Is Spatial Big Data Any Special?
The role of Spatial Big Data in managing the multi-layered issues in Indonesia, including Disaster Risk Reduction
What is spatial big data and why is it important?
Spatial big data is a group of data in huge amounts which have these complex characteristics known as 4V: volume, velocity, variety, and veracity. The complexity shows that it needs special skills to analyse those data, and it’s one of the nine pillars of technological advancement in the foreseeable future. Spatial big data is helpful for disaster risk management, sustainable development, and spatial data management purposes. The types of analysis we can do with spatial big data summarising or inferring, analysing patterns, finding locations, using predictions and proximity, and data management.
According to the Indonesian Ministry of Research and Development, Indonesian spatial data has a huge dimension, and we have approximately 40 trillion GB worth of spatial data in 2020. This aligns with the increasing amount of Internet users, both nationally and globally. Three sectors that use big data the most are agriculture, housing/real estate, and industrial.
Spatial big data comes from various resources, such as location, 4D surveys, satellite imagery, social networks, and the internet of things (IoT). Spatial big data is an important key for the development of Smart Cities because it relies heavily on connectivity and networks. In addition, it’s also wiser to use big data because it considers various criteria and parameters in the development, such as drinking water supply and waste management for example. That way it’ll make analysing more efficient.
Data Management for the development in Indonesia
Indonesian Head of Spatial Information Agency once stated that data is an asset of the Nation. It’s no wonder that Indonesia takes up a large amount of geospatial information (GI) data, considering our geographical condition, as it is very important for the purposes of mapping the land and the seas. GI mapping is divided into Basic GI and Thematic GI. Both mappings are needed for the integration of information between fields. Big data also has a role in Indonesia’s One Map policy; a platform to combine thematic maps with other data such as statistics. In the use of big data for development, it should be used wisely alongside prioritising the sustainable development goals (SDGs).
The government finally launched “Satu Data”, a platform that aimed to store big data that is open for the public as well as for data management policy to produce information that is accurate, up-to-date, integrated, and accountable, as well as easy to access and share; combines statistical data, spatial data, and state financial data at the central level. Big data in regional development is used to make precise analysis of the direction and location of the development, as a reference for controlling space utilisation, as well as obtaining a quick and massive response to the design of development plans with a participatory process in urban development, accelerating welfare on target, inter-regional input-output (IRIO) economic analysis, goods and services, and regional transportation. Participatory mapping is an example of the use of big data in regional development. The challenges in using big data include the lack of availability of base maps, privacy, and data access, data analysis capabilities and methodologies, infrastructure support in the form of storage, systems, and minimal standards, the availability of human resources, and the application of a single identification number for all levels of society Indonesia.
Spatial Big Data for Disaster Risk Reduction
In the last ten years, there has been an increase in the incidence of disasters in Indonesia. Hydrometeorological disasters (floods, landslides, extreme weather, flash floods) are the dominant disasters that account for 95% of disaster events in Indonesia. There are seven global targets and four priorities for disaster that were presented in the Sendai Framework for DRR 2015–2030. The priorities are: understanding disaster risk, strengthening disaster risk governance and disaster risk management, investing in disaster risk reduction for resilience, and improving disaster preparedness for effective response, as well as for better recovery, rehabilitation and reconstruction. The targets include:
- reducing damage to infrastructure,
- reducing the number of losses due to disasters,
- reducing the number of people affected by disasters,
- reducing deaths from disasters,
- increasing the availability of information and early warning systems,
- increasing international cooperation, and
- improving local and national DRR strategies.
Summary
During the time that I attended a related seminar about spatial big data, it was before the pandemic and I wouldn’t know for sure that this will be widely used in between stakeholders and not limited to natural disaster risk management, but also for other kinds of disasters (e.g. pandemic) as well as healthcare purposes. Tracking COVID cases is easier for everyone when spatial distribution maps are available. Researchers can get access to open data from institutions for modelling, prediction, and analysis. The government can track people on each scale, from local districts to national, who have been exposed by COVID. People can track themselves and see people around them.
The challenges in using big data include the lack of availability of base maps, privacy, and data access, data analysis capabilities and methodologies, infrastructure support in the form of storage, systems, and minimal standards, the availability of human resources, and the application of a single identification number for all levels of society Indonesia. On general aspects, there might need to provide more massive collaboration opportunities in increasing the effectiveness of the management, so that the use of GI technology must be carried out with careful planning supported by research from experts and academics. Strengthening the knowledge management system and the capacity building of human resources and communication between actors is very much needed. Even though some of the facilities and platforms are not yet well-established, it gives hope for future spatial data utilisation in our daily lives.
