A clustering framework proposal for defining neighborhood-scale dynamics: Evidence from São Paulo, Brazil

This study is aimed at decomposing and identifying the mediating effects of transit-oriented compact city planning elements on rail transit ridership during the proliferation of the Middle East Respiratory Syndrome (MERS) in Seoul, Korea. The study are focused on how urban physical form such as density, diversity, design, and transit accessibility in rail station areas had affected the decrease of rail transit ridership during that time indirectly as well as directly, by employing the path modeling. Their indirect impacts on it are measured as rail transit ridership of a rail station affected by the form indictors as well as condensing trip-inducing facilities and socio-economic indicators for the 500m-buffer rail station area. Analysis results are summarized as follows below. First, rail transit ridership significantly decreased by urban physical form as well as single-unit facilities within a certain area. Second, the former had more indirectly influenced ridership decrease. Third, some urban physical form such as density, diversity and design had statistically significant on it while total effects of the two socio-economic measures had not. Fourth and finally, the avoidance for the use of rail transit were more prominent for the elderly than for the others. In addition, all of the urban form measures were differentiatively influenced by the two groups of rail riders.

Accurate delineation of urban form is essential to understand the impacts that urbanization has on the environment and regional climate. Conventional supervised classification of urban form requires a rigidly defined scheme and high-quality sample data with class labels. Due to the complexity of urban systems, it is challenging to consistently define urban form types and collect metadata to describe them. Therefore, in this study, we propose a novel unsupervised deep learning method for urban form delineation while avoiding the limitations of conventional supervised urban form classification methods. The novelty of the proposed method is the Multiscale Residual Convolutional Autoencoder (MRCAE), which can learn the latent representation of different urban form types. These vectors can be further used to generalize urban form types by using Self-Organizing Map (SOM) and the Gaussian Mixture Model (GMM). The proposed method is applied in the metropolitan area of Guangzhou-Foshan, China. The MRCAE model along with SOM and GMM is used to generalize the urban form types from satellite images. The physical and functional properties of each urban form type are also analyzed using several auxiliary datasets, including building footprints, Points-of-Interests (POIs) and Tencent User Density (TUD) data. The results reveal that the urban form map generated based on the MRCAE can explain 55% of the building height distribution and 55% of the building area distribution, which are 2.1% and 3.3% higher than those derived from the conventional convolutional autoencoder. As the information of urban form is essential to urban climate models, the results presented in this study can become a basis to refine the quantification of urban climate parameters, thereby introducing the urban heterogeneity to help understand the climate response of future urbanization.

Based on the bid rent theory, a thematic map depicting the pattern of land value of a city could be an effective tool to understand its urban form because the land value pattern reflects the cities' structures of both accessibility and land use intensity. This paper presents a case study of the aforementioned argument using the urbanized area of the Taichung City in central Taiwan as the study area. The paper outlines a regression analysis, whose dependent variable is the government appraised land value and two independent variables are accessibility and land use intensity factors, to verify the connection between land value and urban form and determine to what extent they interact. The result shows that the two factors account for nearly 50% of the land-value variation. Further examining those spots where disparity between actual and estimated land values located reveals even more insights about the city.

The market analysis report that is submitted to the decision maker (see chapter 4) should include a descriptive, qualitative overview of the context the real estate project has to the existing and changing urban environment. To better accomplish this task, one should have knowledge of the general theory regarding the market processes that bring about land use and urban form. This chapter presents three relevant general theories of land use and land value. Together, these general theories provide a general qualitative understanding of how the existing urban environment came to be and allows the analyst to prognosticate the trajectory of change of urban land uses and land values. The first two of the general theories presented here arose out of an attempt to explain agricultural land values and land uses. Why should a discussion of the agronomy sector be included in a book on urban real estate analysis? First, all the general theories relevant to land values and land use, and their spatial distribution within a city, are part of an intellectual heritage that dates from general theories of agricultural land values and land use. Second, much of new urban development occurs at those suburban margins. To understand development at the suburban margins, there must be an understanding of the nonurban land uses and land values at those locations. The third general theory explains spatial equilibrium and its role in shaping urban land values and land uses. Two eighteenth-century theorists, David Ricardo and Johann Heinrich von Thünen, are credited for having created a vast and sometimes opposing literature on land valuation. Ricardo’s economic theory was based upon the relative productivity of sites. In contrast, von Thünen’s geographic theory was focused on the locational component of land values and land use. The juxtaposition of these two competing giants of land theory in many respects still differentiates economists and geographers even today. After the theories of Ricardo and von Thünen are presented, an overview of the consumption theory of land rent (CTLR) is provided. The CTLR is my general theory and methodology for evaluating urban housing land use, land values, and urban form (Thrall 1980, 1987, and see 1991).

Transportation Greenhouse Gas Emissions and its Relationship with Urban Form, Transit Accessibility and Emerging Green Technologies: A Montreal Case Study

Transit access and urban space-time structure of American cities

Influences on transit ridership and transit accessibility in US urban areas

SARS-CoV-2 pandemic first emerged in late 2019 in China. It has since infected more than 298 million individuals and caused over 5 million deaths globally. The identification of essential proteins in a protein–protein interaction network (PPIN) is not only crucial in understanding the process of cellular life but also useful in drug discovery. There are many centrality measures to detect influential nodes in complex networks. Since SARS-CoV-2 and (H1N1) influenza PPINs pose 553 common human proteins. Analyzing influential proteins and comparing these networks together can be an effective step in helping biologists for drug-target prediction. We used 21 centrality measures on SARS-CoV-2 and (H1N1) influenza PPINs to identify essential proteins. We applied principal component analysis and unsupervised machine learning methods to reveal the most informative measures. Appealingly, some measures had a high level of contribution in comparison to others in both PPINs, namely Decay, Residual closeness, Markov, Degree, closeness (Latora), Barycenter, Closeness (Freeman), and Lin centralities. We also investigated some graph theory-based properties like the power law, exponential distribution, and robustness. Both PPINs tended to properties of scale-free networks that expose their nature of heterogeneity. Dimensionality reduction and unsupervised learning methods were so effective to uncover appropriate centrality measures.

Context. Classification will be an important first step for upcoming surveys aimed at detecting billions of new sources, such as LSST and Euclid, as well as DESI, 4MOST, and MOONS. The application of traditional methods of model fitting and colour-colour selections will face significant computational constraints, while machine-learning methods offer a viable approach to tackle datasets of that volume. Aims. While supervised learning methods can prove very useful for classification tasks, the creation of representative and accurate training sets is a task that consumes a great deal of resources and time. We present a viable alternative using an unsupervised machine learning method to separate stars, galaxies and QSOs using photometric data. Methods. The heart of our work uses Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to find the star, galaxy, and QSO clusters in a multidimensional colour space. We optimized the hyperparameters and input attributes of three separate HDBSCAN runs, each to select a particular object class and, thus, treat the output of each separate run as a binary classifier. We subsequently consolidated the output to give our final classifications, optimized on the basis of their F1 scores. We explored the use of Random Forest and PCA as part of the pre-processing stage for feature selection and dimensionality reduction. Results. Using our dataset of ∼50 000 spectroscopically labelled objects we obtain F1 scores of 98.9, 98.9, and 93.13 respectively for star, galaxy, and QSO selection using our unsupervised learning method. We find that careful attribute selection is a vital part of accurate classification with HDBSCAN. We applied our classification to a subset of the SDSS spectroscopic catalogue and demonstrated the potential of our approach in correcting misclassified spectra useful for DESI and 4MOST. Finally, we created a multiwavelength catalogue of 2.7 million sources using the KiDS, VIKING, and ALLWISE surveys and published corresponding classifications and photometric redshifts.

Multi-level urban form and commuting mode share in rail station areas across the United States; a seemingly unrelated regression approach

The Spatial Equity of Calgary’s Urban Form: Walkability and Transit Access in Newcomer Destination Communities ( poster )

Disentangling the effects of urban form and socio-demographic context on street tree cover: A multi-level analysis from Montréal

Street layout and design, once established, are then not easily changed. Urban form affects community development, livability, sustainability, and traffic safety. There has been an assumed relationship between urban form and traffic safety that favors designs with less through streets to improve safety. An empirical study to test this assumed relationship was carried out for crash data for Portland, Oregon. This thesis presents an empirical methodology for analyzing the relationship between urban form and traffic safety utilizing a uniform grid for the spatial unit. Crashes in the Portland, Oregon city limits from 2005-2007 were analyzed and modeled using negative binomial regression to study the effect of urban form and street layout through factors on exposure, connectivity, transit accessibility, demographic factors, and origins and destinations. These relationships were modeled separately by mode: vehicle crashes, pedestrian and bicycle crashes. Models were also developed separately by crash type and by crash injury severity. The models found that urban form factors of street connectivity and intersection density were not significant at 95% confidence for vehicle and pedestrian crash rates, nor for different crash severity levels, indicating that high connectivity grid street layout may have comparable safety to loops and lollipops, in contrast to results in earlier studies. Elasticity for all models was dominated by VMT increases. Business density, population and transit stops were also significant factors in many models, underlining the importance not only of street layout design, but also planning to direct development to influence where businesses, employment, and housing will grow and handle traffic volumes safely.

Urban form affects community development, livability, sustainability, and traffic safety. Urban planners have long assumed a relationship between urban form and traffic safety. That relationship favors designs with fewer through streets because such designs are believed to improve safety. An empirical study to explore this assumed relationship used crash data and an extensive resource of other data to define the urban form. Total reported crashes (21,492) within the city limits of Portland, Oregon, from 2005 to 2007 were aggregated by using a uniform 0.1-mi grid for the spatial unit (n = 792 cells); the crashes were modeled by using negative binomial regression to study the effect of urban form, which was defined by variables that captured street layout, exposure, connectivity, transit accessibility, demographics, and trip making (origins and destinations). These relationships were modeled separately by mode (vehicle, pedestrian, and bicycle crashes), by crash type, and by severity of crash injury. The models found that urban-form variables of street connectivity and intersection density were not significant at the 95% confidence level for vehicle and pedestrian crashes or for different levels of crash severity, in contrast to results in earlier studies. Elasticity estimates for all models were dominated by increases in vehicle miles traveled. Business density, population, and transit stops were significant variables in many models; these results underlined the importance of the design and planning of streets in determining where growth in businesses, employment, and housing will occur so that added traffic volumes can be handled safely.

A greenhouse gas (GHG) emissions inventory is estimated at the household level from disaggregated trip data considering all emitting modes. Trip-level GHG emissions are estimated by combining data sources (e.g., origin–destination surveys, vehicle fleet characteristics, transit rider ship data) and by using modeling tools (traffic assignment and GHG models) developed for Montreal, Quebec, Canada. A simultaneous equation model framework is implemented to investigate links between urban form, transit supply, sociodemographics, and travel GHGs, taking into account the issue of residential self-selection. The potential impacts of land use and transit supply strategies with emerging green technology scenarios are then compared with each other. Findings are consistent with the literature; built environment attributes are statistically significant (10% increase in density, transit accessibility, and land use mix results in 3.5%, 5.8%, and 2.5% GHG reductions, respectively), and the number of workers and retirees make important contributions to GHG emissions at the household level (102% increase from adding one worker and 51% decrease from adding one retiree). Also, if the current transit fleet were replaced with electric trains and hybrid buses, transit GHGs would decrease by 32%. If current trends persist in the private motor vehicle fleet, continued improvements in car fuel economy are estimated to reduce car GHGs 7% by 2020. The two most effective strategies for reducing regional and household GHGs appear to be to improve the fuel efficiency of the private motor vehicle fleet and to increase transit accessibility.