Archives for posts with tag: GIS

The Open Data Institute in London has awarded the PetaJakarta.org project, through the SMART Open Source Geospatial Laboratory, a grant to showcase the project’s use of open data and software.

Read the announcement here: http://theodi.org/news/the-odi-announces-winning-odi-showcase-projects-out-for-the-count-and-petajakartaorg.

Link to Q&A interview about the award below.

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Presentation on PetaJakarta.org 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.

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

MapJakarta.org 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.

[view complete abstract]

I recently published a short essay in the IEEE Technology & Society Magazine about the opportunities and research challenges that social media present as a data source for understanding complex urban systems in informal settlements.  The post below is a synopsis of the article posted on IdeaPod.

Social media, driven by the explosive uptake in mobile computing, has caused a systematic shift in personal communications on a global scale. From the Arab Spring to the Occupy Movement it is apparent that social media is becoming an integrated part of our global communication infrastructure. Critically, much of this information is underpinned by geographical content such as mobile GPS coordinates, which enable the user to tie their media to a specific location on the Earth’s surface. In this new paradigm, social media are effectively forming a human-powered sensor network.

PetaJakarta_FloodReport 2

As world populations continue to grow, and we face the social, climatic and economic challenges of the 21st century, how can we leverage the potential of this new global network of intelligence sensors? How can we use this data to inform us about the urban system and adapt to global change?

Article originally published in IEEE Technology & Society Magazine Spring 2014: http://ro.uow.edu.au/smartpapers/119/

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.

I recently discovered the GeoAlchemy2 project – a replacement for the original GeoAlchemy package, focused on providing PostGIS support for SQLAlchemy. The SQLAlchemy package is a “Python SQL Toolkit and Object Relational Mapper”. In a nutshell this means you can write Python classes and map them to PostgreSQL tables without the need to write SQL statements – pretty cool!

PostGIS is great for doing spatial stuff, but if you’re using it as back-end for a Python app then you can spend a lot of time writing Python wrappers around SQL statements, and even with the excellent Psycopg2 package this can be tricky. This is especially true if you’re using the OGR Python bindings to handle PostGIS read/writes.

Enter GeoAlchmey2. I’ve been experimenting with it for a week, in that time I’ve learnt this:

For developing geospatial Python apps with PostGIS, GeoAlchemy2 is nothing short of revolutionary.

You can call PostGIS functions in Python, which means you can use them (and the data) directly within your Python application logic. Here’s an example. The SQL statement below uses PostGIS to create a new line geometry between a point, and the closest point on the nearest line.

Now here’s a snippet from a Python script, performing the same process using GeoAlchemy2.

You’ll notice here that we’re actually calling our own Python function “make_link_line” during the query to create the new geometry. This exemplifies how we can move PostGIS objects around inside the script. Once the query runs we can access the returned data in our application from the row variable. Below is the complete script.

Nearest neighbour PostGIS and GeoAlchemy2 script:

The script above is just a simple example, but it shows how powerful GeoAlchemy2 is for embedding PostGIS objects and methods inside Python. I’m really looking forward to digging deeper into the functionality of GeoAlchemy2 and SQLAlchemy to integrate them within my own projects. Check out the official tutorials for more examples: https://geoalchemy-2.readthedocs.org/en/latest/#tutorials

Following in the footsteps of the Raster Processing Suite, I’ve added GitHub pages for the PostGISDroid script which I wrote last year. This was a prototype Python script, built on the Android scripting layer, to log the location of an Android device to a remote PostGIS server. Check it out here: http://talltom.github.com/PostGISDroid.

PostGISDroid track in QGIS
A track from PostGISDroid in Quantum GIS.

If you’re looking for a full-blown Android client for mobile data acquisition check out the Sense Cloud Framework: http://ceg-sense.ncl.ac.uk/geoanorak/nclsensecloud.html.