Geographic information systems, or more commonly known as GIS, is a technology that combines location with real-time or static data.
This technology collects, manages, analyses, and shares data to achieve location intelligence. Rooted in the science of geography, GIS integrates many types of data.
It can use large data sets from different sources and represent them as meaningful real-time apps, dashboards and analytical tools.
Immediate visualisations can be produced, which give critical insights into fast-moving situations. By using this technology many situations or problems can be handled more efficiently by how it reduces the complexity of the situation. It’s purpose is to enable smarter decision making.
Governments and Public Health Agencies using GIS to Solve COVID-19 issues
Today GIS technology is being extensively used in the fight against Covid-19. Governments, public health agencies and other organisations are using this technology in their defence against the pandemic.
On the global scale, it is being used to show how, over time, the virus is spreading across the world. It is also being deployed for contact tracking and tracing.
GIS also helps predict health needs and spikes, supports the delivery of vital PPE and facilitates the delivery of medicines to vulnerable citizens.
Mapping data is also being used to provide local authorities with key information. It helps tailor data reports about local demographic, economic and health statistics which can help plan community response to COVID-19.
Maps and visualisations are an excellent way to present large amounts of information so it can be quickly and easily interpreted. As such, mapping is being used to collect, collate, integrate and share critical information with frontline healthcare staff, doctors and health authorities.
Transport organisations are using it to ensure appropriate services running for key workers.
GIS is critical to answering many Coronavirus related questions: Where are current cases in the community? Where is the virus likely spread? Where are the closest testing sites? Are there specific demographics that are at greater risk? Which areas or sectors are being most exposed? The numbers of hospital supplies and hospital beds on a regional or national basis. How quickly local and regional hospital resources are being depleted?
Government use GIS technology to Communication Purposes
Governments are using mapping technology for communication purposes through maps, apps, and dashboards. Examples include sharing a situation assessment with the media and the public to help the public locate healthcare facilities.
Many local governments are producing story maps to keep citizens informed on what’s happening in their area.
They also use GIS maps to communicate emergency information regarding school closures, public notices and other Coronavirus containment measures.
Accurate public information is critical for risk communication and behaviour changes such as appropriate hygiene measures and social distancing recommendations.
GIS technology has become part of mass notification systems, allowing leaders to send out messages to staff, partners, or the public based on geographic location.
1. Restricted Community Mobility (Best chance to beat COVID-19)
Keeping people at home is noticeably slowing the spread of the virus. The rates of infection in locked-down areas have slowed albeit not as quickly as desired in some places.
To help public health officials better understand the movement of people, Silicon Valley giants Apple and Google have begun releasing reports documenting relative changes in community mobility. These reports use anonymous locational data from their maps services to track daily changes in the movement of users against a baseline value.
Specifically, Google is using the average number of visits to places for each day of the week over a five-week period in January as its baseline. Crucially, visits to these places are aggregated by the type of establishment.
These tags present a key aspect of the dataset as they allow users to identify mobility trends by the category that a place belongs to. The data shows percentage deviations from the baseline in January, back when things were largely normal.
Things are hardly normal now. In almost every society, schools and workplaces are shut, while only essential services are allowed to carry on.
The World Economic Forum reported that nearly 3 billion people—close to half of the world’s population—have come under COVID-19 lockdowns. This number is likely higher now as the outbreak worsens and governments take even more stringent measures.
The dust appears to be settling in Western Europe however which was one of the first few regions outside China to be impacted by the virus.
As Spain and neighbouring countries begin to ease their lockdowns, it is worthwhile to take stock of the effectiveness of their safe distancing measures. A second wave of infections is upon us, as evidenced by ongoing events in Beijing.
In the above visualisation, it is not surprising that visits to grocery shops and pharmacies peaked before lockdowns came into effect, probably due to bouts of panic buying.
Potential uses of this data could include restocking supermarkets in advance to cope with a surge in demand or developing internet infrastructure to cope with higher demand from residential areas.
The right use of actionable data will help policymakers gauge the efficacy of their regulations. It can aid in the enforcement of lockdowns, as well as enable a targeted and phased reopening of the economy.
Both Apple and Google should be commended for their efforts in making anonymised data transparent and available for policymakers to gain valuable insights. We hope to see more such public interest initiatives in the future.
2. Costly Miss Explosion of cases in New York
If the state of New York were a country, it would have more COVID-19 cases (as at Mar 29, 2020) than any country other than the US. Such is the scale of the coronavirus situation in New York. T
he state has become the epicentre of the pandemic in America. Notably, the crisis and the ensuing lockdown caused a tussle between New York Governor Andrew Cuomo and US President Donald Trump.
Trump asserted that he has the ultimate authority to reopen the economy and Cuomo has refuted this claim.
Speaking in a CNN interview, he said, “If he ordered me to reopen in a way that would endanger the public health of the people of my state, I wouldn’t do it.”, referring to Trump.
The governor is looking for a phased reopening which may take months to complete.
The lighter colours in the visualisation above show forecasts that were made in mid-April using data available then. Unfortunately, New York’s recovery has not been as smooth as predicted here. A fresh spike of cases on April 25 has cast uncertainty on its future. This proves the difficulty of predicting the number of cases by fitting a simple model due to the numerous complexities involved in the spread of viruses.
Analysing the timeline of cases, as we go from each day to the next, the number of infections is multiplied by some constant. The spread of viruses is a textbook example of exponential growth because what causes the new cases are the existing ones. This is why we have put the y-axis on a logarithmic scale—each step of a fixed distance corresponds to multiplying by a certain factor. On this scale, exponential growth should look like a straight line. This straight line does not go on forever. It has to start slowing down at some point. The key question is when.
Owing to rigorous social distancing, it looks like New York has passed the peak, and the line of cumulative cases is slowly flattening. Now, governments worldwide are mulling over when to reopen their economy. Too early, and we could see another spike in infections. Too late, and the impact on the economy may be irreparable.
3. No Job – The New Normal
The impact of the pandemic on employment in unmistakable. As with recessions of the past, job losses were expected. What differentiates this downturn from any other is the enormity of these job losses. Instead of a gradual decline in economic activity as seen in business cycle depressions, business operations have ground to a halt, creating shockwaves in the national and global economy.
The current economic situation has been dubbed The Great Lockdown. A shutdown so fast and job losses so many have never been experienced before.
The above visualisation looks at the worst US job losses on record. These are measured over a four-week period. To account for population growth, the number of jobless claims as a percentage of the US population is also shown.
In case the true scale of this crisis not been emphasised enough, the number of job losses is about ten times higher than the average number of job losses in recessions since 1975. The number stands at a staggering 22.03 million, which is almost equal to the populations of middle powers such as Taiwan and Australia.
The recovery of jobs from the last recession was very slow. It took roughly ten years for the US economy to return to an unemployment rate similar to pre-recession levels.
Like other recessions, The Great Recession took many months to culminate. The current crisis is different in that businesses have been suddenly forced to pause operations. One can hope that businesses are able to stay afloat during this shutdown and rehire workers once normalcy resumes.
Besides temporary shocks, the pandemic will result in structural changes in the global economy. In the microeconomic context, it will expedite the adoption of technologies like e-learning and e-commerce.
Telecommuting will be normalised, and more firms will provide the option to work from home. Politically, this pandemic will test the effectiveness of various institutions and it could determine upcoming elections.
4. World Economy at Risk
The Organisation for Economic Co-operation and Development (OECD) published an interim economic assessment in March 2020.
Importantly, it has revised its growth projections from November last year. In most countries, the growth adjustment is negative for 2020 but positive for 2021.
Mature economies like the US will take a slight hit in 2020 but will recoup their losses in the following year. Emerging economies on the other hand like India will be badly hit economically.
India has negative GDP revisions in both 2020 and 2021 and, as such, its recovery is likely to be slow. China, being the earliest to recover from the pandemic, will have the greatest jump in growth in 2021 at 0.9 percentage points.
Argentina’s economy was already shrinking and shocks from this pandemic will not do it any good. It is clear from this economic outlook that the timing of economic effects will vary across countries.
The GDP growth forecasts have been adjusted because the world economy is being buffeted by both demand and supply-side shocks.
Authors Philipp Carlsson-Szlezak, Martin Reeves and Paul Swartz (2020) summarised three main shocks in an article for Harvard Business Review (HBR).
The first demand shock is an indirect hit to consumer confidence. Turmoil in financial markets has lowered household wealth.
Macroeconomics fundamentals tell us that this must result in higher household savings and less consumption. Advanced economies are more predisposed to this as their household exposure to the equity asset class is high.
Secondly, there will be a direct hit to consumer confidence. As consumers are forced to isolate themselves, they may reduce their discretionary spending and be less optimistic about the future.
Lastly, a supply-side shock results as the pandemic causes production to cease and disrupts key components of supply chains. This would lead to greater unemployment, but the effects would differ across industries. The crisis may not last long enough for this shock to be significant.
While the above data is useful, the authors of the HBR article warn against becoming too dependent on projections. Instead, leaders should look past the crisis, scanning for opportunities and challenges, and considering how they would address the post-crisis world.
What coronavirus could mean for the global economy. (2020, March 3). Harvard Business Review. Retrieved from https://hbr.org/2020/03/what-coronavirus-could-mean-for-the-global-economy
An international research team led by the University of Hong Kong (HKU) developed a new method to accurately track the spread of COVID-19 using population flow data and establishing a new risk assessment model to identify high-risk locales of COVID-19 at an early stage.
This new system serves as a valuable toolkit to public health experts and policymakers in implementing infectious disease control during new outbreaks. The study findings have been published in the journal Nature today (April 29).
The team used nation-wide data provided by a major national carrier in China to track population movement out of Wuhan between 1 January and 24 January 2020, a period covering the annual Chunyun mass migration before the Chinese Lunar New Year to a lockdown of the city to contain the virus.
The movement of over 11 million people travelling through Wuhan to 296 prefectures in 31 provinces and regions in China were tracked.
Differing from usual epidemiological models that rely on historical data or assumptions, the team used real-time data about actual movements focusing on aggregate population flow rather than individual tracking. The data include any mobile phone user who had spent at least 2 hours in Wuhan during the study period.
Locations were detected once users had their phones on. As only aggregate data was used and no individual data was used, there was no threat to consumer privacy.
Combining the population flow data with the number and location of COVID-19 confirmed cases up to 19 February 2020 in China, the team showed that the relative quantity of human movement from the disease epicentre, in this case, Wuhan, directly predicted the relative frequency and geographic distribution of the number of COVID-19 cases across China.
The researchers found that their model can explain 96% of the distribution and intensity of the spread of COVID-19 across China statistically.
The research team then used this empirical relationship to build a new risk detection toolkit. Leveraging on the population flow data, the researchers created an “expected growth pattern” based on the number of people arriving from the risk source, i.e. the disease epicentre.
The team thereby developed a new risk model by contrasting the expected growth of cases against the actual number of confirmed cases for each city in China, the difference being the “community transmission risk”.
If there are more reported cases than the model expected, there is a higher risk of community spread. If there are fewer reported cases than the model expected, it means that the city’s preventive measures are particularly effective, or it can indicate that further investigation by central authorities is needed to eliminate possible risks from inaccurate measurement.
What is innovative about the team’s approach is that they use misprediction to assess the level of community risk. Our model accurately tells us how many cases we should expect given travel data.
They contrast this against the confirmed cases using the logic that what cannot be explained by imported cases and primary transmissions should be community spread.
The approach is advantageous because it requires no assumptions or knowledge of how or why the virus spreads, is robust to data reporting inaccuracies, and only requires knowledge of the relative distribution of human movement. It can be used by policymakers in any nation with available data to make rapid and accurate risk assessments and to plan allocation of limited resources ahead of ongoing disease outbreaks.
The team’s research indicates that the geographic flow of people outperforms other measures such as population size, wealth or distance from the risk source to indicate the gravity of an outbreak.
As governments work around the clock to try and stop the spread of COVID-19, they are using all resources possible, turning more and more towards technology solutions to speed up their efforts in battling the spread of the virus. This includes large surveillance networks, mobile phone tracking, accessing and sharing health records, AI and facial recognition.
Tech Solutions to Beat Coronavirus raise Data Privacy Concerns
Although these efforts are being used for public health and safety, and it makes sense for Governments to use everything possible to fight this virus, it does raise concerns about data privacy.
Some of those tech solutions being implemented have a direct impact on people’s privacy. In certain cities, the entire population is under intense surveillance, while in some places the medical data of those infected with the virus is being shared between organisations and countries. It’s a fine line between using data for good and infringing on personal data rights.
Surveillance: external monitoring and personal data
Cameras or drones monitoring or ensuring people stay at home, tech solutions to screen crowds for people with elevated temperatures, facial recognition technology to track activity and movement are all ways governments are trying to curb the spread of coronavirus.
It is not just external sources that are being used for surveillance, governments are looking at citizens digital footprints to track their activity from their credit cards activity or tracking their movements from their smartphone data.
Governments all over the world looking to mobile data to help combat COVID-19
Singapore Government has launched a contact-tracing smartphone app last week to help identify those who have been exposed to the coronavirus and to aid contact tracing nationwide
BT, owner of UK mobile operator EE, is in talks with the UK government about using its phone location and usage data to monitor whether coronavirus limitation measures such as asking the public to stay at home are working.
Similar measures have already been carried out much further in South Korea, which has used apps to monitor the spread of the disease.
Israel also recently passed an emergency law which allows the government to track the spread of the virus using data from mobile phones.
Government Data Usage needs to be transparent-
Privacy and data protection laws cannot and should not get in the way of government strategy to saving lives. But even at times of crisis, data privacy should still be respected, and frameworks put in place for emergency situations like this and for also what happens once the crisis has been resolved. This should be clearly communicated to all citizens to maintain government transparency and trust, and good government-citizen relationships.
On 24 January 2020, the first confirmed case of COVID-19 in Singapore was reported, shortly after stocks of surgical masks were sold out throughout retail outlets across Singapore. In response, the Singapore government announced on 30 January 2020 that it would be giving each household four surgical masks, to be collected from specific distribution centres between 1-9 February 2020.
GovTech Agency quickly worked to help keep citizens informed and updated on the Government’s mask distribution
The Agency recognised the importance of getting timely and accurate information about the distribution centres to citizens, a team of developers at the Government Technology Agency of Singapore (GovTech) mobilised to develop MaskGoWhere – a website providing information on mask collection points.
The MaskGoWhere website allowed citizens to key in their postal code to learn about their respective mask collection points. Led by Mr Lim Eyung, director of Government Digital Services at GovTech, the team built and designed the first version of the site in less than 12 hours.
When the government announced that they would distribute facemasks to every household in Singapore on 30 January, GovTech was already working on an information resource website about mask collection points.
Some of the challenges they faced included mapping postal codes to distribution centres and making sure information was accurate and finding a single reliable reference source for details of collection points.
The GovTech team collaborated with the People’s Association and the Public Service Division to find a solution. Since each constituency had its own posters created by the People’s Association outlining the latest mask collection points, the GovTech team could launch MaskGoWhere so that when users input their postal code, they would be redirected to an image of a poster with all the relevant information for their constituency.
At the same time, cybersecurity specialists at GovTech began preparations to ensure that MaskGoWhere would be secure when it went live.
MaskGoWhere functional within 48 hours
The first version of MaskGoWhere was up and running although not yet live by 30 January. Cybersecurity specialists at GovTech were also carrying out penetration testing. Tests which had to be completed quickly before user volume on MaskGoWhere was expected to spike.
“Typically, penetrating testing would require at least two weeks to complete. We finished the process within a few hours,” Mr Thomas Lim explained, adding that a separate cybersecurity team accomplished the same feat with securing the government chatbot AskJamie, which was also being updated to deal with queries about the latest information on COVID-19.
“Since the COVID-19 outbreak, we have seen an increase in engagements from project teams across the whole of government working on other COVID-19 related applications,” Mr Thomas Lim said. “We have since reprioritised our resources to support these engagements.”
Lessons learnt from MaskGoWhere Application
Mr Lim Eyung said that although the development of MaskGoWhere was not technically demanding, the tight time constraints meant that there were some learning points to be had from the experience.
For example, the team left out organisational logos, mastheads or footers when the site was first published, which quickly raised suspicions of it being a malicious site.
“It was a mistake on our part, but we managed to rectify it quickly,” he said. “The other point to highlight is the fact that everyone on the team was very mission-centric,” Mr Lim Eyung said. “In this case, we were very clear: the mission is to allow citizens to get hold of the latest information over a web-based medium.”
He also added that technology was not the only determinant of mission success. “Working closely with PA allowed for optimal OpsTech (operations-technology) integration, where we could use real-time feedback to learn about user needs, and the Agile approach to deliver continuous improvements.”
The project was completed within 48 hours, but the team continued to monitor, enhance and stabilise MaskGoWhere by keeping the operation going throughout the mask distribution period, which was extended until the end of February.
Mr Lim Eyung said he is already thinking about how the design and functions of MaskGoWhere could be applicable to other contexts, such as for national events or emergencies.
The EU recently released a communication on the future data strategy for Europe outlining a strategy for policy measures and investments to enable the data economy in Europe for the next five years. This strategy was launched at the same time as the Commission’s Communication on “Shaping Europe’s digital future”.
The data strategy paper outlines specific measures that can be taken to keep the EU at the forefront of the data-agile economy, but at the same time promoting the values of the EU.
“This Communication puts forward a European data strategy whose ambition is to enable the EU to become the most attractive, most secure and most dynamic data-agile economy in the world – empowering Europe with data to improve decisions and better the lives of all of its citizens. It enumerates a number of policy measures and investments needed to achieve this goal.”
In order for EU to acquire a leading role in the data economy, they have recognised the need to address issues ranging from connectivity to processing and storage of data, computing power and cybersecurity and to improve its governance structures for handling data and to increase its pools of quality data available for use and reuse.
The European data strategy aims to give businesses in the EU the opportunity to make the most of the Single Market. It will ensure that data can flow within the EU and across sectors, that laws and regulations are upheld with particular focus on personal data protection, consumer protection legislation and competition law. It also will ensure that rules for access to and use of data are fair, practical and clear and that there is an open approach to international data flows.
Four pillars of the Data Strategy
The EU strategy is built on four pillars: A cross-sectoral governance framework for data access and use; Enablers: Investments in data and strengthening Europe’s capabilities and infrastructures for hosting, processing and using data, interoperability; Competencies: Empowering individuals, investing in skills and in SMEs; and Common European data spaces in strategic sectors and domains of public interest.
A cross-sectoral governance framework for data access and use
The first priority is to put in place a legislative framework for the governance of common European data spaces by the end of 2020. This framework will support decisions on what data can be used in which situations, facilitate cross-border data use, and prioritise interoperability requirements and standards within and across sectors. The framework will reinforce the necessary structures in the Member States and at EU level to facilitate the use of data for innovative business ideas, both at sector-specific and from a cross-sector perspective.
Investments in data and Strengthening capabilities and infrastructures for hosting, processing and using data
There will be investments made into a High Impact project on European data spaces and data sharing architectures which include standards for data sharing, best practices, tools and governance structures. The first implementation phase is expected for 2022. Other steps planned are to sign a Memoranda of Understanding with the Member States on cloud federation by the third quarter 2020 as well as launching a European cloud services marketplace and create an EU regulatory cloud rulebook by the end of 2022.
Empowering individuals, investing in skills and in SMEs
The EU will encourage its citizens to take control of their data through tools to enable decisions about what is done with their data. They will invest in skills and general data literacy and by 2025, the EU and the Member States hope to half the current gap of 1 million digital specialists, with a focus on increasing the participation of women. The European SME strategy will set out steps to support SMEs and start-ups in which data plays a big role.
Create common European data sector spaces and domains of public interest
The Commission will promote the development of common European data spaces in strategic economic sectors and domains of public interest. These sectors are those where the use of data will have an impact on the entire ecosystem and its citizens. A framework will measure data flows and estimate their economic value within Europe, and between Europe and the rest of the world by the end of 2021.
EU to seize the opportunities the data economy presents
The report concludes that “A European way for handling data will ensure that more data becomes available for addressing societal challenges and for use in the economy while respecting and promoting our European shared values. In order to secure its digital future, the EU has to seize its window of opportunity in the data economy.”
The Philippines’ Department of Social Welfare and Development (DSWD) recently signed a Memorandum of Agreement (MOA) with the Bureau of Fisheries and Aquatic Resources (BFAR), which will share the results of the 2nd Nationwide Household Assessment or Listahanan 2 database.
About the Agreement
According to a recent press release, BFAR will use the database for the provision of programs and services to poor fisher folks.
BFAR will use the Listahanan database in assessing the names of fisher folks registered in its Municipal Fisher folk Registry System for program planning and prioritization, particularly for its national fisheries program.
Listahanan is also known as the National Household Targeting System (NHTS) for Poverty Reduction (NHTS-PR).
It is an information management system that identifies who and where the poor are nationwide.
It provides the national government agencies (NGAs), local government units (LGUs), non-government organisations (NGOs), and other stakeholders, a database of poor households as basis in identifying potential beneficiaries of social protection programs and services.
DSWD uses the database in targeting beneficiaries for its various programs and services, such as the Pantawid Pamilyang Pilipino Program, Social Pension for Indigent Senior Citizens, and Unconditional Cash Transfer Program.
Apart from BFAR, several government agencies also use the Listahan 2 database. These are:
- The Department of Agrarian Reform (DAR),
- The National Food Authority (NFA)
- The Philippine Health Insurance Corporation (PhilHealth)
- The Philippine Carabao Center (PCC)
- The Philippine Institute for Development Studies (PIDS)\
- The Department of Finance (DOF)
In addition, it is also being used to identify the grantees of the Tertiary Education Subsidy (TES) under the Unified Student Financial Assistance System for Tertiary Education (UniFAST).
Conducted in 2015, Listahanan 2 was able to identify 5,251,194 households, out of 15,485,429 households assessed, to be poor.
DSWD is currently conducting the 3rd Household Assessment or Listahanan 3 with a target of 16.1 million households nationwide.
Listahanan is led by the DSWD through the NHTO and its Field Offices.
Significance of Databases
Databases are essential for easier tracking and storage of pertinent information, which can be accessed by those who need it.
The Philippines’ Bureau of Internal Revenue, for instance, is working with the Department of Labor and Employment (DOLE) in developing an inter-agency database of foreign nationals working in the country.
This move will effectively monitor the foreign nationals and guarantee that they pay the correct amount of taxes to the Philippine government.
Meanwhile, the Vietnam government aimed to create a national database system as well as launch a national public service portal, which connects with local portals to monitor and enhance public service delivery.
Sharing data is very important as it enables the formation of close links between the central Government and local governments.
Another significant database initiative is that of Indonesia’s. The Indonesian Government is determined to protect its domestic mobile phone, computer and tablet industry, including the users.
Three ministerial regulations were made to address this and one of them is the Minister of Industry Regulation concerning the Mobile Telecommunications Equipment Identity Database System.
More than 1.4 billion IMEI data have already been entered into the database.
A data-sharing agreement was recently signed by the Department of Trade and Industry (DTI) with TransUnion Philippines.
As reported, DTI will provide Publicly Available Data of business enterprises to the company, which is the country’s largest credit bureau, through the agreement.
About the Agreement
The data-sharing agreement will allow the company to develop a “firm bureau database” in order to expand distribution of its credit information not only to individuals but to firms as well.
This criterion is measured by the World Bank (WB) in their Doing Business (DB) Report under its “depth of credit information index.”
The signing of the data-sharing agreement is part of the Philippine government’s continuing effort to improve the country’s competitiveness ranking.
This partnership signals greater collaboration between the government and the private sector in using data analytics to help in policymaking and program development.
DTI Secretary Ramon Lopez explained that this initiative is hitting two birds with one stone.
Working closely with the company through information-sharing will secure additional points under the Getting Credit Indicator.
More importantly, the publicly available data will be processed together with the database so that they can be used to determine the borrowers’ creditworthiness.
In the end, this will contribute to increasing the bank’s efficiency in processing loan applications.
Following the data protest filed by the Philippine government in 2018, the WB accepted the data correction request of the Philippine government when it confirmed that the company is the largest credit bureau in the country with 8.47 million adult population in its database.
This is 13.5% of the total adult population in the Philippines, which exceeded the WB threshold by 5%.
The data correction request resulted in a 35-point increase in the Philippines’ score for Getting Credit and a significant +52 notches increase in the indicator ranking.
The company uses the information to empower its lending partners in giving financial access to more consumers.
In order to do this, the company continually strives to bring in more extensive credit information from various sources into their database.
The more diverse and extensive the data, the more efficient it will be for the business partners to make decisions on extending credit to both consumers and businesses.
The DB Report is an annual survey report released by the World Bank.
In the DB 2020 report, the Philippines reached the Top 100 competitive countries when it recorded a +26-notch increase.
It remains to be the highest recorded annual improvement of the country since 2010. From 124th place, the Philippines rose to 95th.
With the enactment of RA 11032, or the “Ease of Doing Business and Efficient Government Service Delivery Act,” the DTI has turned over to the Anti Red Tape Authority (ARTA) the reform initiatives for the succeeding cycles.
Usefulness of Data
The significance of data use and data analytics is gaining traction across different countries. Initiatives about this have been reported by OpenGov Asia in the past.
For instance, India’s data and analytics platform will democratise publicly available government data. The platform will host the latest data sets from government websites, present them coherently, and provide analytic tools.
The National Data and Analytics Platform (NDAP) will follow a user-centric approach and will enable data access in a simple and intuitive portal tailored to the needs of a variety of stakeholders.
Meanwhile, the Hong Kong government announced that four city dashboards will help the public visualise real-time dynamic Hong Kong data.
The dashboards present data on environment and weather, transport and traffic, public facilities and services and a city overview on interactive charts and maps.
Researchers from CSIRO’s Data61, data and digital specialist data sciences arm of Australia’s national science agency, are working with researchers from the Macquarie University, in collaboration with the University of Sydney and Nokia Bell Labs for FinalBlacklist.
FinalBlacklist is the largest global cybersecurity threat dataset to predict future attacks.
The comprehensive data of the global cybersecurity threat landscape, which spanned from 2007-2017, was developed to enable cybersecurity specialists to derive new insights and predict future malicious online activity (mal-activity).