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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

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Showing 22 citing articles:

How much is the view from the window worth? Machine learning-driven hedonic pricing model of the real estate market
Tomasz Potrawa, Anastasija Tetereva
Journal of Business Research (2022) Vol. 144, pp. 50-65
Open Access | Times Cited: 58
How do innovation factor allocation and institutional environment affect high-quality economic development? Evidence from China
Jinfeng Zhao
Journal of Innovation & Knowledge (2024) Vol. 9, Iss. 2, pp. 100475-100475
Open Access | Times Cited: 9
Uncovering drivers of community-level house price dynamics through multiscale geographically weighted regression: A case study of Wuhan, China
Binbin Lu, Yong Ge, Yilin Shi, et al.
Spatial Statistics (2022) Vol. 53, pp. 100723-100723
Open Access | Times Cited: 20
A structured comparison of causal machine learning methods to assess heterogeneous treatment effects in spatial data
Kevin Credit, M. D. Lehnert
Journal of Geographical Systems (2023) Vol. 26, Iss. 4, pp. 483-510
Open Access | Times Cited: 12
A spatial autoregressive random forest algorithm for small-area spatial prediction
Cara MacBride, Vinny Davies, Duncan Lee
The Annals of Applied Statistics (2025) Vol. 19, Iss. 1
Closed Access
Spatial econometrics to estimate traffic reduction by transforming office space into housing and other land uses: The case for Barcelona
Javier Argota Sánchez–Vaquerizo, D. Helbing
Cities (2025) Vol. 162, pp. 105901-105901
Open Access
Modeling Habitat Suitability for Endangered Herb (Salvia leriifolia Benth) Using Innovative Hybrid Machine Learning Algorithms
Emran Dastres, Hamidreza Rabiei‐Dastjerdi, Hassan Esmaeili, et al.
Environmental and Sustainability Indicators (2025), pp. 100694-100694
Open Access
Spatial Prediction of Apartment Rent using Regression-Based and Machine Learning-Based Approaches with a Large Dataset
Takahiro Yoshida, Daisuke Murakami, Hajime Seya
The Journal of Real Estate Finance and Economics (2022) Vol. 69, Iss. 1, pp. 1-28
Closed Access | Times Cited: 15
Mapping heterogeneity: Spatially explicit machine learning approaches for urban value uplift characterisation and prediction
Xiuning Zhang, Yumo Zhu, Wei Gan, et al.
Sustainable Cities and Society (2024) Vol. 114, pp. 105742-105742
Open Access | Times Cited: 1
A random forests-based hedonic price model accounting for spatial autocorrelation
Emre Tepe
Journal of Geographical Systems (2024) Vol. 26, Iss. 4, pp. 511-540
Closed Access | Times Cited: 1
Introduction to the special issue on spatial machine learning
Kevin Credit
Journal of Geographical Systems (2024) Vol. 26, Iss. 4, pp. 451-460
Open Access | Times Cited: 1
Three Common Machine Learning Algorithms Neither Enhance Prediction Accuracy Nor Reduce Spatial Autocorrelation in Residuals: An Analysis of Twenty‐five Socioeconomic Data Sets
Insang Song, Daehyun Kim
Geographical Analysis (2022) Vol. 55, Iss. 4, pp. 585-620
Closed Access | Times Cited: 3
A comparative assessment of the statistical methods based on urban population density estimation
Merve Yılmaz
Geocarto International (2022) Vol. 38, Iss. 1
Open Access | Times Cited: 3
Searching for correct specification in spatial probit models. Classical approaches versus Gradient Boosting algorithm
Miguel De la Llave, Fernando A. López Hernández
Spatial Statistics (2024) Vol. 61, pp. 100815-100815
Closed Access
Mapping Heterogeneity: Spatially Explicit Machine Learning Approaches for Urban Value Uplift Characterisation and Prediction
Xiuning Zhang, Yumo Zhu, Wei Gan, et al.
(2024)
Closed Access
O9Answering new urban questions: Using eXplainable AI-driven analysis to identify determinants of Airbnb price in Dublin
Amir Panahandeh, Hamidreza Rabiei‐Dastjerdi, Polat Göktaş, et al.
Expert Systems with Applications (2024), pp. 125360-125360
Closed Access
Incorporating Spatial Information for Regionalization of Environmental Parameters in Machine Learning Models
Marc Ohmer, Fabienne Doll, Tanja Liesch
Mathematical Geosciences (2024) Vol. 57, Iss. 2, pp. 251-273
Open Access
Using the Web to Predict Regional Trade Flows: Data Extraction, Modeling, and Validation
Emmanouil Tranos, André Carrascal Incera, George L. Willis
Annals of the American Association of Geographers (2022) Vol. 113, Iss. 3, pp. 717-739
Open Access | Times Cited: 2
Assessing the Impact of Ferry Transit on Urban Crime
Bryan S. Weber, Paolo Cappellari
Urban Affairs Review (2022) Vol. 59, Iss. 6, pp. 1950-1972
Closed Access | Times Cited: 2
Using the web to predict regional trade flows: data extraction, modelling, and validation
Emmanouil Tranos, André Carrascal Incera, George A. Willis
(2022)
Open Access | Times Cited: 1
Analyzing the Factors that Affect and Predict Employment Density Using Spatial Machine Learning: The Case Study of Seoul, South Korea
Jane C. Ahn, Young-Sang Kwon
Geographical Analysis (2023)
Closed Access
A House Price Modeling Based on Clustering and Kriging: The Medellín Case
Hernán Darío Villada-Medina, Juan F. Rendón-García, César A. Ramírez-Dolores, et al.
Review of Regional Studies (2022) Vol. 52, Iss. 3
Open Access
Spatial prediction of apartment rent using regression-based and machine learning-based approaches with a large dataset
Takahiro Yoshida, Hajime Seya
arXiv (Cornell University) (2021)
Open Access
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