This paper presents a novel approach to retrieve indoor structures from raster images of complicated floor plans. We extract the building elements in the floor plan and process them into a vectorized form to provide indoor layout information. Unlike conventional approaches, the proposed model is robust when recognizing rooms and openings surrounded by obscuring patterns, including superimposed graphics and irregular notation. To this end, we integrate various floor plan formats into a unified style using conditional generative adversarial networks prior to vectorization. This style-transferred plan that follows the unified style represents the room structure intuitively and is readily vectorized due to its concise expression. Raster-to-vector conversion is conducted with a combinatorial optimization in junction units of the layout. The experimental results demonstrate that when implemented with complex drawings, our model is comparable to existing methods in the detection and recognition of rooms and provides a much better score in one-to-one matches.

Since walkability plays an important role in active commuting, several cities are actively promoting its integration into urban and environmental planning policies. This study examined the association between walkability and active commuting in Seoul, Korea. A multilevel logistic regression model was used to examine the correlation between Walkability Score and the probability of active commuting after controlling for individual variables. The analysis used 129,044 individual samples nested within 424 administrative districts (dongs). In this study, three models were tested: Model 1 contained only individual variables, Model 2 contained individual variables and Walkability Score, and Model 3 included neighborhood-level variables in addition to the variables of Model 2. The results showed that the Walkability Score was significantly correlated with the odds of active commuting. Specifically, every additional one-point increase in Walkability Score was associated with 0.3% higher odds of active commuting (Model 2: odds ratio (OR) = 1.003, 95% confidence interval (CI) = 1.001–1.005; Model 3: OR = 1.003, 95% CI = 1.001–1.006). Additionally, public transportation density was also positively correlated with the odds of active commuting. The odds of active commuting were positively correlated with younger age, female, lower-income, and having no car. Based on the findings, policy recommendations in urban planning and design, transport engineering, and environmental planning are provided.

This study aims to examine the relationship between the level of walkability and housing prices in Seoul, Korea. The average transaction price per square meter for each apartment complex was set as a dependent variable and the walkability score was used as an independent variable. This study divided a total of 5986 apartment complexes into areas with high and low housing prices for analysis. Based on the strong spatial autocorrelations of housing prices, this study employed spatial regression models in addition to the Ordinary Least Squares (OLS) model. Results showed that housing prices positively correlated with the walkability score in areas with low housing prices, whereas no significant association was observed in areas with high housing prices. Additional findings showed that housing prices were associated with building age (−), number of households in the complex (+), slope (−), and greenness (+) in both subsamples. Results also showed that high school quality had a different association with housing prices depending on the subsample (e.g., the sign was positive in areas with high housing prices and no significance in areas with low housing prices). The results herein support public policy proposals relevant to urban planning, environmental design, and housing policies.

Automatic floor plan analysis has gained increased attention in recent research. However, numerous studies related to this area are mainly experiments conducted with a simplified floor plan dataset with low resolution and a small housing scale due to the suitability for a data-driven model. For practical use, it is necessary to focus more on large-scale complex buildings to utilize indoor structures, such as reconstructing multi-use buildings for indoor navigation. This study aimed to build a framework using CNN (Convolution Neural Networks) for analyzing a floor plan with various scales of complex buildings. By dividing a floor plan into a set of normalized patches, the framework enables the proposed CNN model to process varied scale or high-resolution inputs, which is a barrier for existing methods. The model detected building objects per patch and assembled them into one result by multiplying the corresponding translation matrix. Finally, the detected building objects were vectorized, considering their compatibility in 3D modeling. As a result, our framework exhibited similar performance in detection rate (87.77%) and recognition accuracy (85.53%) to that of existing studies, despite the complexity of the data used. Through our study, the practical aspects of automatic floor plan analysis can be expanded.

A walkable environment is a crucial factor for promoting active transportation. The purpose of this study is to examine the association between neighborhood walkability and active transportation for noncommuting purposes (leisure and shopping) in Seoul, Korea. The Walkability Score is used as a measure of walkability, and a multilevel logistic regression model is employed to measure the odds of active transportation (i.e., walking and cycling; nonmotorized trips) at two levels: individual (level 1) and neighborhood (level 2). The results of the study showed that the Walkability Score was significantly correlated with higher odds of active transportation in shopping models. Specifically, every one-point increase in the Walkability Score was associated with 1.5%–1.8% higher odds of active transportation in shopping models. However, there was no significant correlation between the two in leisure models. Meanwhile, individual characteristics associated with the odds of active transportation differed in the leisure and shopping models. Older age was positively correlated with the odds of active transportation in the leisure model, while females showed a positive correlation in the shopping model. Based on the study, urban and transportation planners can recommend urban policies to promote active transportation in an urban setting.

Since the dawn of the 21st century, Japan has switched its national industry strategy from traditional industries—manufacturing and trading—toward tourism. Regional revitalization is a particularly important issue in Japan, and by uniting regions as an integrated tourism zone, the government expects an increase in visits to tourism zones. This study quantitatively evaluates whether the regions that contain a tourism zone experience a significant increase in visitors by using a quasi-experimental pretest–posttest control group design. Additionally, it examines the effects of subsidies through regression modeling. The results indicated that the tourism zones that were comprised of a narrow region in the same prefectures experienced a significant increase in visitors. The subsidy on information transmission, measures for the secondary traffic, and space formation had a significant positive impact on the increase in visitors to these tourism zones. Implications on tourism policies, urban and regional development, and community development can be obtained through this study.