Analysis on urban densification dynamics and future modes in southeastern Wisconsin, USA

Verification of Psychophysiological Effects of Satoyama Activities on Older Adult Volunteers and Young People in Post-COVID-19 Society: A Case Study of Matsudo City, Japan

Many scholars have focused on Satoyama, which is characterized by mountains or villages away from the urban spaces. Our objective is to verify its psychophysiological effects on people performing usual Satoyama activities in ignored, small urban green spaces to help people find ways to stay healthy in post-coronavirus disease 2019 (COVID-19) society. In this study, 12 older adult volunteers from the "Forest around the Mountains" Nonprofit Organization and 12 young people from the university were invited as study subjects. They were asked to observe nature for 10 min and work for 30 min in the small green space "Forest around the Mountains". The Profile of Mood States 2nd Edition (POMS) and the State-Trait Anxiety Inventor (STAI) were used as psychological scales to detect their psychological restoration and blood pressure before and after the Satoyama activity. Their heart rate during the activity was used as the physiological indicator. The study showed that, as Satoyama volunteers, the older adults group had significant restorative psychophysiological effects during this experiment compared to the younger group; their systolic and diastolic blood pressure dropped significantly after Satoyama activities, On the other hand, the young group have been in the normotensive range before or after Satoyama activities The psychological indicators such as Anger-hostility, Confusion-bewilderment, and Tension-anxiety were significantly lower in the younger group but were still significantly higher than the indicators of the older adults. In general, this study found that Satoyama activities benefited young and older participants, especially older adults with high blood pressure. Satoyama activities in small urban green spaces are thus necessary and worth promoting in the post-COVID-19 era.

Cellular Automata in Modeling and Predicting Urban Densification: Revisiting the Literature since 1971

The creation of an accurate simulation of future urban growth is considered to be one of the most important challenges of the last five decades that involves spatial modeling within a GIS environment. Even though built-up densification processes, or transitions from low to high density, are critical for policymakers concerned with limiting sprawl, the literature on models for urban study reveals that most of them focus solely on the expansion process. Although the majority of these models have similar goals, they differ in terms of implementation and theoretical assumptions. Cellular automata (CA) models have been proven to be successful at simulating urban growth dynamics and projecting future scenarios at multiple scales. This paper aims to revisit urban CA models to determine the various approaches for a realistic simulation and prediction of urban densification. The general characteristics of CA models are described with respect to analysis of various driving factors that influence urban scenarios. This paper also critically analyzes various hybrid models based on CA such as the Markov chain, artificial neural network (ANN), and logistic regression (LR). Limitation and uncertainties of CA models, namely, neighborhood cell size, may be minimized when integrated with empirical and statistical models. The result of this review suggests that it is useful to use CA models with multinomial logistic regression (MLR) in order to analyze and model the effects of various driving factors related to urban densification. Realistic simulations can be achieved when multidensity class labels are integrated in the modeling process.

Urban Policy Sustainability through a Value-Added Densification Tool: The Case of the South Boston Area

Over the past decade, urban density has been growing faster than ever, forcing high-density expansion. The aim of this study is to verify whether urban density is accepted as a sustainable value-added quality, internalized in the willingness to pay on a buildable per square feet basis. To explore the relationship between land prices and densification processes, this study focused on a low-density area, which recently went through a densification policy process with the approval of a new zoning tool. The study analyzes land price trends on a 144-Acre of area, located in the South Boston Submarket, identified as the Dorchester Ave Area. I analyzed land transactions in this area between 2012 and 2021. I also examined land price variations before and after the approval of a densification plan in correlation with the overall trend of the real estate market in that area. The results suggest that density is a value-added feature that affects land prices. Indeed, a higher density leads to higher values per buildable square feet. Densification policies have a strong positive impact on land transaction prices. Community and developers valued density with a greater willingness to pay, internalizing the economic, social, and environmental sustainability benefits. This phenomenon should be taken into consideration by local public authorities implementing their zoning tools.

Exploring Built-Up Indices and Machine Learning Regressions for Multi-Temporal Building Density Monitoring Based on Landsat Series

Uncontrolled built-up area expansion and building densification could bring some detrimental problems in social and economic aspects such as social inequality, urban heat islands, and disturbance in urban environments. This study monitored multi-decadal building density (1991-2019) in the Yogyakarta urban area, Indonesia consisting of two stages, i.e., built-up area classification and building density estimation, therefore, both built-up expansion and the densification were quantified. Multi sensors of the Landsat series including Landsat 5, 7, and 8 were utilized with some prior corrections to harmonize the reflectance values. A support vector machine (SVM) classifier was used to distinguish between built-up and non built-up areas. Regression algorithms, i.e., linear regression (LR), support vector regression (SVR), and random forest regression (RFR) were explored to obtain the best model to estimate building density using the inputs of built-up indices: Urban Index (UI), Normalized Difference Built-up Index (NDBI), Index-based Built-up Index (IBI), and NIR-based built-up index based on the red (VrNIR-BI) and green band (VgNIR-BI). The best models were revealed by SVR with the inputs of UI-NDBI-IBI and LR with a single predictor of UI, for Landsat 8 (2013-2019) and Landsat 5/7 (1991-2009), respectively, using separate training samples. We found that machine learning regressions (SVM and RF) could perform best when the sample size is abundant, whereas LR could predict better for a limited sample size if a linear positive relationship was identified between the predictor(s) and building density. We conclude that expansion in the study area occurred first, followed by rapid building development in the subsequent years leading to an increase in building density.

Spatiotemporal Changes in 3D Building Density with LiDAR and GEOBIA: A City-Level Analysis

Understanding how, where, and when a city is expanding can inform better ways to make our cities more resilient, sustainable, and equitable. This paper explores urban volumetry using the Building 3D Density Index (B3DI) in 2001, 2010, 2019, and quantifies changes in the volume of buildings and urban expansion in Luxembourg City over the last two decades. For this purpose, we use airborne laser scanning (ALS) point cloud (2019) and geographic object-based image analysis (GEOBIA) of aerial orthophotos (2001, 2010) to extract 3D models, footprints of buildings and calculate the volume of individual buildings and B3DI in the frame of a 100 × 100 m grid, at the level of parcels, districts, and city scale. Findings indicate that the B3DI has notably increased in the past 20 years from 0.77 m3/m2 (2001) to 0.9 m3/m2 (2010) to 1.09 m3/m2 (2019). Further, the increase in the volume of buildings between 2001–2019 was +16 million m3. The general trend of changes in the cubic capacity of buildings per resident shows a decrease from 522 m3/resident in 2001, to 460 m3/resident in 2019, which, with the simultaneous appearance of new buildings and fast population growth, represents the dynamic development of the city.