GeoSpatial-temporal analytics to gain insight from linked open data

Collective endeavours, operating in an environment of efficient collaboration and informed decision making in a value network, bear the only effective way to meet the challenges and threats we face in this modern, interconnected world. Enhanced inter-agency and inter-company communication and collaboration has been defined as the capability to deliver information superiority when required to enable agile and informed decision making to underpin effects-based operations: delivering the right effect, at the right time, to achieve the outcome required. Challenges and threats in our modern world are global and multi-faceted requiring complex responses: governments and corporations buoyed by the realization that the interests of both are mutually engage, are pursuing joint corporate social responsibility to make life and business conduct safe and sustainable. One outcome is increasing openness: organisations increasingly publish data and knowledge in open formats and open spaces and (others) provide tools to gain insight from this open and accessible data. This case study summarizes the technological state-of-the-art and points the way how value networks can benefit from these digital society trends.


The Problem Space
Collective endeavours (to achieve a specific goal or end-state) require the interaction and coherent cooperation between governmental, non-governmental and commercial organizations.Coherency relies on the continuous and real-time sharing of situational awareness between all participants in the value network.As we move forward into the decade of the "smarter planet", increasingly instrumented, interconnected and intelligent, data volumes which underpin decision making will double every two years.A large percentage of that data already is accessible freely in the internet e.g. in social networks.The ICT challenge is three-folded: how can relevant data be found, how can it be assured and what insight can be gained? 2 Linked Open Data Within the semantic web, data is identified and accessed via Uniform Resource Identifiers (URI).Coding, referencing and linkage between data resources can (and should) be done using the Resource Descriptor Framework (RDF, 0).Linked Open Data (LOD) is defined as the "cloud" of freely accessible data defined and linked via these open standards.

Open Data Policy
The "Re-Use of Public Sector Information (PSI) Directive", 2003/98/EC (0), encourages and strives for extensive publication and opening of open government data -and therefore also recommends a fundamental change of paradigm and policy from the "need-to-know" to the "need-to-share" principle fundamental for network enabled capabilities (NEC) and successful engagements in collective endeavours:  Publicity: 3 Corporate Social Responsibility The Digital Agenda Europe (0) sets the policy and implementation targets for a unified digital society and an integrated single market within and beyond the member states of the European Union.The European Commission in particular promotes the adoption of Open Data and the realization of the intelligent future internet (FI) of people, things and services (IoPTS).As these political mandates are not specific to Europe and the European Union, the recent IBM Corporate Responsibility Report (0) acknowledges that more and more the concept of corporate citizenship is realized as an opportunity to create business value: "Organizations today are embracing a more sustainable approach to business -one that takes into account the environmental and societal impact of their activities.By factoring this accountability into their strategy, they implement new ways to source, manufacture and distribute goods in a more sustainable manner, often while simultaneously lowering costs.And, based on more transparent and proactive engagement with employees, consumers and the communities where they operate, organizations are becoming better equipped to create products and services for a smarter planet".
Figure 2 shows a few examples of critical infrastructure domains and their sustainability challenges.Each of these scenarios is truly multi-disciplinary: characterized by facts, measurements and events from different domains.For example, weather, resource availability, human factors and socio-economic behaviour of populations all influence the ecosystems.Through the Smarter Planet Initiative, IBM is driving solutions to deliver the social and economic benefits of our ability to exploit information and is already witnessing the convergence of business strategy and citizenship strategy.The issues being addressed as a result, and shown in Figure 2, range from clean water, to safe food, to sustainable and vibrant cities, to smarter work and to empowered communities.These are not a choice of either strategy driving the other; it is the alignment of both.This alignment of citizen and business strategies, is not only a recipe for economic growth, it also enables expanded economic and societal opportunity.But of course, building sustainable ecosystems and protecting and maintaining the interdependent critical infrastructure networks requires enhanced capabilities to detect events, to monitor behaviour, to gather and interpret data and to share, aggregate and fuse the information pieces into actionable intelligence.

IBM City Forward
City Forward, launched on December 13 th , 2010 (0), is a donation to cities' and city subsystems' stakeholders.It is a one-stop shop for elected and appointed officials and citizens of cities for ongoing analysis of city information and the city's current state.It encompasses an aggregation of global best practices and provides the kind of community knowledge repository which can be further populated by using LOD as raw data input.City Forward is a tool for helping cities or city-like entities such as an airport, become smarter; it provides:  Predictive modelling and simulation and decision support for future policy  Comparison to an ideal smarter city (model)  Exploration and visualization tools that allow subject matter experts from academia, government and industry to illustrate ideas and trends and encourage discussions of their validity and potential impact  Illustration of a city's journey via historical snapshots of its data  Best practices information and lessons learned from other geographies  Social media and collaboration tools to engage citizens in city decision-making  Interrelated and integrated information from sources ranging from real-time social sensors to decennial censuses providing ad hoc situational awareness and a foundation for new insights.
The City Forward rationale is to provide tools to create a consolidated source of information to enable city, state, regional or national leaders to collaborate with citizens in priority setting to make their cities smarter.Participation and inclusion of citizens in policy setting is considered not only to be a way of becoming more efficient and effective in a municipality, but also make the city a safer place to live in.Whereas IBM commercial offerings typically focus on operational, tactical analysis, City Forward focuses on analytics and correlation at a high (strategic) level.Potential benefits include:

Behaviour and Business Analytics Solutions
Open platforms like City Forward can only prepare the ground for decision support including human factors and social behaviour analysis -a domain in collective endeavours where communication and collaboration in the value network at hand is most needed.The City Forward knowledge base functions as sensors complementary to corporate business intelligence platforms and contributes to the intelligence aggregation, correlation and fusion process which provides real-time situational awareness in a competitive situation; Figure 6 illustrates the end-to-end process to generate a low-latency operational picture for all parties in the value network -including the use of open data sources such as the internet to provide the decision makers with a complete situational awareness.This methodology generates a bi-folded benefit: more data can be scanned and the data passed on to decision makers is more relevant.This effect cannot be achieved with only one step in the process; it takes the combination of all three:  Scan digests vastly greater quantities of information ("raw data") and such increases the "take"  Cue creates and leverages Linked Open Data to ultimately  enable Focus onto material relevant to commanders.
Using the same technologies as in the City Forward cloud, IBM offers a variety of accelerated solutions across the whole value chain from data collection to status visualizations.Examples for intelligence solutions in (semi-) closed networks that can play an important part in collective decision making include  Crime Information Insight for Public Safety and Security: IBM offers a performance measurement solution for law enforcement and policing agencies aiming to gain more insight into their operations.It includes planning support, score-carding, dash-boarding and reporting to maximize effectiveness. Performance Management for Governments who are often data-rich but information-poor.Business analysis can help governments to establish a strategic view of what they want to achieve -independent of reactive or election-driven agenda.Moving towards open government, the envisioned transparency makes performance management critical and potentially provides the citizen clear information against which to measure the performance of their governments. Business Analytics for Smarter Cities: IBM solutions provide municipal government leaders with a data-driven, consistent and real-time framework for defining and achieving strategic goals.By tracking work groups, departmental, agency and government-wide performance against goals, intervening when necessary before an issue becomes critical and continuing to drive toward positive outcomes, city leaders can better manage in agile environments, improving service levels to citizens and enterprises, managing budgets and day-to-day operations while identifying and correcting undesirable and unexpected trends leading to improved outcomes. Crime Prevention and Prediction: many legacy crime information systems are incompatible, silo-like systems making pattern recognition a manual paper-based task.Coupled with tools to extract facts and relationship from unstructured information, IBM SPSS analytics solutions provide the capability to analyze crime data, understand events that trigger and enable crime and better predict upcoming criminal activity to facilitate effective deployment of personnel.
6 Visual Analytics of Spatiotemporal Data Spatiotemporal data involve geographical space, time, various objects existing in space and multidimensional attributes changing over time, as depicted in Figure 8.This complexity poses significant challenges for analytics; however it also enables the use of the data for many purposes:  To study the properties of space and places  To understand the temporal dynamics of events and processes  To investigate the behaviour of people and objects.EPIC was launched November 1 st , 2010, and will build a sustainable cloud-and Government Industry Framework (GIF) enabled SOA foundation for information and web services to be shared and governed in a global environment.This platform could, and should, also be used for experimentation in the area of collective decision making in prototypical value networks and for the establishment of best practices for good governance.

Figure 1
depicts one initiative to populate this knowledge base in a W3C community project (0) and shows the current topology of the included network of open datasets.

Figure 2 :
Figure 2: Sustainability on a Smarter Planet

Figure 3 :
Figure 3: IBM Cloud-Based distributed Science Research Figure 3 depicts the natural and medical science research programs supported by IBM global research programme, which maps the human genome supported by unused computer capacity around the world captured with advanced virtualization and load balancing technologies.
City agencies can cooperate and integrate between themselves and with their citizens  Cross-views of city subsystems and understanding of interdependencies can be achieved  Current state analysis across all subsystems  Decision support tools enable cities to assess what is needed to become smarter  Involvement of citizens in priority-setting and policy-making  Learning from other cities' success stories  Promotion of data transparency and public engagement  New and unexpected insights by using powerful interactive visualization tools  Insight gleaned from analyzing the data can force us to rethink the physical, commercial and governance structures that orchestrate life in cities  Opportunity for "network empowered governance"  Ability to simulate scenarios for smarter planning  Development of a roadmap towards a smarter city  Collection of useful insight for future decision making  Better use of scarce resources in tough economic conditions  Encouragement of transparency and accountability of open government  Access to newly published datasets (e.g.LOD) and data sources  Validation of other sources of data.City Forward can be considered a cloud-based showcase for the IBM premier analytics software capabilities as illustrated in Figure 4.

Figure 4 :
Figure 4: IBM City Forward Architecture

Figure 5 :
Figure 5: IBM City Forward Data Categories

Figure 7 :
Figure 7: Scan -Focus -Cue to enable a scarce Resource

Figure 8 :
Figure 8: Dimensions of Spatiotemporal Data Visual Analytics (0) is the science of extracting information from large, homogenous, multimodal data sources.It relies on the smart combination of automatic algorithms and interactive visualization.The objective of IBM research is to develop a web-based platform that enables people to access, explore and analyze information in a visual and intuitive manner:  Consumption and integration of the relevant (LOD) datasets, possibly requiring data curation, semantic mapping and reconciliation  Interactive visualization capabilities of time-variance and geospatial attributes of the datasets  Consumable representation of the underlying data and its complexity; an example is given in Figure 9.

Figure
Figure 9: Spatiotemporal Event Analysis

Figure 10
Figure10gives an overview of the framework bringing the technologies together in business intelligence applications.

Figure 11 :
Figure 11: European Platform for Intelligent Cities