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How to grow? -Modeling land use change to develop sustainable pathways for settlement growth in the hinterland of Cologne, Germany

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How to grow? -Modeling land use change to develop sustainable pathways for settlement growth in the hinterland of Cologne, Germany
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How to grow? -Modeling land use change to develop sustainable pathways for settlement growth in the hinterland of Cologne, Germany Urban sprawl is associated with negative environmental impacts such as the loss of habitat and the loss of most fertile soils for agriculture. The hinterland of Cologne, Germany is facing these challenges. The area is expected to face a population increase by 200,000 inhabitants in the next twenty years. Given past development trends, this population increase will have to be mainly absorbed by the cities and villages in the hinterland. While this provides ample economic opportunities, negative impacts on ecosystems as well as on agriculture have to be assumed due to urban sprawl and increasing fragmentation. The region is known as as one of the most productive agricultural regions in Central Europe. As highest fertile soils are located in the direct neighborhood of existing settlements, urban sprawl will lead to strong trade-offs with agricultural production. The aim of the scientific project NACHWUCHS is to identify alternatives to the continuation of existing development patterns. Therefore, we developed a baseline land use model and compare it to scenarios that assume different brownfield development activities. Stakeholder involvement is at the core of the project, as policies for alternative pathways cannot be successfully implemented without the support by farmers, real estate companies, environmental stakeholder , the municipalities and the district administration. The most important aspect of land use change in the region is the allocation of new housing areas. This is modeled by a tool-chain based on a free software stack, that uses PostgresSQL with a Postgis extention, Python and QGIS. The allocation model for new housing areas is currently based on a random forest classifier that has been trained on the official governmental ATKIS vector land use data set. The predictors of the model included distance to public transport and social infrastructure as well as existing land use development plans. The allocation of new housing areas was limited to areas outside of protected areas. Furthermore, only a few land use classes – mainly agriculture – were allowed for the allocation of new housing areas. The distance-based predictors were calculated by the openrouteservice, which uses OpenStreetMap data to build the routing graph and to assign routing weights. A 100 by 100m vector grid was used for model training and prediction. Model performance was evaluated based on a split in test and training data that considered spatial relationships. Based on the suitability of the grid cells the demand for projected new housing areas was allocated. We used nine scenarios that differed in the building density for new housing areas as well as by the extent of brownfield development . In the study presented, building density is expressed in residential units per hectare. Residential units per hectare is simplified as the number of flats in a building. In the simulated scenarios, three density classes (10, 30 and 50 residential units per hectare) and three different proportions of brownfield development (10, 20 and 40 per cent) were combined. In the simulated period from 2018 to 2040, we had an area increase of more than fifty percent between the scenario with the lowest density and the lowest proportion of brownfield development and the scenario with the highest density and the highest proportion of brownfield development . The results of the allocation procedure was evaluated based on a set of indicators which cover environmental, agricultural and social aspects. Examples are the supply of agriculture related ecosystem services, soil fertility, economic value of agricultural production and hemeroby.We used the Open Data of the State of North Rhine-Westphalia, which contained geodata for the relevant domains economy, environment and nature conservation, agriculture, social affairs and transport. The data are Inspire-compliant and available under a free licence (DL-DE->Zero-2.0) . The data set further allowed the evaluation of the model results with regard to the consequences of the flood disaster of the 14th July 2021, which severely affected parts of the hinterland of Cologne. Our results will be used in the context of a mission statement for the future regional development, developed together with locals stakeholders. The mission statement defined development goals for four sub-regions derived by socio-economic and environmental properties based on 17 UN SDGs. With the help of the above-mentioned indicators, we will evaluate how close or how far the results of the different scenarios are to these goals and assist local stakeholders, e.g. in the search for locations of new residential areas. A transfer of the model to regions with similar settings is possible as long as suitable data is available for retraining the model and for the estimation of the indicator sets, highlighting again the importance of open data. The ATKIS data used is openly available for some of the federal states of Germany but not beyond. For North-Rhine Westphalia a transfer semms reasonable- Test runs based on the CORINE land use / land cover product lead to comparable results, indicating that this might be a suitable replacement for the ATKIS based land use information.The Python code of the model, the necessary scripts to generate the required postgisdatabase, a QGIS project example for the visualisation of the results as well as a set of training and test data are provided under free licence via a Gitlab repository.
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Transcript: English(auto-generated)
Thank you. First of all, I'm a little bit nervous because my first talk for international plenary in English. What I present today is a part of a research project we've done with several stakeholders from municipalities and other universities in our region of Cologne in Germany.
Cologne, if you can, you can for this cathedral and the carnival. In the region of Cologne, we have an influx of 170,000 people from 2018 to 2014.
This people came from new industries like IT and bio science and aeration and for this people we need like 3,300 hectares of new settlement areas.
And this is a big problem because we have conflicts with agriculture and natural resources and on the other part
we have in the region a structural change in the traditional industries like light mining for light coal and chemistry industry.
So we have the typical situation of urban sprawl at the moment and we would look in our project how can we bring this development of more sustainable with ongoing the SDG number 11 and the SDG number 2 in the second tray.
For this we have three main questions on my work.
It's possible to develop a land use model on open source software and open data and what can the model bring for the stakeholders in the region and for the urban planning. For CHEC it's possible to develop a model. We have developed two scenarios
with the local stakeholders for the development of the region from 2018 to 2015. One scenario is we ongoing with the current development of new residential areas and the other
scenario is we will protect very high potential areas too from development of new residential areas. And we have looked for some predictors who were interesting for people to go in new residential areas.
The predictors can you see in zoom? The distance to train stations, motorways and social infrastructure.
On these days we have looked if we can find open data through these themes. For the current land use the government of Germany and not Westphalia have all data over land use bring on
a free license which near is to the to a nonzero license that we have freaked information over current land use. Then the public transport organizations brings her information to the CEC license and the
other information over social infrastructure and motorways we can found an open street map. So we have free data. But the devil is in the detail. First of all for a good model we must bring this data of uniform quick from 100 and 100 meter per grid cell.
And one problem is the mostly data have trouble local and gamutally errors. So we have the data now we must look for a model.
You can see the results of the data processing. That is the current language in the region based on the German official catastrophe system. That is the distance to the train station and to the motorway.
Please speak to the microphone. Okay. Now we have looked what for software have for model.
And we have lot of good open source tools for such land use model. We have found a way to build up model in completely open source deck with QGIS, Postgres as database and the typical Python scientific stack with Pandas and Skidlearn and NumPy.
For the allocation to simulate the allocation process of the residential areas we use a random forest model.
But for we can use the model we must preprocess the data in SQL.
First we must the data bring of one similar projection. Then we must repair the broken gamutries and topologies. Then we can cut then first we can cutting the data to the study area. And in the last strip we transform the data of the uniform grid.
With this data we can train the random forest with the current land use and the describing factors. Now if we train the random forest we gave the random forest the information from the different scenarios.
And for the current practice order the maintaining agriculture scenario who we protect the very high agriculture areas.
After the random forest process we became a map of potential new residential area grid cells. With this map we go in the second step of the model who we disputed the needed new residential spaces on the potential areas.
And we evaluate the model results with some tools. Now the evaluation of the results we have developed 20 indicators from 5 categories are used for evaluation of the sustainability.
And we can analyze the impact of the new residential areas on protected species on protected plants.
To show conflicts for the regional planning and the urban planning very early.
On this slide you can see some indicators. We have indicators from infrastructure, from agriculture value of areas and for ecological questions.
Now we see the first result. The first result is the map of the potential new areas. You can see in here. Maybe you can use this as a pointer.
For me it's difficult. I'm visually impaired. Yes, sorry. Okay. It should be like that. Ah, okay. Thank you. We can see here. We can see which grid shell from agriculture is changed to potential new area in which scenario.
In the second map, I write that not good to see. We can see which grid shell in which year will be changed from agriculture to residential area.
And the last and the stakeholders in main result is they have a chance to found a conflict very early. With nature conversation or other things. That is a problem in the German regional planning system.
Because information over conflicts came in the German planning systems very late. And so it's mostly planning work was going on and the justice must look what to do with conflicts with nature or with agriculture.
Now we have a tool we can see conflicts very, very, very early. And can give some other sustainable development changes.
At the moment I will be re-implemented the model with some new technologies.
Since I have the model developed in the year 2018, we have some nice new software libraries. Like Geopandas, like Fiona, like Moving Pandas. And the open world service from Heidelberg. So that we have such better model in the next year.
What will I implemented as a QGIS plugin so that people can use the model all over the world. Because I have checked that the model works with the current land use data and with distance data from other countries.
On my journey to Norway last year, I have tested the model with Norwegian data and it works seamless too.
And if some people have regions or can test the model in the world, I am very interested. Questions and discussing points?
Thank you Mirko for your presentation.