Mapping Imprecision: How to Geocode Data from Inaccurate Historic Maps

Panecki, Tomasz

doi:10.3390/ijgi12040149

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…Following log-scaling, we utilized geocoding to pinpoint the geographic locations related to the housing transactions. Geocoding converts addresses into their corresponding positions on the Earth's surface (i.e., latitude and longitude), enabling the mapping and analysis of geographical data [53][54][55]. For the geocoding process, we leveraged the AI•NAVER API from the Naver Cloud Platform to convert the 'ADRES' variable from our base dataset into geographical coordinates.…”

Section: Geocoding and Distance Calculationmentioning

confidence: 99%

Influence of Social Overhead Capital Facilities on Housing Prices Using Machine Learning

Paik,

Baek,

Kim

et al. 2023

Applied Sciences

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The South Korean residential real estate market is influenced by both the traditional dynamics of demand and supply and external factors such as housing policies and macroeconomic conditions. Considering the proportion of housing assets in individual wealth, market fluctuations can have significant implications. While previous studies have utilized variables such as GDP growth rate, patent issuance, and birth rate, and employed models such as LSTM and ARIMA for housing price predictions, many have overlooked the influence of local factors. In particular, there has been insufficient investigation into the impact of subway stations and living social overhead capital facilities on housing prices, especially in metropolitan areas. This study seeks to bridge this gap by analyzing the usage trends of subway stations, evaluating the impact of living social overhead capital facilities on housing values, and deriving the optimal machine learning model for price predictions near subway stations. We compared and analyzed a total of eight machine learning regression models, including Linear Regression, Decision Tree, Random Forest, LightGBM, Ridge, Lasso, Elastic Net, and XGBoost, all of which are popular regression models, especially in the context of machine learning and data science. Through comparative analysis of these machine learning techniques, we aim to provide insights for more rational housing price determinations, thereby promoting stability in the real estate market.

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Section: Geocoding and Distance Calculationmentioning

confidence: 99%

Influence of Social Overhead Capital Facilities on Housing Prices Using Machine Learning

Paik,

Baek,

Kim

et al. 2023

Applied Sciences

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Mapping the past with historical geographic information systems: layered characteristics of the historic urban landscape of Nanjing, China, since the Ming Dynasty (1368–2024)

Bai,

Jiao,

Sakai

et al. 2024

Herit Sci

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The layering process of the historic urban landscape (HUL) has spatial and temporal characteristics. With the help of digital technology, building a database to collect and manage spatial information on HUL is an effective research method. Taking Nanjing since the Ming Dynasty (1368–2024) as an example, our paper proposes a framework for constructing a historical geographic information system (HGIS) for HUL. The results show that (1) ancient Chinese historical archives have good potential for use in the construction of historical landscape databases. The HGIS can visualize the evolution of HUL and realize the storage, expression, querying, and analysis of information in different formats. (2) The Republic of China is a turning point in the evolution of Nanjing’s HUL. In terms of spatial evolution, the main distribution areas of urban form and characteristic places, as well as the NACH (normalized angular choice) and NAIN (normalized angular integration) cores of the road network, all moved from the south to the north of the city. In terms of typological evolution, the urban form changes from a predominantly residential area to one that emphasizes residential, administrative, educational and industrial areas. The main types of characteristic places shifted from house gardens, temples and ancestral halls to scenic buildings and public gardens. (3) Political factors, urban planning, commercial ports, and land prices influence the layering process of the HUL. The constructed database guides the preservation of HUL from the digital heritage perspective and the construction of other historical databases with spatial characteristics.

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Optimal Machine Learning Model for SOC Facilities’ Impact on Housing Prices

Paik,

Baek,

Kim

et al. 2023

Preprint

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In South Korea, the residential real estate market is influenced not just by traditional supply and demand dynamics, but also by external factors such as housing policies and macroeconomic conditions. Given the significant role of housing assets in individual wealth, market fluctuations can have profound implications. While prior research has utilized variables like GDP growth rate, patent issuance, and birth rates, and employed models like LSTM and ARIMA for housing price predictions, many overlook key localized factors. Notably, the impact of subway stations and living SOC facilities on housing prices, especially in metropolitan areas, remains underexplored. This study addresses these gaps by analyzing usage trends across subway stations, assessing the influence of living SOC facilities on housing values, and identifying the optimal machine learning model for price predictions near transport hubs. Through a comparative analysis of machine learning techniques, we aim to provide insights for more informed housing price determinations, promoting a more stable real estate market.

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Mapping Imprecision: How to Geocode Data from Inaccurate Historic Maps

Cited by 3 publications

References 24 publications

Influence of Social Overhead Capital Facilities on Housing Prices Using Machine Learning

Influence of Social Overhead Capital Facilities on Housing Prices Using Machine Learning

Mapping the past with historical geographic information systems: layered characteristics of the historic urban landscape of Nanjing, China, since the Ming Dynasty (1368–2024)

Optimal Machine Learning Model for SOC Facilities’ Impact on Housing Prices

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