Archives for category: Maps

Presentation on 2.0 at FOSS4G in Seoul, September 2015

Paper Abstract

The use of mobile devices for identifying risk and coordinating disaster response is well accepted and has been proven as a critical element in disaster risk management. As new tools, applications, and software are adopted by municipal governments and NGOs for the identification and management of urban risk, the need for greater integration of the various data they collect becomes acute. While the challenge of integrated data management is substantial, it is aided by the fact that many new tools have been developed to include an Application Programming Interface (API), which allows the machine-to-machine (i.e. automated) sharing of open data. While some proprietary platforms for the management of urban data are currently available, they are extremely costly and very limited in terms of data inputs; to date there are no open source geospatial software tools for the integrated management of various API sources to evaluate hazards for disaster response.

A key to improving disaster risk management as an element of risk identification is the development of an integrated open source Decision-Support Risk Evaluation Matrix that enables: 1) automated integration of multiple geospatial and non-geosapatial API sources into a low cost, user-oriented dashboard; 2) backend database and software design for the Risk Evaluation Matrix that enables data sources to be parameterized and interrogated; 3) the development of an output API stream that allows additional secondary applications to optimize their evaluations and analyses through open access to critical risk information. To address these challenges this paper presents an open source Risk Evaluation Matrix, currently in development, which aims to provide situational oversight of flood hazards from multiple data-sources, including social media, in the city of Jakarta, Indonesia.

…the symposium brings together leading scholars, researchers, critics, and practitioners for a series of discussions about the consequences of big data, data-driven design, and their latent potentials for design, planning, and activism.

As forays into big data analytics support increasingly innovative design strategies, and as new theoretical approaches and policy frameworks shape the future of urban data politics, the symposium asks how, why, and for whom: Data Made Me Do It.

Accountabilities Panel Presentation
Hacking Twitter to build evidence based flood response in Jakarta

Slides from the Free and Open Source Software for Geospatial Conference, Portland September 2014.

Mapping urban infrastructure systems is a key requirement to advance our capacity to understand and promote the resilience of cities to both extreme weather events as a result of climate change and to long-term infrastructure transformation as a process of climate adaptation. Yet, while developing nations will bear the brunt of the interwoven, climatic, economic and social challenges of the 21st century, many of these countries lack the sensor networks required to monitor and model the response of the urban system to change.

The nexus of people and place embedded in social media communication which is widespread and ubiquitous in many developing nations offers one potential solution. In this context, location-based social media often in the form of big-data, can be used to map emerging spatio-temporal trends to support situational management. Critically, however, the collection and application of such data raises significant questions around privacy, trust and security of the information gathered. The project will be presented as a demonstration of the capabilities of free and open source geospatial technology to employ real-time social media data in a secure and anonymous manner for the purpose of decision support.

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November 2014 marks the first year of work on the PetaJakarta project. As we prepare to “go live” in December, the Year 1 Research Highlights gives a snapshot of our progress to date.

PetaJakarta Year 1 Report Cover

Project Abstract

PetaJakarta is a crowd-sourcing data-collection initiative which aims to advance our capacity to understand and promote resilience of cities to both extreme weather events as a result of climate change and to long-term infrastructure transformation as a process of climate adaptation. Developing new ways of capturing information about megacities during extreme events will be critical to understand how the urban environment, informal settlements, and infrastructure will response to the challenges of a changing climate, flooding and sea level rise. This is particularly prevalent to South-East Asian mega-cities which will bear the brunt of much of this change. PetaJakarta is our proof of concept GeoSocial Intelligence Platform, which will harness the power of social media to gather, sort and display information about flooding for Jakarta residents and governmental agencies in real time.

Half the world's population live within the Asian circle

More half the world’s population live within the Asian circle

Download report (.pdf) | Visit for more

Previously, in this blog post, I discussed the ways in which we’re tackling the infrastructure challenges in developing nations using open data. Below are the slides I presented at the first International Symposium for Next Generation Infrastructure. The work presented is a proof-of-concept model using data from Map Kibera to optimise a road-based sewage network. The great thing about using this data is that for the first time we can glean an insight into infrastructure provision in informal urban settlements, and examine methods to improve it.

As part of my PhD I had to produce a land cover map for the Greater London area. I derived a simple land cover classification using the UKMap Basemap (which I previously used to generate the 3D London map). Click on the image below to see a larger version (1.8Mb at 300dpi).

London land cover map

Land cover in the Greater London area

I created the layers using PostGIS tables for each land cover type, based on the Basemap’s Feature Type Code (FTC), which classifies land use based on the National Land Use Database. Using separate tables also significantly improved rendering performance in Quantum GIS (QGIS), which I used for the cartography. I was impressed by QGIS’ ability to process and render such a detailed data-set (the Basemap contains ~11 million polygons for London).