GIScience 2021 Short Paper Proceedings
Parent: Center for Spatial Studies
eScholarship stats: Breakdown by Item for September through December, 2024
| Item | Title | Total requests | Download | View-only | %Dnld |
|---|---|---|---|---|---|
| 0kb4z5hq | Embodied digital twins of forest environments | 63 | 11 | 52 | 17.5% |
| 60v7597c | Assessing Correlation Between Night-Time Light and Road Infrastructure: An Empirical Study | 59 | 19 | 40 | 32.2% |
| 7km7x3w1 | The influence of landmark visualization style on expert wayfinders' visual attention during a real-world navigation task | 58 | 5 | 53 | 8.6% |
| 9vv6j0m9 | Integrating XAI and GeoAI | 54 | 9 | 45 | 16.7% |
| 9cs309kd | Geo-Event Question Answering Systems: A Preliminary Research Study | 52 | 14 | 38 | 26.9% |
| 6q03b36x | Generalizing the Simple Linear Iterative Clustering (SLIC) superpixels | 45 | 7 | 38 | 15.6% |
| 62s7n79k | Geographically weighted regression for compositional data: An application to the U.S. household income compositions | 43 | 6 | 37 | 14.0% |
| 0kd9q103 | MapSpace: POI-based Multi-Scale Global Land Use Modeling | 39 | 4 | 35 | 10.3% |
| 41t46420 | Multiscale Geographically Weighted Discriminant Analysis | 38 | 5 | 33 | 13.2% |
| 8kg664zg | Stable geographically weighted Poisson regression for count data | 37 | 7 | 30 | 18.9% |
| 6tt8j58m | Varying salience in indoor landmark selection for familiar and unfamiliar wayfinders: evidence from machine learning and self-reports | 35 | 2 | 33 | 5.7% |
| 4bp4q4z3 | Testing Landmark Salience Prediction in Indoor Environments Based on Visual Information | 31 | 16 | 15 | 51.6% |
| 5dj756b5 | Simulating changing traffic flow caused by new bus route in Augsburg | 31 | 8 | 23 | 25.8% |
| 88c5p28w | A network for simulating pre-colonial migration in the Americas | 31 | 3 | 28 | 9.7% |
| 5016t2k9 | A novel method for mapping spatiotemporal structure of mobility patterns during the COVID-19 pandemic | 29 | 6 | 23 | 20.7% |
| 59t385np | Specifying multi-scale spatial heterogeneity in the rental housing market: The case of the Tokyo metropolitan area | 29 | 4 | 25 | 13.8% |
| 8dc7t93b | Understanding the use of greenspace before and during the COVID-19 pandemic by using mobile phone app data | 27 | 8 | 19 | 29.6% |
| 0x82c21d | Agent-based Line-of-Sight Simulation for safer Crossings | 26 | 3 | 23 | 11.5% |
| 89h883x4 | Improving pedestrians' spatial learning during landmark-based navigation with auditory emotional cues and narrative | 25 | 6 | 19 | 24.0% |
| 8t51k45t | Measuring Polycentricity: A Whole Graph Embedding Perspective | 25 | 4 | 21 | 16.0% |
| 1690j3zc | Examining geographical generalisation of machine learning models in urban analytics through street frontage classification and house price regression | 21 | 5 | 16 | 23.8% |
| 3wz9104b | The Virtual Reality of GIScience | 21 | 7 | 14 | 33.3% |
| 4575267v | Eco-friendly Routing based on real-time Air-quality Sensor Data from Vehicles | 21 | 7 | 14 | 33.3% |
| 4c09g6wt | Anonymization via Clustering of Locations in Road Networks | 20 | 3 | 17 | 15.0% |
| 4xj1008p | An Individual-Centered Approach for Geodemographic Classification | 18 | 4 | 14 | 22.2% |
| 5zt0p1ft | Urban Data Science for Sustainable Transport Policies in Emerging Economies | 18 | 0 | 18 | 0.0% |
| 3376341d | Segmentation of point-based geographic space | 17 | 1 | 16 | 5.9% |
| 4bs0z3mc | GIScience in Poland – Research, Education, Community | 17 | 4 | 13 | 23.5% |
| 65t7h04k | Spatio-temporal variability in Wikipedia content: The case of Greater London | 17 | 6 | 11 | 35.3% |
| 9pc4j56s | A pattern-based approach to analysis and visualization of spatio-racial distribution | 15 | 5 | 10 | 33.3% |
| 4n31h85w | Spatially-explicit forecasting of racial change | 10 | 1 | 9 | 10.0% |
Note: Due to the evolving nature of web traffic, the data presented here should be considered approximate and subject to revision. Learn more.