Identifying Urban Neighborhood Names through User-Contributed Online Property Listings

Neighborhoods are vaguely defined, localized regions that share similar characteristics. They are most often defined, delineated and named by the citizens that inhabit them rather than municipal government or commercial agencies. The names of these neighborhoods play an important role as a basis for community and sociodemographic identity, geographic communication and historical context. In this work, we take a data-driven approach to identifying neighborhood names based on the geospatial properties of user-contributed rental listings. Through a random forest ensemble learning model applied to a set of spatial statistics for all n-grams in listing descriptions, we show that neighborhood names can be uniquely identified within urban settings. We train a model based on data from Washington, DC, and test it on listings in Seattle, WA, and Montréal, QC. The results indicate that a model trained on housing data from one city can successfully identify neighborhood names in another. In addition, our approach identifies less common neighborhood names and suggestions of alternative or potentially new names in each city. These findings represent a first step in the process of urban neighborhood identification and delineation.

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A Data-Driven Approach for Winter Precipitation Classification Using Weather Radar and NWP Data

Atmosphere ◽

10.3390/atmos11070701 ◽

2020 ◽

Vol 11 (7) ◽

pp. 701

Author(s):

Bong-Chul Seo

Keyword(s):

Random Forest ◽

Binary Classification ◽

Weather Prediction ◽

Model Development ◽

Winter Precipitation ◽

Ensemble Classification ◽

Supervised Machine Learning ◽

Data Driven ◽

Support Vector ◽

Data Driven Approach

This study describes a framework that provides qualitative weather information on winter precipitation types using a data-driven approach. The framework incorporates the data retrieved from weather radars and the numerical weather prediction (NWP) model to account for relevant precipitation microphysics. To enable multimodel-based ensemble classification, we selected six supervised machine learning models: k-nearest neighbors, logistic regression, support vector machine, decision tree, random forest, and multi-layer perceptron. Our model training and cross-validation results based on Monte Carlo Simulation (MCS) showed that all the models performed better than our baseline method, which applies two thresholds (surface temperature and atmospheric layer thickness) for binary classification (i.e., rain/snow). Among all six models, random forest presented the best classification results for the basic classes (rain, freezing rain, and snow) and the further refinement of the snow classes (light, moderate, and heavy). Our model evaluation, which uses an independent dataset not associated with model development and learning, led to classification performance consistent with that from the MCS analysis. Based on the visual inspection of the classification maps generated for an individual radar domain, we confirmed the improved classification capability of the developed models (e.g., random forest) compared to the baseline one in representing both spatial variability and continuity.

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Data-Driven Approach for Satellite Onboard Observation Task Planning Based on Ensemble Learning

Wireless and Satellite Systems - Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ◽

10.1007/978-3-030-69072-4_15 ◽

2021 ◽

pp. 175-184

Author(s):

Shuang Peng ◽

Jiangjiang Wu ◽

Chun Du ◽

Hao Chen ◽

Jun Li

Keyword(s):

Ensemble Learning ◽

Data Driven ◽

Task Planning ◽

Data Driven Approach

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Stochastic Detection of Interior Design Styles Using a Deep-Learning Model for Reference Images

Applied Sciences ◽

10.3390/app10207299 ◽

2020 ◽

Vol 10 (20) ◽

pp. 7299 ◽

Cited By ~ 1

Author(s):

Jinsung Kim ◽

Jin-Kook Lee

Keyword(s):

Deep Learning ◽

Interior Design ◽

Learning Model ◽

Data Driven ◽

Data Preparation ◽

Reference Images ◽

Data Driven Approach ◽

Image Set ◽

Design Style ◽

Deep Learning Model

This paper describes an approach for identifying and appending interior design style information stochastically with reference images and a deep-learning model. In the field of interior design, design style is a useful concept and has played an important role in helping people understand and communicate interior design. Previous studies have focused on how the interior design style categories can be defined. On the other hand, this paper focuses on how stochastically recognizing the design style of given interior design reference images using a deep learning-based data-driven approach. The proposed method can be summarized as follows: (1) data preparation based on a general design style definition, (2) implementing an interior design style recognition model using a pre-trained VGG16 model, (3) training and evaluating the trained model, and (4) demonstration of stochastic detection of interior design styles for reference images. The result shows that the trained model automatically recognizes the design styles of given interior images with probability values. The recognition results, model, and trained image set contribute to facilitating the management and utilization of an interior design references database.

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