Last week our latest paper was published entitled ‘Assessing urban strategies for reducing the impacts of extreme weather on infrastructure networks’ in the Royal Socities Open Science journal. Alistair Ford, Craig Robson and Stuart Barr all contributed to the artical alongside colleagues from civil engineering, Maria Pregnolato (lead authour), Vassilis Glenis and Richard Dawson. A summary is given below.
“A framework for assessing the disruption from flood events to transport systems is presented that couples a high-resolution urban flood model with transport modelling and network analytics to assess the impacts of extreme rainfall events, and to quantify the resilience value of different adaptation options. A case study in Newcastle upon Tyne in the UK is presented and shows that both green roof infrastructure and traditional engineering interventions such as culverts or flood walls can reduce transport disruption from flooding.The magnitude of these benefits depends on the flood event and adaptation strategy, but for the scenarios considered here 3–22% improvements in city-wide travel times are achieved.
Both options should form part of an urban flood risk management strategy, but this method can be used to optimize investment and target limited resources at critical locations, enabling green infrastructure strategies to be gradually implemented over the longer term to provide city-wide benefits. This framework provides a means of prioritizing limited financial resources to improve resilience. By capturing the value to the transport network from flood management interventions, it is possible to create new business models that provide benefits to, and enhance the resilience of, both transport and flood risk management infrastructures.”
The volume of a teams away following is often used as measure for the “size” of a club and is heavily debated amongst football fans. But their are many factors that influence this, not least their proximity to other clubs.
With the Football league last week releasing statistics showing the average away attendance for each team playing in the championship (shown below), I looked at whether this was effected by the clubs accessibility to other clubs and how long fans were spending travelling.
With Leeds being located in within fairly close proximity to a number of the northern teams, and Leeds railway station being well connected I wanted to look at the whether this contributed to their loyal away following.
So I used the Google Travel API to look at the total time a football fan would spend travelling if they went on public transport to every away game. The results are below.
Time Spent Travelling
6 Days 3 Hours 44 Minutes 14 Seconds
Brighton and Hove Albion
6 Days 1 Hours 23 Minutes 36 Seconds
5 Days 23 Hours 17 Minutes 34 Seconds
5 Days 21 Hours 25 Minutes 26 Seconds
5 Days 11 Hours 18 Minutes 10 Seconds
5 Days 10 Hours 22 Minutes 50 Seconds
5 Days 2 Hours 57 Minutes 20 Seconds
4 Days 22 Hours 6 Minutes 38 Seconds
4 Days 20 Hours 5 Minutes 32 Seconds
4 Days 19 Hours 58 Minutes 32 Seconds
Preston North End
4 Days 19 Hours 52 Minutes 22 Seconds
Queens Park Rangers
4 Days 18 Hours 44 Minutes 38 Seconds
4 Days 18 Hours 13 Minutes 12 Seconds
4 Days 17 Hours 38 Minutes 16 Seconds
4 Days 11 Hours 55 Minutes 58 Seconds
4 Days 11 Hours 17 Minutes 36 Seconds
4 Days 7 Hours 2 Minutes 12 Seconds
4 Days 5 Hours 1 Minutes 4 Seconds
4 Days 4 Hours 30 Minutes 54 Seconds
4 Days 0 Hours 30 Minutes 48 Seconds
3 Days 23 Hours 23 Minutes 6 Seconds
3 Days 19 Hours 59 Minutes 4 Seconds
3 Days 18 Hours 12 Minutes 6 Seconds
3 Days 17 Hours 41 Minutes 6 Seconds
From this it appears that there is very little correlation between the average away attendance and time spent travelling especially with Middlesbrough’s travel time and attendance being so high.
As a final thought I looked at using the travel times for ranking the teams be the number of “fan hours” spent travelling.
40 Years 31 Weeks 3 Days 1 Hours 4 Minutes 50 Seconds
33 Years 0 Weeks 5 Days 23 Hours 55 Minutes 24 Seconds
28 Years 41 Weeks 5 Days 4 Hours 39 Minutes 0 Seconds
Brighton and Hove Albion
27 Years 27 Weeks 0 Days 6 Hours 8 Minutes 48 Seconds
25 Years 16 Weeks 6 Days 20 Hours 14 Minutes 12 Seconds
22 Years 32 Weeks 3 Days 5 Hours 54 Minutes 42 Seconds
21 Years 39 Weeks 2 Days 20 Hours 37 Minutes 30 Seconds
Preston North End
19 Years 48 Weeks 0 Days 0 Hours 24 Minutes 12 Seconds
19 Years 28 Weeks 5 Days 13 Hours 5 Minutes 20 Seconds
18 Years 41 Weeks 4 Days 6 Hours 38 Minutes 30 Seconds
18 Years 35 Weeks 3 Days 8 Hours 19 Minutes 36 Seconds
16 Years 10 Weeks 1 Days 10 Hours 36 Minutes 0 Seconds
Queens Park Rangers
15 Years 40 Weeks 1 Days 7 Hours 38 Minutes 32 Seconds
14 Years 44 Weeks 5 Days 9 Hours 0 Minutes 18 Seconds
14 Years 18 Weeks 0 Days 19 Hours 22 Minutes 56 Seconds
13 Years 51 Weeks 4 Days 14 Hours 3 Minutes 36 Seconds
13 Years 46 Weeks 5 Days 20 Hours 10 Minutes 24 Seconds
13 Years 40 Weeks 0 Days 14 Hours 34 Minutes 40 Seconds
13 Years 16 Weeks 5 Days 5 Hours 13 Minutes 44 Seconds
12 Years 32 Weeks 0 Days 11 Hours 26 Minutes 32 Seconds
11 Years 17 Weeks 3 Days 7 Hours 47 Minutes 6 Seconds
11 Years 8 Weeks 1 Days 16 Hours 13 Minutes 36 Seconds
9 Years 28 Weeks 3 Days 23 Hours 52 Minutes 10 Seconds
7 Years 17 Weeks 2 Days 23 Hours 18 Minutes 24 Seconds
Following on from an early email exchange about how Google defined “Newcastle” it got me thinking about other providers and how they define the city. Twitter for instance provides a bounding box the centre of which is the middle of Nun’s Moor (as does Google but the point location is not the centroid of it). As you can see from the image there is wide variation. Note that a couple (Yahoo/Michelin) place the point location at the point roughly where the centre of the text “Newcastle upon Tyne” appears on certain map products.
The mean centre of the point locations excluding the centroids is shown as well.
So I visited a pile of map sites and extracted their default location (and/or bounding box) and plotted them on the map. For twitter I sent a tweet from “Newcastle upon Tyne” and captured the coordinates using the API. Just to be rigorous I installed TOR browser which obfuscates the IP address to ensure that location was not being picked up from the browser and tried different browsers as well. The OS point for Newcastle upon Tyne was collected from the Gazetteer service provided through Digimap.
The vaguely serious point here is that we now routinely use APIs to get data from and its location yet those locations provide spurious precision!
Following our blog post looking at the distances of football grounds from their nearest railway station, we had a think about other interesting football-related analysis that we could do. Having previously been a holder of an away season ticket, I thought that one interesting comparison might be to investigate which football fans would spend the longest on public transport to visit away games.
In order to do this analysis we used the Google Maps Distance Matrix API (https://developers.google.com/maps/documentation/distance-matrix/ ) to calculate the time and distance you would have to travel on public transport between each football club, to arrive at 2pm on a Saturday. The locations of the football grounds were used as the start and end points for each journey since we obviously don’t know where supporters actually live!
Below is a list of the 5 longest away trips between any pair of Football League or Premier League clubs. You could look at this as the nightmare FA Cup draw if you’re committed to going to the game, and the ones you really don’t want to be switched to a Friday night for TV coverage.
6 hours 39 mins
6 hours 29 mins
6 hours 22 mins
6 hours 13 mins
6 hours 13 mins
The 5 quickest are below – some of these would likely be quicker to walk!
West Bromwich Albion
In summary, you really don’t want to draw Plymouth away…
As I imagine happens at many work places the coffee break for the Geospatial group often brings up some lively debates. Whilst today’s debate itself wasn’t exactly lively it did pose an interesting question. Which football ground is the further away from a mainline railway station? The answer put out there Hillsborough. Not content with this guesswork I ran a simple piece of analysis. Luckily we have the football grounds from the work we carries out with the football tweets (previous blog & site) and the the railway stations from work carried out for ITRC (http://www.itrc.org.uk/).
Therefore all i had to do was look at the nearest railway station to each ground and calculate the distance between them. I did this by loading both datasets into PostgreSQL + PostGIS and performing a few simple queries. The results are below: Whilst Hillsborough wasn’t the furthest it wasn’t far off.
Ideally we’d look at the distance along the UK road network and not straight line distance. But this seems somewhat overkill for answering this question.
Last week a book, ‘The Future of National Infrastructure: A Systems-of-Systems Approach’, by reasearchers from the ITRC (Infrastructure Transitions Research Consortium), was released and made available for purchase. The book provides insights into a range of the work undertaken in the ITRC project, from the economic and demographic projections to 2100 for the UK, to the analysis performed with developed national scale models for critical infrastructure systems and the developed underlying database and visualisation tools used. Synopsis:
“The future of national infrastructure: A system-of-systems approach provides practitioners, decision-makers, and academics with the concepts, models and tools needed to identify and test robust, sustainable, and resilient strategies for the provision of national-scale infrastructure. It takes a “system-of-systems” view on the interconnected infrastructure networks – including transport, telecommunications, energy, water, and waste-management – and derives an integrated vision on infrastructure provision required to ensure that nations have an infrastructure system that is fit for the future.”
Our own Stuart Barr, David Alderson and Craig Robson have all been involved in the research behind the book which has been carried out over the past five years, with a single chapter devoted to the work where their time has been focused, though they have also contributed to much of the other work. The chapter, ‘Database, simulation modelling and visualisation for national infrastructure assessment’, documents the tools developed here at Newcastle; the underlying database for the infrastructure models including developed schema’s as well as the visualisation and reporting tools for both the data used for the modelling and for the results from the simulation work and subsequent analysis. This research has involved the development of a national infrastructure database containing a suite of data for many of the critical infrastructures in the UK along with the associated data such as economic and demographic modelling outputs for demand modelling as well as hazard data for modelling the resilience of the infrastructure networks/systems. Along with this, a schema and associated functions for the simulation and modelling of national infrastructures has been developed as well as a PostgreSQL/postgis schema for networks and the wrappers for integration into the python package NetworkX. On top of these, a suite of web based visualisation tools have been developed with facilities to view and interrogate the results from the infrastructure modelling and simulation, tools to view the underlying demographic data, one of the main drivers for the modelling. More details of these outputs are available in the book, and further publications in the form of journal articles are in preparation.
As part of Mustafa Hameed’s PhD research, he spent some time doing fieldwork in communities in his native Iraq. The research is assessing the role of VGI (volunteered geographic information) input in re-building the Iraqi cadastral system after years of chaos. The work done in the town of Al-Hilla (south of Baghdad) during December 2015 and January 2016 concentrated on several aspects: firstly, interviews with land administration professionals yielded an assessment of the current land administration system and opinions regarding the role of VGI; secondly, sample attribute data pertaining to land parcels was collected from professionals, gatekeepers of selected communities, and some owners of parcels in those communities; finally, geometrical data was collected by the owner of parcels who identified their parcel boundaries using three different techniques (GPS app on a smartphone, iPad tablet, and paper-printed satellite image). It was pleasing to see such a large number of local people willing to volunteer their knowledge, time and services to this project.
Above: Using printed satellite image to identify parcel boundaries
Above: Using an iPad with uploaded and georeferenced cadastral maps to identify and edit parcels
Above: Using smartphones to find GPS coordinates on parcel corners
As a research group a small number of us have been involved in the ITRC project over the past five years, namely Stuart Barr and Dave Alderson. Craig Robson joined the ITRC ranks in January, pausing his PhD work to help complete the final phase of the research required by autumn 2015. ITRC (Infrastructure Transitions Research Consortium) has been investigating the future of the UK’s national scale infrastructure with regards to how it must develop to meet ever changing demands and how climate change amoungst other factors will affect the resilience of those networks we rely on.
Our role in the project has centered around the development of the tools which would allow the five year project to be completed and included, but was not limited to, the development of the central database for all data for the project, and the support tools which would enable the analysis to be undertaken and results reported. It is on this later point where most of the past 6 months have been spent; developing a reporting tool for the presentation of the results from the long-term infrastructure planning aspect of the project.
The developed reporting tool allows users to view results from the each infrastructure sector (e.g. transport or waster supply), or view cross sector results, a set of similar metrics computed for each sector allowing for direct comparisons between them on there performance. For each sector a range of model outputs can be viewed from the level of emissions produced, to the running costs per year to the cumulative capital investment required. Results are shown not only at the regional level, but where possible at the sub-national level through the government office regions for example where the models output data at this granularity. This allows the tool to show data not just in charts, but also through maps, allowing new insights to be learned which may not be identified through non-spatial results. More detail on the tool, along with images and the like will be provided in a specific post at a later date, but for now a small selection of images below exemplify the tool.
With the end of the project looming near an event was organised at the ICE in London on the 15th October where the key results and impacts from the project could be disseminated to a wider audience with those key members behind the research all being present to answer questions and discuss their work. At the event we were available to demo the reporting tool and discuss the complexities behind the database for those interested, while a set of slides were used to give a overview of our work. More generally two videos (below) were produced giving an overview of the project and the one on the resilience of the UK’s national infrastructure.
Anyone with any experience of working/visiting the Geomatics group will know that, just like in most other academic establishments, visits to the pub at the end of a hard week are just as important as anything else that happens during the working week. To this end, the Friday night pub call is now well and truly part of the culture of the group, and without some would be inclined to say they would feel lost without this. However, the visit to the pub could be argued to have fallen into the shadow of the call email itself, which depending on the sender, can tell a complex story which few understand and can only read with a face of utter bewilderment.
So, to cut to the chase, as an exploratory piece of ‘academic research’ we have compiled a comprehensive database, going back to the creation of the GWRS call email list, 2008, until the current day. Every pub call ever made over the past 7 years, that’s 397 Fridays, the pub and the person who made the call have been recorded (though some are blank due to public holidays and missing data). Further data has then been added including the weather to allow us to explore the call data in more detail, and attempt to understand any patterns which may or may not be present.
To start with, and to end this initial post here, below are some key initial statistics on the data we have collected.
An amazing summer school took place last week in Obergurgl, Austria (http://www.uibk.ac.at/geographie/summerschool/). The focus was to investigate the various close range photogrammetry remote sensing techniques for alpine terrain research. Such an international environment made the atmosphere very interesting. I met other researchers from Austria, Germany, Italy, Poland and even Equador!!! Professors in remote sensing and photogrammetry from Germany and Italy insipred us with their talks.
The days were very busy with data collection in the alps and data processing, however the fieldwork gave us the opportunity to explore the beautiful mountains and refresh our minds. The best part of course was the social events with tasty austrian beers. The weather was incredibly warm, even the locals became crazy in such a warm environment; I couldn’t believe that Austrian drivers use the horn, this is a Greek habit!
The local newspaper reported the event on their website: http://www.tt.com/panorama/katastrophe/10251350-91/katastrophen-besser-lesen-lernen.csp
The organiser Dr. Martin Rutzinger successfully managed to create a great event and make everybody happy.
The 2nd attached picture shows how engineering is combined with nature. 🙂