Assessment of landscape changes under different urban dynamics based on a multiple-scenario modeling approach

Abstract

In the context of rapid urbanization, it remains unclear how urban landscape patterns shift under different urban dynamics, in particular taking different influencing factors of urban dynamics into consideration. In the present study, three key influencing factors were considered, namely, housing demand, spatial structure, and growth form. On this basis, multiple urban dynamic scenarios were constructed and then calculated using either an autologistic regression–Markov chain–based cellular automata model or an integer programming-based urban green space optimization model. A battery of landscape metrics was employed to characterize and quantitatively assess the landscape pattern changes, among which the redundancy was pre-tested and reduced using principal component analysis. The case study of the Munich region, a fast-growing urban region in southern Germany, demonstrated that the changes of the patch complexity index and the landscape aggregation index were largely similar at sub- and regional scales. Specifically, low housing demand, monocentric and compact growth scenarios showed higher levels of patch complexity but lower levels of landscape aggregation, compared to high housing demand, polycentric and sprawl growth scenarios, respectively. In contrast, the changes in the landscape diversity index under different scenarios showed contrasting trends between different sub-regional zones. The findings of this study provide planners and policymakers with a more in-depth understanding of urban landscape pattern changes under different urban planning strategies and its implications for landscape functions and services.

What does urban informatics add to planning support technology?

“…Instrumental value is produced by enabling feedback solicited from planners and local stakeholders directly implicate PSS model outcomes and improvements (Cai et al, 2020(Cai et al, : 1380Pettit, 2020Pettit, : 1490Page et al, 2020Page et al, : 1508. Planner and stakeholder inputs help to create multiple scenarios that can inform planning consequences and policy decisions (Yang, 2020(Yang, : 1440Yang et al, 2020Yang et al, : 1474Xu et al, 2020Xu et al, : 1361. The normative value of participation is realized in the smart city ideal of transparent, participatory, and collaborative open government and data (Deal et al, 2017b;Pereira et al, 2017).…”

“…Pan et al ( : 1456 and Jia et al (2020Jia et al ( : 1418 use large, voluminous, and diverse data sources (address change data from postal records and POI data from navigation systems) to better understand the science of cities and advance the theories of complex urban systems. Xu et al (2020Xu et al ( : 1361 and Yang (2020Yang ( : 1440 contribute to the understanding of urban dynamics, urban mobility and their resulting impacts. Though these theoretical findings do not immediately, nor intuitively support practical planning, understanding complex urban phenomena is the first step toward future iterations that will ultimately become useful for planning practice.…”

“…Four different papers demonstrate the range and variability in current planning support model approaches. They include models of urban landscape patterns (Xu et al, 2020(Xu et al, : 1361, a land use change scenario modeling approach (Cai et al, 2020(Cai et al, : 1380, a new land use and transport interaction model (Basu and Ferreira, 2020: 1397) focusing on residential location and vehicle ownership choices, and a new approach to urban cellular automata (CA) modeling that takes into account future points-of-interest (POI) density distributions on urban streets (Jia et al, 2020(Jia et al, : 1418.…”

“…PSS models have traditionally been constructed to provide information on land use forecasting or impact assessment -two of the most challenging tasks facing urban planners. In this issue, Xu et al (2020Xu et al ( : 1361 assess urban landscape pattern shifts under multiple scenarios of housing demand, spatial structure, and growth with combined CA and optimization models. Cai et al (2020Cai et al ( : 1380 integrate a spatially dynamic CA land use model with preset policy scenarios (different roads, no-growth planning, and population/employment projections) that are derived from real-world planning documents to provide local policymakers with the capability to test and evaluate "what-if" consequences.…”

Spatial dynamic modelling for urban scenario planning: A case study of Nanjing, China

“…This goal-oriented scenario planning is also suitable for embedding PSS. Once the scenario planning approach is defined and standardised, the data and modelling-based PSS can be used within the process to quantify the information of development-driven elements (Xu et al, 2020). Researchers interpret the interconnectedness, unintended consequences and reduce the likelihood of extreme situations by using follow-up methods, such as impact analysis, to deal with the results of scenario planning (Meissner and Wulf, 2013;Raford, 2015).…”