סמינר מחלקתי בגאוגרפיה וסביבת האדם: Spatial (Big) Data Science – Case Study Urban Sprawl
Dr. Martin Behnisch, Leibniz Institute of Ecological Urban and Regional Development
The aim of this contribution is to highlight and describe the importance of spatial data science for the analysis of (large) digital spatial data sets, using the persistent environmental problem of urban sprawl as an example. In relation to the analysis and evaluation of the status quo as well as the dynamics of urban sprawl, the following guiding question is posed: "To what extent can methods of spatial data science make empirical contributions for the study of urban sprawl?"
In general terms, urban sprawl denotes dispersed urban expansion at low density, or 'the uncontrolled spread of towns and villages into undeveloped areas'. Urban sprawl is an increasing threat to the long-term availability of many vital ecosystem services and other resources, aggravates climate change, and contradicts the principles of sustainable land use. Recent availability of more consistent digital land-cover and land-use data at high resolutions offer new opportunities for investigating urban sprawl using quantitative methods at higher spatial and thematic resolutions and allow for temporal comparisons over a long period. In particular, the Global Settlement Layer and Global Urban Footprint datasets allow to measure and compare global patterns of urban sprawl at six spatial scales for the period 1990–2014. The approach of weighted urban proliferation (WUP) is presented as a landscape-oriented metric. WUP consists of three components (proportion of builtup areas PBA, dispersion DIS, and land-uptake per inhabitant or job LUP).
Looking at the changes in urban sprawl between 1990 and 2014, it is noticeable that high urban sprawl and high dynamics can be observed above all in countries that have had sophisticated planning systems for a long time. The question is whether the existing planning rules are effective enough to limit urban sprawl or whether they are inadvertently designed to encourage urban sprawl, for example by setting relatively low densities for new developments, e.g. by supporting the construction of low‐density residential and commercial areas.
The observed urban sprawl patterns need to be explained and understood in their respective context of legal and socio‐economic conditions and planning practices. Explanatory models of urban sprawl and its drivers will address these aspects in the future. Monitoring urban sprawl can serve to guide policy development such as the implementation of targets and limits and to evaluate the effectiveness of measures to curtail control sprawl.
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