1 Annual World Bank Conference on Land and Poverty 2015, March 23-27, 2015, Washington, DC FIG Com 3 Publication on: The Potential of Crowdsourcing in.

1 Annual World Bank Conference on Land and Poverty 2015, March 23-27, 2015, Washington, DC

FIG Com 3 Publication on:

The Potential of Crowdsourcing

in Geospatial Data Collection

Prof. Dr. Yerach Doytsher

Mapping and Geo-Information Engineering,Technion, Israel

Annual World Bank Conference on Land and Poverty 2015, March 23-27, 2015, Washington, DC

2 Annual World Bank Conference on Land and Poverty 2015, March 23-27, 2015, Washington, DC

Acknowledgement The paper and this presentation is based on the contribution

of all my co-researchers in this study: Prof. Dr. Chryssy Potsiou, National Technical University of Athens,

Athens, Greece; FIG President Dr. Sagi Dalyot, Technion – Israel Institute of Technology,

Haifa, Israel Prof. Dr. Hartmut Muller, University of Applied Sciences,

Mainz, Germany Prof. Dr. Ioannidis Charalabos, National Technical University of Athens,

Athens, Greece Prof. Dr. Muki Haklay, University College London,

London, UK Prof. Dr. Vlado Cetl, University of Zagreb,

Zagreb, Croatia

3 Annual World Bank Conference on Land and Poverty 2015, March 23-27, 2015, Washington, DC

Outline FIG / FIG commission 3 GeoSpatial data acquisition New trends in data handling Web applications Wisdom of the Crowd & Crowdsourcing Problem definition of these terms OpenStreetMap and Waze projects The Neogeography revolution Main findings Planning & management of cities (an example)

4 Annual World Bank Conference on Land and Poverty 2015, March 23-27, 2015, Washington, DC

FIG / FIG commission 3 FIG - The International Federation of Surveyors

An international, non-government organization whose purpose is to support international collaboration for the progress of surveying in all fields and applications

The FIG vision A profession, armed with knowledge and best practices,

extending the usefulness of surveying for the benefit of society, environment and economy, ….

Members of FIG Member associations - national surveying associations

Affiliates - surveying organizations, NMAs, etc.

Corporate members - institutions/agencies providing commercial services

Academic members - departments promoting surveying education

5 Annual World Bank Conference on Land and Poverty 2015, March 23-27, 2015, Washington, DC

FIG / FIG commission 3 10 commissions lead FIG’s technical work

Commission 1 : Professional Practice Commission 2 : Professional Education Commission 3 : Spatial Information Management Commission 4 : Hydrography Commission 5 : Positioning and Measurement Commission 6 : Engineering Surveys Commission 7 : Cadastre and Land Management Commission 8 : Spatial Planning and Development Commission 9 : Valuation and the Management of Real Estate Commission 10 : Construction Economics and Management

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FIG / FIG commission 3 Commission 3 on Spatial Information Management

Management of spatial information about land, property and marine data

Spatial data infrastructure Data collection, analysis, visualization, standardization,

dissemination Support of good governance Knowledge management for SIM Business models, public-private-partnerships Professional practice and administration

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FIG commission 3 FIG Commission 3 initiated during the 2011-2014 term an

in-depth research on possible uses of geospatial crowdsourcing within: Surveying communities Cartographic communities Using crowdsourcing geospatial data collection,

inter alia, for: SDIs (spatial data infrastructure) Cadastre (for land management purposes) Navigation maps and navigation systems

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GeoSpatial Data Acquisition Traditionally:

Carried out by Professionals (“experts' domain”) – surveyors, cartographers, photogrammetrists, etc.

Mapping and surveying projects initiated by the public sector –NMAs, governmental agencies, municipalities, etc.

Date acquired from different sources and of various qualities: Field Surveying utilizing TS and GPS receivers Photogrammetry utilizing stereo pairs of aerial/space imagery Cartographic digitization and scanning of existing maps and

drawings (e.g. urban infrastructure plans) LiDAR systems utilizing laser ranging/GPS/INS techniques

9 Annual World Bank Conference on Land and Poverty 2015, March 23-27, 2015, Washington, DC

New Trends in Data Handling A development of online publishing tools, and

particularly of the World Wide Web (WWW) simplified: Interaction between users ‘Navigation’ through enormous amounts of data and

information

Users all over the world are involved with data processing (thanks to Web 2.0)

This revolution has brought the development of two important working methodologies: Wisdom of the Crowd Crowdsourcing

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Web Applications

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Wisdom of the Crowd The GeoWeb Described by Surowiecki (2004):"Large

groups of people are smarter than an elite few“. The crowd can be any group of people that "can act

collectively to make decisions and solve problems“ ‘Wise’ crowd has to exist of four main attributes :

Diversity Decentralization Independence Aggregation

By: Jeremy Dean

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Crowdsourcing Main definitions of the term are:

1. Taking a job traditionally performed by a

designated agent and outsourcing it to an

undefined, generally large group of people”

(Howe, 2006).

2. “The practice of obtaining needed services, ideas,

or content by soliciting contributions from a large

group of people and especially from the online

community rather than from traditional employees

or suppliers” (Merriam-Webster online dictionary,

2014).

13 Annual World Bank Conference on Land and Poverty 2015, March 23-27, 2015, Washington, DC

Crowdsourcing (cont.) Main definitions of the term are (continued):

3. “Can be explained through a theory of crowd

wisdom, an exercise of collective intelligence… It is

a model capable of aggregating talent, leveraging

ingenuity… crowdsourcing is enabled only through

the technology of the web” (Brabham, 2008).

4. “A type of participative online activity in which an

individual, an institution, a non-profit organization,

or company proposes to a group of individuals of

varying knowledge…” (Estelles-Arolas and

Gonzalez-Ladron-de-Guevara, 2012).

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Wisdom of the Crowd vs. Crowdsourcing Wisdom of the crowd

clearly defined by Surowiecki (2004).

Crowdsourcing does not have a comprehensive definition, although attempts

were made

These two working methodologies can be found across fields as diverse and popular as:

culture, psychology, biology, behavioral economics,

artificial intelligence, social sciences, military,

history, politics, and computing

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Problem Definition Crowdsourcing and wisdom of the crowd are often

terminologically intertwined and indefinite

There is no clear statement trying to define these two terms specifically in respect to the geospatial scientific discipline and geo-services and processes

Aiming to focus on the differences between the two terms with the use and analysis of two main location based services

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Differentiation The comparison and differentiation between the two terms

cab be demonstrated and analyzed in respect to two very popular (tens/hundreds of million users worldwide) location based services having geospatial characterization:

1. OpenStreetMap (OSM)

2. Waze (© 2009-2014 Waze Mobile) Four primary indices were selected to characterize various

processes involved in each of the services: Diversity Decentralization Independency Aggregation

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OSM A collaborative online project An open-source editable vector

map of the world

Users can:1. view and edit the underlying data

2. upload GPX files (GPS traces) from hand-held GPS units

3. correct errors in local areas according to satellite imagery and out-of-copyright maps

4. download desirable data freely and use it to their own purposes

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OSM Number of registered OSM users in the years 2005-2013

(Neis and Zielstra, 2014)

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OSM

OSM drawbacks: Maps are created not according to cartographic standards There is no systematic process to cover the area Missing information mainly in non-urban regions Heterogeneity resulting from variable measuring equipment,

different contributors, etc.

Still, there is a question mark about the quality of acquired information The focus right now is on:

Positioning accuracy and completeness of the information

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Waze A community real-time GPS-based traffic and geographical

navigation service

+

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Waze Drivers can:

1. share real-time traffic and road information (with just opening the Waze app)

2. actively report traffic jams, accidents, road dangers, etc.

3. from the online map editor users can add new roads, update existing roads, add landmarks, house numbers, etc.

The collected data are aggregated and provided to the user/ community as alerts, traffic flow updates – and more

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Identification of Terms Diversity

What? each individual contributes different pieces of information, and in mapping it helps in covering wide topographic areas – crucial for both terms

How? OSM: every volunteer can (actively) add data Waze: every user add (passively) new roads, place of accidents, road dangers, etc.

Decentralization What? answers are not influenced from the hierarchy e.g.,

boss, founder or a group of people – crucial for both terms How? OSM: relevant only in terms of coverage

Waze: some information might influence on users’ decision

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Identification of Terms Independency

What? person's opinion is not affected by other people in his close vicinity but from his own judgment – crucial in wisdom of the crowd, less crucial in crowdsourcing

How? OSM: a volunteer will search for less mapped area Waze: users supply their own route and alerts; the route is influenced from all users

Aggregation What? a mechanism that unifies all individual opinions into a

collective decision or conclusion – crucial in wisdom of the crowd, unessential in crowdsourcing

How? OSM: the current update is added to the final DB version Waze: the information is by aggregation of all ’reports’

24 Annual World Bank Conference on Land and Poverty 2015, March 23-27, 2015, Washington, DC

Identification of Terms Aiming at deferring between the two terms

“Crowdsourcing” and “Wisdom of the Crowd” it can be concluded that: OSM is a typical crowdsourcing project Waze is more a wisdom of the crowd project, mostly because

of its aggregation process characteristics

Due to rapid technological developments: A clear definition might be hard to achieve, since it seems that

both terms are in principle flexible and dynamic

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Geospatial Accuracy and Precision Since the beginning of era of digital geospatial data (and GI

systems) the issue of quality analysis is getting an increasingly attention

In 2002, the International Organization of Standards (ISO) defined the quality characteristics of geospatial information. The definition includes:

Integrity Positioning accuracy Logical consistency Just in time, etc.

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Geospatial Accuracy and Precision Terms Moreover, accuracy and precision should be differentiated

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Accuracy of Crowdsourced Data A comparison in Germany (Helbich et al., 2012) between OSM,

TomTom (conventional navigation maps) and SD (a German mapping authority) – differences in meters

High/medium/low positioning accuracy of OSM and TomTom

OSM vs. SD TomTom vs. SD OSM vs. TomTom

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Accuracy of Crowdsourced Data A comparison in London (Haklay, 2010) between OSM and the

Ordnance Survey maps

Average difference in location was approximately 6 meter Best results: difference of 1-2 meter; the worst 20 meter

Vector approach (by using buffers) Raster approach (by overlaying layers)

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The Neogeography Revolution The GeoWeb has transformed itself as the platform for the

novel and pioneering online GIS The mapping field becomes a public domain (not only an

experts' domain) Users all over the world are nowadays involved in data

collection and processing Mapping projects and services are using today groups of

volunteers focusing on the of geographic data Creating and Updating geospatial information (online

maps/applications) is becoming possible even by laymen Practically replacing licensed surveyors/cartographers/geographer

experts

Crowdsourcing: A knowledge paradigm

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The Change in the History of Human Mapping

In the past:

The geospatial data knowledge collection & dissemination was carried out via authoritative professionals only

The process was characterized as top-down scheme

Nowadays:

Less qualified professionals and even citizens and laymen are the new data collectors and knowledge producers

The process is gradually shifted to the bottom-up scheme

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Main Findings

At the moment we have two parallel systems: Existing official geospatial databases (public sector / NMAs) New crowdsourced data and datasets

In order to achieve an optimal utilization of crowdsourcing capabilities, it is required to:

Enable the integration of crowdsourced data in the existing (formal) geospatial databases

Increase the public opinion, its capacity and willingness to participate in the changes in the geospatial domain

Decrease the concerns of the authorized professionals from these new changes in the technology

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Main Findings (cont.)

There is a need to develop specific requirements and work methodologies for implementation of the crowdsourcing techniques in various fields

For establishing and updating SDIs/GSDIs, For land administration and the collection and updating of

cadastral data To facilitate fast and fit for purpose cadastral products

Open issues to be handled/discussed in the future: Accuracy Completeness Standards

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An Example of the Impact: Planning & Management of Cities

Efficient planning is based on updated geospatial data In the past in light of limitations (manpower and budget) - updating

processes were not carried out on frequent basis

Efficient management should be based on (near) real-time info as to the human activity in the urban environment

In the past this information was collected with a considerable delay – days, weeks or even months

The new era of VGI and crowdsourcing opens new frontiers toward collecting real time geospatial data - infrastructure info, pedestrians activity, traffic and many more

Detailed and updated geospatial data will lead to efficient planning processes and effective management processes of cities “smart cities”

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