing that data myself.
However, central repositories reduce direct contact between people, the same people who used to tell you whether their data was fit for your purposes…or not. How can data users evaluate a
warehouse’s data quality without talking to the people creating and providing the data?
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Spatiotemporal Data Quality Validation
Unclosed polygons
Closed polygons
Duplicate vertices
Clockwise exterior and interior rings
Proper exterior and interior ring
rotation
No duplicate vertices
Examples of potentially problematic geometries The same geometries with fixes applied
But… the consolidated datasets in a central repository and the associated quality reports are useless unless they can be searched by both data providers and potential data
recipients.
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Spatiotemporal Data Storage and Inventory
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Pain Remedy Benefit
It’s time consuming to distribute our spatial data in the many different formats that our users’ heterogenious systems require.
The Repository accepts the data in one standard submission format but distributes it to users in a variety of formats.
Instead of managing our data’s distribution formats, we now have more time to improve and maintain our data.
Pain Remedy Benefit
The data we need is not available in the required format, so we spend time reformatting the data ourselves.
The Repository provides data in the formats most often requested by data recipients.
Instead of reformatting data to suit our system’s needs, we can begin analysing that data immediately upon receiving it.
Spatiotemporal Multi-format Data Access
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Primary Customers:
• Departments capturing spatial data for/from public constituents
• Domain specialists analysing spatial data to answer policy questions
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Spatiotemporal Data Capture and Maintenance
Spatiotemporal Application Development
?Spatiotemporal
Data Management&
Systems Integration
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Spatiotemporal Data Capture and Maintenance
Spatiotemporal Application Development
Do these 4 services comprise Spatio-
temporal Data
Manage-ment?
Spatiotemporal Data Consolidation
Spatiotemporal Data Quality Validation
Spatiotemporal Data Storage and Inventory
Spatiotemporal Multi-format Data Access
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Spatiotemporal Infrastructure
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Spatiotemporal Security
Data Provide
r
Data Provide
r
Data Provide
r
Data Provide
r
Data User
Data User
Data User
Data User
Repository
Data Provide
r
Data Provide
r
Data Provide
rData
Provider
Data User
Data User
Data User
Data User
Point-to-point security model Centralised, administered security model
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Offering #9: Usage Metrics and Metering
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Spatiotemporal Data Capture and Maintenance
Spatiotemporal Application Development
Spatiotemporal Data Management
Systems Integration
Spatiotemporal Data Consolidation
Spatiotemporal Data Quality Validation
Spatiotemporal Data Storage and Inventory
Spatiotemporal Multi-format Data Access
Spatiotemporal Infrastructure
Spatiotemporal Security
Spatiotemporal Usage Metrics and Metering
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Technical Constraints
Pure “greenfields” are ever more rare – what technical constraints do implementors encounter in large projects?
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IBM’s technical constraints
IBM’s p-series hardware (64-cpu server)
IBM’s AIX 5.3 Operating System
IBM’s Websphere Application Server (with 64-bit Java Virtual Machine)
IBM’s Websphere Portal Server
Java Development Environment (preferred)
Websphere Application Server
Websphere Portal Server
DEFRA’s technical constraintsGI Clients: CadCorp and ESRI and MapInfo
OracleSpatial
Oracle 9i or 10g Spatial Database
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Evaluating DEFRA’s constraints
Version compatibilities of ESRI, Oracle Spatial and OGC Web Services
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IBM’s technical constraints
IBM’s p-series hardware (64-cpu server)
IBM’s AIX 5.3 Operating System
IBM’s Websphere Application Server (with 64-bit Java Virtual Machine)
IBM’s Websphere Portal Server
Java Development Environment (preferred)
Websphere Application Server
Websphere Portal Server
DEFRA’s technical constraints
GI Clients: CadCorp and ESRI and MapInfo
OracleSpatial
Oracle 9i or 10g Spatial Database
Geospatial Rendering Engine ??
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The tension between traditional GIS, mainstream IT and free open-source geospatial software products
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“How is it that groups of computer programmers (sometimes very large groups) made up of individuals separated by geography, corporate boundaries, culture, language, and other characteristics, and connected mainly via telecommunications bandwidth, manage to work together over time and build complex, sophisticated software systems outside the bounaries of a corporate structure and for no direct monetary compensation? And why does the answer to that question matter to anyone who is not a computer programmer?”
“This book explains how the open source software process works. It is broadly a book about technology and society, in the sense that changes in technology uncover hidden assumptions of inevitability in production systems and the social arrangements that accompany them. It is also about computers and software, because the success of open source rests ultimately on computer code, code that people often find more functional, reliable, and faster to evolve than most proprietary software built inside a conventional corporate organization. It is a business and legal story as well. Open source code does not obliterate profit, capitalism, or intellectual property rights. Companies and individuals are creating intellectual products and making money from open source software code, while inventing new business models and notions about property along the way.”
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FOSS4G2006 (Free Open Source Software for Geospatial, 2006 Conference
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What’s the next (bigger) problem?
For Information Technology practitioners in general, the DEFRA problem is systems integration in support of a more “joined-up” government organisation.
Evidence of success is the British public’s level of satisfaction with government services – for DEFRA, this public is often farmers or anyone involved with food,
livestock, disease or the rural enviroment.
What’s the spatial problem?
For GI practitioners, the DEFRA challenge is to make spatiotemporal data management more efficient for both data providers and data recipients. This increased
efficiency will in turn lead to higher quality data and improved policy making.
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Secondary once-removed Customers:
• Government Administrators & Policy-makers
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Spatiotemporal Processing Services
• Your postcode intersects a Nitrate Vulnerable Zone.
• Please confirm the following acreages for each field on your farm.
• You are eligible for countryside stewardship scheme funding due to your land’s proximity to a Site of Special Scientific Interest.
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Spatiotemporal Data Quality Validation
Unclosed polygons
Closed polygons
Duplicate vertices
Clockwise exterior and interior rings
Proper exterior and interior ring
rotation
No duplicate vertices
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Note: Basic Validation does not test Spatiotemporal Contextual Data Quality. Although their edges should match, polygon data representing moorland (purple diagonal cross hatching) may extend beyond government-established boundaries for ecosystem protection (solid pink) due to data capture at different scales.
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No Payment (outside of scheme)
Payment = £16.10/haDisadvantaged Ecosystem
Payment = £29.78/ha Severely Disadvantaged
Ecosystem
Payment = £11.26/haMoorland
Invalid data!RULE: Moorland Line must exist within LFA
Moorland
SeverelyDisadvantagedEcosystem Disadvantaged
Ecosystem
Land Parcel
This farmer’s land parcel may be subject to FOUR different payment tiers, as well as an erroneous payment due to contextually invalid geometric data.
Note: Basic Validation does not test Spatiotemporal Contextual Data Quality. Although their edges should match, polygon data representing moorland (purple diagonal cross hatching) may extend beyond government-established boundaries for ecosystem protection (solid pink) due to data capture at different scales.
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Spatiotemporal Data Capture and Maintenance
Spatiotemporal Application Development
Spatial Database(PostgreSQL, Oracle, Informix, DB2, MySQLMicrosoft SQL Server)
Traditional GIS
Traditional GIS
Mainstream IT applied to a
traditional GIS?
![Page 34: Uniting traditional GIS and mainstream IT](https://reader033.vdocument.in/reader033/viewer/2022061218/54b70a534a79596c528b45c9/html5/thumbnails/34.jpg)
Spatiotemporal Data Capture and Maintenance
Spatiotemporal Application Development
Spatiotemporal Data Management
Systems Integration
Spatiotemporal Data Consolidation
Spatiotemporal Data Quality Validation
Spatiotemporal Data Storage and Inventory
Spatiotemporal Multi-format Data Access
Spatiotemporal Infrastructure
Spatiotemporal Security
Spatiotemporal Usage Metrics and Metering
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Spatiotemporal Data Capture and Maintenance
Spatiotemporal Application Development
Spatiotemporal Data Management
Systems Integration
Spatiotemporal Data Consolidation
Spatiotemporal Data Quality Validation
Spatiotemporal Data Storage and Inventory
Spatiotemporal Multi-format Data Access
Spatiotemporal Data Processing
Spatiotemporal Infrastructure
Spatiotemporal Security
Spatiotemporal Usage Metrics and Metering
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Step 1: An inspector discovers a diseased animal and reports its position. The State Veterinary Svc. creates an initial buffer zone (circle) around the point of disease discovery.
Step 2: SVS then expands the buffer zone (manually) to include intersected and epidemiologically relevant contiguous land known to host livestock management activities.
Step 3: SVS then identifies the livestock keepers contained within the zone and enlists their cooperation in controlling animal movement within it, enforced through a permitting process. In past outbreaks, this series of steps has taken several hours of desktop processing each night, accompanied by a manual results distribution process.
Disease Mapping Scenario for State Veterinary Service (SVS)
Farmer Jones
Farmer Blair
Farmer Smith
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Disease Mapping Scenario for State Veterinary Service (SVS)
An alternative: Following disease point discovery, SVS places a point on a map, and sets a radius. The tool automatically selects underlying intersecting fields, allows for easy one-click expansion and automatically identifies the links to a land ownership database. Hours of processing are reduced to minutes and results are automatically available to all members of the service.
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Possible technical solutions for Disease Zone polygon extension scenario
Land boundaries via(A) remote
spatial databaseconnection, or
(B) “throttled” WebFeature Services
connection
Local Client
Local Client
Local Client
Database & Web Server
Database & Citrix Server
Database & Web Server
1
2
3
Users’ mouseclicks are sent
to a remote webapplication that
is built for asingle functional
purpose.
Desktop ToolsCadCorp/ESRI/MapInfo
Client Spatial LayersDisease Zones
Desktop ToolsCitrix Client
simulates CacCorp/ ESRI/MapInfo
Client Spatial Layers[None]
Desktop ToolsWeb Browser
Client Spatial Layers[None]
Users’ mouseclicks are sentto Citrix server
which simulatesa local GIS desktopon the local client.
Local GI Client & Remote Spatial Data: Use a local GI client to snap locally stored Disease Zone polygons to remote land boundary geometry via (A) network access to a remote spatial database holding PBL data (e.g. a “mart”), or, (B) a “throttled” PBL Web Feature Service.
Remote Solution-specific GI Application & Remote Spatial Data: Use a web browser and online custom application to add or remove land boundary polygons from the Disease Zone polygon by clicking desired land boundary polygons.
Remote ArcGIS & Remote Spatial Data: Use a remote GI client, provided via Citrix, to snap (remotely stored) Disease Zone polygons to underlying (remotely stored) land boundary polygons. The GI client accesses the remote spatial data via spatial database connections or “throttled” Web Feature Service connections.
Connection
Connection
Connection
Pros: Power users can apply full suite of desktop GI capabilities. Access to features enables local analysis and copy/snap.
Cons: Performance limited by network bandwidth. New data must go back through QC system. Desktop GI client training time and software/licence costs.
Pros: Same pros as (1) above, plus good performance and manageable deployment, licencing, and version control of GI Client tools.
Cons: Citrix licence is an extra expense. Cost of spatial database software & licence. Cost of GI client tool training.
Pros: Same pros as (1) above, plus ease of deployment, no licencing, minimal training, good performance and wide accessibility.
Cons: Time/cost of capturing requirements and building custom solution.
HeavyCPU load
Server Spatial LayersLand boundaries
Aerial Photos
Server Spatial LayersLand boundariesDisease ZonesAerial Photos
Server Spatial LayersLand boundariesDisease ZonesAerial Photos
HeavyNetwork
traffic
HeavyCPU load
LightCPU load
ModerateNetwork
traffic
HeavyCPU load
LightCPU load
LightNetwork
traffic
HeavyCPU load
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Spatiotemporal Data Capture and Maintenance
Spatiotemporal Data Consolidation
Spatiotemporal Data Quality Validation
Spatiotemporal Data Storage and Inventory
Spatiotemporal Multi-format Data Access
Spatiotemporal Data Processing
Spatiotemporal Infrastructure
Spatiotemporal Security
Spatiotemporal Usage Metrics and Metering
Spatiotemporal Application Development
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Tonight’s Agenda, in no particular order:
What’s the problem? Enterprises such as DEFRA want to manage their spatiotemporal data using consolidation, quality and access strategies.
Who cares? Introducing a typical cast of stakeholders (and their diverse cultures) in a shared spatiotemporal data management programme…
Laws of the jungle. Pure “greenfields” are ever more rare – what technical constraints do implementors encounter in large projects?
Meet the Beast. What is IBM’s architectural approach when answering spatiotemporal data management and systems integration challenges?
Where next? With a baseline for data exchange in place, what happens next for an integrated spatiotemporal enterprise, and for our industry?
Questions? Heckle during the presentation, or save up for the end.