Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
- Teacher spread
- 0.216 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
Abstract By incorporating temporal effects into the geographically weighted regression (GWR) model, an extended GWR model, geographically and temporally weighted regression (GTWR), has been developed to deal with both spatial and temporal nonstationarity simultaneously in real estate market data. Unlike the standard GWR model, GTWR integrates both temporal and spatial information in the weighting matrices to capture spatial and temporal heterogeneity. The GTWR design embodies a local weighting scheme wherein GWR and temporally weighted regression (TWR) become special cases of GTWR. In order to test its improved performance, GTWR was compared with global ordinary least squares, TWR, and GWR in terms of goodness-of-fit and other statistical measures using a case study of residential housing sales in the city of Calgary, Canada, from 2002 to 2004. The results showed that there were substantial benefits in modeling both spatial and temporal nonstationarity simultaneously. In the test sample, the TWR, GWR, and GTWR models, respectively, reduced absolute errors by 3.5%, 31.5%, and 46.4% relative to a global ordinary least squares model. More impressively, the GTWR model demonstrated a better goodness-of-fit (0.9282) than the TWR model (0.7794) and the GWR model (0.8897). McNamara's test supported the hypothesis that the improvements made by GTWR over the TWR and GWR models are statistically significant for the sample data. Keywords: geographically and temporally weighted regressiongeographically weighted regressionspatial nonstationaritytemporal nonstationarityhousing priceCalgary Acknowledgments This research is funded by the Hong Kong Research Grants Council (RGC) under CERG project no. CUHK 444107 and the Natural Sciences and Engineering Research Council (NSERC) of Canada under discovery grant no. 312166-05. Their support is gratefully acknowledged. We also thank the two anonymous reviewers for their insightful comments that have been very helpful in improving this article.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- International Journal of Geographical Information Systems
- Topic
- Housing Market and Economics
- Field
- Economics, Econometrics and Finance
- Canadian institutions
- University of Calgary
- Funders
- Chinese University of Hong Kong
- Keywords
- Ordinary least squaresGeographically Weighted RegressionWeightingStatisticsSample (material)Regression analysisSpatial variabilityEconometricsGoodness of fitGeographyRegressionSpatial analysisCartographyComputer scienceMathematics
- Has abstract in OpenAlex
- yes