Big Data Implications for Safe City Security Solutions A Frost & Sullivan White Paper in conjunction with HP
frost.com Part 1: Introduction to the Safe Cities Market & Associated Big Data Implications... 3 Part 2: Overview of Safe City Challenges... 4 a. Challenges Cities Face Identifying the Customer Problem... 3 b. Benchmarking Snapshot of Global Safe City Index... 4 c. Key Questions that City Stakeholders Need to Answer... 4 d. Review of Selected City Case Studies for Threat, Operation and Citizen Projects... 4 e. What s Next for Safe Cities in 2015 and Beyond... 4 Part 3: Solution Evaluation & Assessment... 8 a. Introduction of HP Solution... 8 b. Mapping of HP Solution in Big Data Context... 8 c. Solution Differentiators & Highlights... 8 d. Solution Recommendations... 8 contents
Big Data Implications for Safe City Security Solutions Part 1: The Safe City Market and Big Data Cities continue to provide unique challenges and opportunities. The growing urban population and migration toward cities, increasing infrastructure, and implementation of smart city solutions show no signs of slowing down. The unrelenting pace of this evolution has had significant impact on city operations and on the level of information available to city governments, operators, and service providers across the world. Security and safety remain at the heart of city operations, and the evolution of safe cities over the past 10 years has seen a movement from the use of traditional video surveillance and basic information to the deployment of advanced technological solutions. This has fuelled rapid growth of information and data produced across operations in transport, energy, security, and other services. The figure below maps the development of cities and growth of information sources. Figure 1: The Evolution of Safe Cities Communication and evidentiary video solutions Evolution of Safe City Unified Intelligence and Safe City solutions Variety/ Volume/ Veracity/ Velocity of Data PMR Limited video Consolidation and upgrades of video networks Increased sensors Integration of intelligent traffic sensors Social media Growth of connected realtime data bases Biometrics Integration of new data with video Connected citizen information Internet of Things Big Data analytics Next-generation 911 Video streams from public Smart cities/ devices Connected smart cities/ devices across city infrastructure Analysis of public services and citizen engagement Communication and evidentiary video solutions Evolution of Safe City Unified Intelligence and Safe City solutions What is Big Data and Who is in a Position to Utilise it? The concept of Big Data typically refers to a volume of data so great that traditional IT systems and technologies cannot manage, store, or process it. It will usually come from multiple sources and in different forms, including text, video, audio, and email. Creation and collation of all this data presents a number of opportunities if it is utilised correctly. Like many companies and organisations that have leveraged big data for business intelligence, cities have started to engage with the information sources that they have acquired to try and gain operational benefits and advantages from it. However, the progressive nature of the city depends on a number of factors. Cities have adopted technology at different rates based on a number of different drivers. Frost & Sullivan completed an analysis of the 600 most populated cities to benchmark them against the implementation of safe city technologies. They were mapped against three criteria: threat, technology investment, and wealth. 3
frost.com The cities fell into different clusters and differentiated the level of adoption of safe city technologies and solutions. The top 216 cities landed in the top four clusters, which were categorised as Leaders, Followers, Aspirational and Acquirers, and have been grouped as Advanced Developed Cities. These categories would either have high threat levels, technology adoption, wealth, or a mix of all that has driven implementation of security solutions. The map below shows a breakdown of where these cities fell by region against the remaining 378 cities that fell in a second tier of categories that were predominantly driven by traditional solutions and basic video surveillance. Graphic 2: Safe City Benchmarking According to Threat, Wealth, and Technology Intent CENTRAL ASIA EUROPE 3 18 NORTH AMERICA 65 0 48 16 AFRICA MIDDLE EAST 11 14 ASIA- PACIFIC LATIN AMERICA 7 50 47 229 35 51 ADVANCED DEVELOPED CITIES: 216 EMERGING CITIES: 378 The advanced developed cities were predominantly made up from North America, Europe, and Asia Pacific. These are the most likely cities that are producing large quantities of data. However, many of these are still not using it effectively to bring about clear benefits to security, citizens, and city operations. PART 2: Challenges of the City Operator In a competitive environment where city operators and mayors want to project a safe and secure image to attract investment, skilled workers and tourism, they need to make good decisions to improve safety, operation, and citizen wellbeing. One of the key questions that many of them face is how to use the data from the diverse sources efficiently to help inform strategy and decision making. The level of security and operational technology, sensors, and data that they produce has led to challenges with integration and gathering a clear operational picture across city operations and public safety. This is applicable at a citywide level, but also in respects to the management of the data. 4
Big Data Implications for Safe City Security Solutions Figure 3: City & Data Challenges City Challenges Resource Allocation Coordination Decision Making Budget Constraints Operational efficiencies Data Challenges Volume, Velocity, Veracity and Variety of Data Multi agency Collaboration Sharing the data Organising / Collating / comprehending Storage of the data In a digital world, there are a number of potential implications for cities that do not engage with the data resources or use the information at their disposal. These could include: not identifying potential threats; not foreseeing underlying problems, issues or inefficiencies; or even missing potential opportunities to improve services. The effects of these could impact competitiveness of the city, reputation, and potential tourism and economic prosperity. Questions that City Operators Need to Answer In many cases, however, cities have the level of data and information to help identify key challenges that they are facing, but are not leveraging it effectively at present to help operational planning and strategy. In some cases the largest challenges may be very obvious to the city operator and citizens. This could fall into three categories: Threat: Crime, terror, large events, natural disaster Operation: City services, transport, business/financial performance Citizens: Health, wellbeing, safety, mobility For example, if a city has high crime rates, it is easy to identify. In other scenarios, underlying issues such as health issues or provision of city services may be causing problems that are not obvious. In both cases data can be critical to identify and measure the scale of the problem, and can also help to provide insights and information to help rectify issues. City operators need to go through a process of asking specific questions to help understand the key challenges, but also to find potential solutions. Figure 4: Key Questions City Operators Should Consider IDENTIFY MEASURE IMPLEMENT/ AMEND REVIEW What is the biggest challenge that I face? Why is that my biggest challenge and what needs to change? How can I make a change to improve the situation? When will the changes make an impact? Has the issue improved? Data is key to all the questions to either understand, process, change or review 5
frost.com CASE STUDIES: The following case studies demonstrate how cities have gone through the process of asking themselves the above questions, and used data to either discover specific issues that need addressing or found solutions to ongoing problems. Case Study 1: Addressing Threats for Dubai Police - Delivering Actionable Intelligence of Structured and Unstructured Data Through Automatic Number Plate Recognition What was the challenge? The Dubai police force faced a number of challenges to locate wanted vehicles throughout the Emirate. Why was it a challenge? The challenge was to automate previously manual tasks using printed lists of wanted license plate details and relying on manpower to identify potential criminal vehicles. This was time consuming and ineffective. What was implemented? The requirement included using technology operating 24 hours a day to capture both English and Arabic lettering, whilst also being able to find and track wanted vehicles in moving traffic. The system involved a range of cameras deployed throughout the city and mounted on patrol cars that were able to capture number plates in real time. The analytics platform was able to process all the information and give real-time insights to law enforcement officials on cars that were linked to criminal activity or traffic offences. What was the outcome? The system enabled a real-time update of information, providing officers with reliable and accurate intelligence to respond. Within 18 months the system helped Dubai police capture 2,739 persons of interest or criminals. Case Study 2: Improving Operation in Auckland Transport Streamlining Traffic Flow and Providing Enhanced Safety for Citizens What was the challenge? As part of its future cities initiative, the city of Auckland wanted to improve the safety and well-being of citizens on public transport networks and roads. Why was it a challenge? The whole project needed to help deliver a safer environment and more efficient services for cyclists and transport users in the city. It centered on being able to identify issues early and responding in real time to incidents to reduce congestion and public inconvenience, whilst getting situational awareness for larger scale events and accidents. What was implemented? In 2014, Auckland Transport announced its intention to deploy a big-data solution to leverage vast quantities of data from multiple sources, including text, images, audio and real-time video to proactively identify trends and respond to critical safety incidents. This includes information and feeds from over 2,000 cameras that are deployed, environmental sensors, and real-time social media and news feeds. What was the outcome? The outcome is yet to be fully measured; however, the solution continues to work toward enhancing the lives of citizens and becoming a global model of transport for cities. 6
Big Data Implications for Safe City Security Solutions Case Study 3: Enhancing Citizen Services in Norfolk County Council Transforming a UK County Council s use of Data What was the challenge? With a requirement to save 189 million of public spending in three years, Norfolk County Council had to figure out how to deliver smarter public services to citizens. Why was it a challenge? The council didn t know exactly where to focus and needed help identifying particular areas to improve services and increase efficiency. What was implemented? The Council began utilizing data to provide a seamless experience to its users. The initiative included creating an information hub with an analytical platform that was able to integrate structured and unstructured data, particularly with the high variety of data at their disposal. What was the outcome? The solution has provided multiple benefits and was able to identify 840 potential troubled or vulnerable families through analysis of different sources, including school, government and law enforcement data. In addition, the data platform has managed to analyse the allocation of resources in the council, identifying potential contracts at risk, inefficient contracts, and further potential savings from the budget. The next stages include deploying master data management that can provide indicators of vulnerable people and help speed up intervention, referral and education where required. Furthermore, by using data, they will be able to identify key pressure points on critical services, such as healthcare indicators, and better understand the biggest health issues in the region so that they can implement strategies to improve citizen well-being. The data initiative has managed to identify areas of focus, help allocate resources, and improve efficiency across council services. 7
frost.com PART 3: Solution Evolution and Assessment HP has developed an offering that can help city operators and public safety organisations navigate through their questions and challenges, and can provide solutions to help them holistically unlock insights from Big Data and better manage data requirements. This will help ensure that they extract comprehensive and actionable intelligence to influence and accelerate positive decision making. They have done this through a consultative approach and strong engagement with end users to go through the process of answering the critical questions to help understand and identify the end users issues. Using its Big-Data platform, HP Haven, it can then deliver an offering that fits the end users requirements. HP Haven is a comprehensive, scalable, open and secure platform for Big Data analytics. It can address 100% of data from all sources through its three analytical engines: HP IDOL (Intelligent Data Operating Layer) for unstructured data analytics, HP Vertica for structured and semi-structured data analytics, and HP Distributed R for predictive analytics. In addition, HP brings strong data integration and advisory services that provide city operators with the support they require to negotiate in often complex environments involving multiple stakeholders and data sources. Moving forward, with the continued growth of data, cities will need to invest in solutions that can help them analyse and interpret the information to give them direction to make informed decisions to improve services, safety and operation within the city. HP is well placed to help guide them through the process and develop the appropriate solution to address current and future public safety requirements. 8
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