Modeling urban growth by coupling localized spatio-temporal association analysis and binary logistic regression. (May 2020)

• Record Type: Journal Article
• Authors: Wang, Yuwei; Sha, Zongyao; Tan, Xicheng; Lan, Hai; Liu, Xuefeng; Rao, Jing
• Is Part Of: Computers, environment and urban systems. Volume 81(2020)
• Publication Date: 2020-05
• Subjects: Urbanization -- Land cover -- Spatial association -- Modeling; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2020.101482
• Languages: English
• ISSNs: 0198-9715

Abstract

Abstract: Understanding and forecasting the dynamics of urban growth can be helpful for making sustainable land-use policies. Computing models can simulate urban growth but many require extensive data input, which cannot be always met. Here we proposed coupling localized spatio-temporal association (LSTA) analysis and binary logistic regression (BLR) to model urban growth from historical land cover configurations. An indicator called neighborhood aggregation index (NAI) was defined first to measure configuration enrichment for any land cover type under spatial-and-temporal contexts. Multiple NAIs for different land cover types were taken into the proposed LSTA-BLR model to project future urban growth. A case study was selected in Wuhan, China where land covers were classified for each year during 2014–2017 based on the Landsat Imagery from Google Earth Engine. Urban growth from the year 2016 to 2017 was extracted from the classified land cover maps as the dependent variable which was modeled by the LSTA-BLR using predictors of the NAIs from the previous years. The LSTA-BLR models were tested under different neighborhood sizes (3 × 3, 5 × 5, 7 × 7, 9 × 9, and 11 × 11) and time windows (2016, 2015–2016, and 2014–2016). Results indicated that the best accuracy of the modeled urban growth reached 72.9% under the setting of 5 × 5 neighborhood size and time window 2014–2016. Urbanization was most likely to occur close to previously urbanized areas and unlikely near the neighborhood of enriched forest and water bodies. The neighborhood size affected the modeled result and the time window defining the NAIs had the most significant impact on model performance. We conclude that prior land cover configurations should be integrated for mapping future urban growth and the proposed LSTA-BLR model can serve as a useful tool to understand the near-future urbanization process based on the historical land cover configurations. Highlights: Prior land cover configurations (LLCs) are important to map future urban growth. A neighborhood aggregation index (NAI) is defined for each LLC. LSTA-BLR takes NAIs as input to model future urban growth. A case study of modeling urban growth is conducted in Wuhan municipality. Spatial and temporal scales may affect model performance.