{"id":1000478,"date":"2026-01-01T00:37:27","date_gmt":"2026-01-01T05:37:27","guid":{"rendered":"https:\/\/sites.temple.edu\/yangyang\/?page_id=1000478"},"modified":"2026-01-02T18:41:58","modified_gmt":"2026-01-02T23:41:58","slug":"spatial","status":"publish","type":"page","link":"https:\/\/sites.temple.edu\/yangyang\/spatial\/","title":{"rendered":"Spatial Modeling"},"content":{"rendered":"\t\t
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Spatial Modeling<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Why is this method important?<\/b><\/div>
Tourism is inherently spatial, involving the movement of people across geographic landscapes. Geospatial analysis, often facilitated by Geographic Information Systems (GIS), is vital for visualizing these flows, identifying spatial clusters (hotspots), and understanding how location affects tourism performance. It allows for the measurement of spatial spillover effects\u2014where events in one location impact neighboring areas\u2014and supports site selection and infrastructure planning by quantifying accessibility and agglomeration.<\/div>
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What has our team done so far?\u00a0<\/b><\/div>
Our research has heavily utilized Exploratory Spatial Data Analysis (ESDA), including Global and Local Moran\u2019s I, to detect spatial autocorrelation and identify “hot-spots” of hotel distribution and tourist flows in China. We have developed advanced spatial metrics, such as the Tourism Spillover Index (TSI), to quantify the benefits regions receive from multidestination travel routes,. At a micro-level, our team has used open GPS-trajectory data and Markov chains to classify tourist movement patterns (e.g., “day-climbing” vs. “night-climbing”) within scenic areas. Additionally, we applied the Empirical Orthogonal Function (EOF) to Wi-Fi probe data to characterize space-time tourist flow patterns in community-based tourism. We also employed spatial econometric tools, such as Geographically Weighted Regression (GWR) and Multiscale GWR (MGWR), to analyze spatial heterogeneity in regional tourism growth and Airbnb pricing.<\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Recommendation <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Book Chapter:<\/p>

Spatial Analytics and Data Visualization<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Related Publications (click for all related publication)<\/a><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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