geospatial big data: business cases from prodatamarket
TRANSCRIPT
Geospatial Big Data
Property Data and the proDataMarket project
Example Business Cases
Data Marketplace
http://www.millennium-project.org/
Geospatial Big Dataare
societal opportunities
Geospatial Big DataRaster Vector Sensors Mobile
It’s easier than ever to collect geospatial data, but how can we exploit these geospatial big data?
Example: Property data
One of the most valuable datasets managed by governments worldwide
Extensively used in various domains by private and public organizations
Challenges in working with property data
• Difficult to access
• Cross-sectors
• Data is highly heterogeneous and possibly large
• Data quality
• Time-consuming integration
• Lack of innovation
• …
How can we innovate (and make money) with property-related (Open) Data?
proDataMarket project goals
• To make property data more accessible, more usable and easier to understand
• To make it easier for: • Property data providers to publish and
distribute their data
• Data consumers to find and access property data needed for their businesses
2.5 Years(2015-2017)
€4.5M
20+Datasets
proDataMarket deliveries
7data-driven business
products and services
1data
marketplace
Example business case #1
Objective evaluation of the real estate properties
Business Intelligence companies (e.g. Cerved)
Automation and cost-reduction in property valuations, new services
Public administration Fact-driven social policy
Real estate agencies Speed up evaluation of properties, more objective estimation of properties
Property buyers/sellers Eliminate intermediaries
Example business case #1 (cont’)
Objective evaluation of the real estate properties in Italy, by
Istat Census
Snapshot of Italy, socio-
demographic data about: house
(its characteristics), people of the
family (personal data, education,
profession, work / study place)
people that live in house (guests)
OpenStreetMap
Point of interest of the city
about transport, downtown,
environment
Cadastral report
Property details (surface,
cadastral category, quality
status, age, ownership details)
~ 10M buildings
The evaluation of real estate property
An up-to-date, objective evaluation
of the real estate properties in
territories in Italy
=Market price €
++
=+
SYNTETIC INDEX ISTAT = -0.23 - (0.12 * UNEMPLOYED) + (0.2 * HISTORIC_BUILDINGS) + (0.58 * GRADUATES_ON_RESIDENTS) + (0.6 * STUDENTS_ON_RESIDENTS)SYNTETIC INDEX POI = -0.5 + (0.15 * closest_metro_station) + (0.14 * closest_railway_station) + (0.24 * n_bus_stops_within_800m) +
(0.6 * n_small_green_areas_pois_within_800m) + (0.02 * n_pedestrian_paths_within_1000m) - (0.05 * closest_airport)
Example business case #1 (cont’)
Objective evaluation of the real estate properties in Italy, by
Example business case #1 (cont’)
Objective evaluation of the real estate properties in Italy, by
Sample technical challenges
Semantic data heterogeneity
How to translate a point of interest into an OSM query?How to retrieve data from the whole Italy?
Structural data heterogeneityHow to compute indicators on different data structures?
Messy dataHow to exclude from computation duplicated annotations of the same real-world entities?
• Stakeholders:• Public administration (e.g. FEGA
in Spain)• Farmers and land owners• Intermediaries (e.g. service
providers)
• Problems:• Unfair grant assignment and
expenditure on audits• Incorrect grant assignments• Features defined subjectively
Example business case #2Common Agriculture Policy (CAP) funds assignmentsin Spain, by
Cadaster InformationParcels and their features:surfaces, limits, slope….
EFAs & LEs
Ecological focused areas andLandscape elements accuratelydefined using LIDAR
SatelliteKind of crops, Healthstatus, Set aside zones,Nitrogen fixing crops, CO2fixing crops…
Accurately defined
CAP parameters objectivelydefined, Automated process tocreate new datasets related toCAP Funds, Less errors, Lessaudits and field visits…
=CAP Funds
++
Fund assignment rules examples• Crop Diversification
• Kind, density and surface of Ecological Focus Areas
• Conditionality
Example business case #2 (cont’)Common Agriculture Policy (CAP) funds assignmentsin Spain, by
4) There are patterns: Groups, lines, isolated trees, etc.
5) Trees in line, hedgesNon-aligned groups, copses
6) A viewer
2) Classified points by their height
1) Raw datasets, just points
3) Points are grouped: Yellow (soil), Green (trees), Orange (bushes)
Example business case #2 (cont’)Common Agriculture Policy (CAP) funds assignmentsin Spain, by
Example business case #3 (cont’)Augment Reality (AR) for Property-related Datain Norway, by
AR for buildings AR for underground infrastructure
What’s the impact of a new building on its surroundings?
Where are the underground pipes?
• A hard copy of 314 pages and as a PDF file
• 6 Person-Months• Data collection with spreadsheets• Quality assurance through e-mails and
phone correspondence
Pains: Time consuming, Poor data quality, Static report without live updating
• Live service• Efficient sharing of data• Simplified integration with
external datasets• Live updating• Reliable access• …
• Risk and vulnerability analysis, e.g. buildings affected by flooding
• Analysis of leasing prices
Report Reporting Service 3rd party services
Example business case #4Reporting state-owned real estate propertiesin Norway, by
https://datagraft.net 21
Linked Data Approach: DataGraft
DataGraft: Data Transformation and Knowledge Graph Publication Process
• Interactive design of transformations
• Repeatable transformations
• Reuse/share transformations (user-
based access)
• Cloud-based deployment of
transformations
• Self-serviced process
• Data and Transformation as-a-Service22
TransformGenerate
RDF
Ontology XOntology X
Ontology X
Ontology mapping
RDF Graph
Raw Data Prepared Data
Map
Map
Semantic graph database
Geospatial Data is BIG thing
Innovation with property-related data in proDataMarket
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