Digital plan data – what’s the state in Europe?

We just started a new ESPON project on the state, use and perspectives of digital plan data and digital planning. We will work with 6 in-depth cases, including Denmark, Norway and Switzerland. Prior to that, we will collect material from 15 European countries to provide a first overview on the state of digital plan data. Looking forward to the work! The project is led by the Spatial Change and Planning Group at University of Copenhagen in collaboration with NMBU, WSL and Nordregio and will be finished in January 2021. Feel free to contact us if you are interested.

See also my previous post related to the topic on the potential of digital plan data to evaluate spatial planning.

ESPON DIGIPLAN kick-off meeting in Copenhagen, January 2020

 

 

Rhino in Urban Planning education

I am teaching in a course on urban planning for undergraduate students in landscape architecture. This year we introduced Rhino to use it as part of the design process. It was a first try for us, ourselves novice in Rhino, and ambitions were set to basic: Getting introduced to Rhino basics and producing a simple urban landscape model. The students took the model from Rhino and refined it in Illustrator. We were quite satisfied with the results and the usability of them for their final project. Have a look below.

Urban planning education in a data rich world

I just came across the following statement which I think highlights an important lack in our current planning education:

“We have entered a data rich environment, where we can have data on systems and behaviors for more frequent time increments and with a greater number of observations of a greater number of factors. Planners are still being trained in methods that are suitable for a data poor environment. [… V]isualization, simulation, data mining and machine learning are the appropriate tools to use in this new environment.”

Its from French, Barchers & Zhang (2017) How Should Urban Planners Be Trained to Handle Big Data? – I can recognize that from our own education in landscape architecture and urban design. However, I think we still need the ‘traditional’ methods like interviews, observation in field etc. especially when working on a small scale. But I can agree that urban planners or urban designers are often not taught in these new methods – with GIS as exception, as also the authors write, and probably a bit in visualization, but not in others. Most of it let to other professionals as computer scientists, some sociologists or physicists. A basic understanding of these methods is though necessary to make use of these data but also to develop a critical position to it.

Digital plan data to evaluate planning practice?

Can we use digital plan data to look into changes in planning practice? In a recent article we suggest to have a closer look at the potential of emerging digital plan data to contribute with new research perspectives.

For example in Denmark, where a national open geodatabase with all spatial plans exists since 2007, we can see, with the help of such data, that the use of “mixed zoning” opposite to other, single use zoning categories for local development plans has been increasing over the past years and is currently at about 20 % – higher than ever before. We could take this as an indication that modern planning is used to support the development of more mixed use areas or to provide from flexible frameworks for development.

Source: Fertner, C., Christensen, A. A., Andersen, P. S., Olafsson, A. S., Præstholm, S., Caspersen, O. H., Grunfelder, J. (2019) Emerging digital plan data – new research perspectives on planning practice and evaluation, Geografisk Tidsskrift-Danish Journal of Geography, 119:1, 6-16, DOI: 10.1080/00167223.2018.1528555

Rush hour in 49 cities

I am sure many of you are regular users of Google Maps and have seen the estimation of the real-time traffic situation which Google provides for major roads around the world. I am not sure what the data is based on – probably Google’s own information from phones using Google products. In any case, as it was Thursday afternoon I was impressed to see traffic pressure around cities in the Central European Time Zone. The figure below is just a collage of quick screenshots I took on my mobile within 20 minutes. I included some cities from neighboring time zones. So besides that the rush hour probably starts and ends at different times in different countries, the time zone plays of course also a role when taking screenshots at the same time. But full comparability was not my aim, rather giving a quick impression of the daily rush of cars around our cities.

Planning research in Europe – cities in AESOP 2018

Last week the annual conference of European planning schools took place in Gothenburg. Presenters came from a many parts of Europe and beyond. However, not surprisingly, most contributions came from traditionally heavy visibly research environments in planning as the UK and the Netherlands. The map below shows the appearance of European cities in the conference programme. Cities are limited to those included in Eurostat’s Urban Audit database with at least 100,000 inhabitants.

Single cities mentioned at least 15 times are London, Stockholm, Gothenburg, Amsterdam, Helsinki/Espoo, Rotterdam/Delft, Dortmund, Lisbon, Paris and Vienna. Copenhagen, including Frederiksberg, Lyngby and Roskilde, was represented 13 times which is quite more than back in 2013 (though the data might not be 1:1 comparable).

Digitale plandata – program online

Now the programme for the seminar on research perspectives in digital plan data is online. There will a couple of presentations in English, some more and a panel debate in Danish – everybody interested is welcome to join – registration per e-mail to me latest on 24 June.

Research seminar on digital plan data

Since 2006, all plans mentioned in the Danish planning law are registered in the digital plan database plandata.dk. This includes all currently effective 34,000 local development plans as well as over 50,000 zoning designations from municipal plans. Besides a range of metadata and basic info on the plans content, the explict geographic extent of each plan element is available as geodata and can be downloaded. This opens up for new research perspectives, e.g. regarding the evaluation of plans and planning, comparison of planning practice as well as more critical discussion on what that data actually illustrates or how it influences planning.

On 27 June we will hold a little research seminar on digital plan data with Danish researches and practitioners as well as guests from Norway, where they have a similar plan register. The seminar takes place at the University of Copenhagen and is funded by the Centre of Strategic Urban Research.

More to cycle than to buy a car in the future in Beijing

Chunli Zhao, a former PhD student at our department just published an article on attitudes towards cycling and buying a car in the future in Beijing, which some colleagues and I were co-authoring. The paper reports on an extensive transport behaviour survey taken in Beijing in 2014. Beijing has experienced an extreme increase in car ownership in recent years, resulting in severe traffic congestion and frequent heavy smog. But it seems that there is hope for a future of cycling in the city. Attitudes towards future cycling are general positive (see table below), even beneath car owners.

The study also shows that cycling space, distances and cycling policy influences attitudes towards future cycling. On the other hand, many respondents are planning to buy a car in the future (if they get the possibility), indicating a further increase in car traffic in the city.

 

 

Cities’ virtual size – Facebook accounts in Danish cities

A couple of years ago we took a close look at the state of Danish small towns. A very small part of the study also discussed the virtual presence of those cities, e.g. the number of Facebook accounts. For a recent seminar I put a bunch of maps on the development of Danish cities together including two on Facebook accounts. The first maps shows the number of Facebook accounts, where users have registered the particular city as their place of residence, in relation to the number of inhabitants as counted by Statistics Denmark. Especially the big cities have a relatively lower number of ‘Facebook citizens’ than of ‘real’ citizens, while this is the other way round for some of the smaller cities. Despite not knowing how Facebook actually counts, we could hypothesize that smaller places have a relatively stronger identity where people from neighbouring rural areas also choose the nearest city for their profile, while in bigger cities, more people choose districts or suburban municipalities (which are actually grown together and therefore statistically are not separated anymore) as place of residence in their Facebook profiles.

All Danish cities above 10,000 inhabitants / Size of the circle shows number of ‘Facebook citizens’, colour shows that in relation to registered (‘real’) citizens (dark blue: more than 10% more FB citizens than real residents, light blue: +/- 10% more visitors, white: more than 10% more real residents than FB citizens)

The second map shows the number of ‘Facebook citizens’ in relation to Facebook users who ‘recently’ have been in that place. All the blue coloured cities had more “Facebook visitors” than “Facebook citizens”. Again, we do not know how Facebook calculates that number – probably registering the location where people login from to Facebook. So this could be people working in the city, visitors or just people who traveled through the place. Regarding Danish cities it is interesting that some suburban towns to Copenhagen and Aarhus but also some towns along the important transport corridors down to Germany stand out. I think this gives some interesting alternative views on the urban structure of Denmark and its functional regions.

All Danish cities above 10,000 inhabitants / Size of the circle shows number of ‘Facebook citizens’, colour shows that in relation to nubmer of ‘Facebook visitors’ (dark blue: more than 50% more visitors than residents, light blue: up to 50% more visitors, white: less visitors than residents)