International Conference on GIScience Short Paper Proceedings
Parent: Department of Geography
eScholarship stats: Breakdown by Item for April through July, 2026
| Item | Title | Total requests | Download | View-only | %Dnld |
|---|---|---|---|---|---|
| 67f0678p | Can we use OpenStreetMap POIs for the Evaluation of Urban Accessibility? | 295 | 125 | 170 | 42.4% |
| 7gc866qs | Time-Geography in Four Dimensions: Potential Path Volumes around 3D Trajectories | 221 | 57 | 164 | 25.8% |
| 729295dw | Spatiotemporal enabled Content-based Image Retrieval | 206 | 149 | 57 | 72.3% |
| 04b2d8xp | Identifying Local Spatiotemporal Autocorrelation Patterns of Taxi Pick-ups and Dropoffs | 185 | 94 | 91 | 50.8% |
| 9z90g3zd | A Function-based model of Place | 176 | 50 | 126 | 28.4% |
| 71h533kp | Fast Computation of Continental-Sized Isochrones | 173 | 58 | 115 | 33.5% |
| 0080r0n8 | Which Kobani? A Case Study on the Role of Spatial Statistics and Semantics for Coreference Resolution Across Gazetteers | 170 | 90 | 80 | 52.9% |
| 0tn5s4v9 | Deriving Locational Reference through Implicit Information Retrieval | 169 | 65 | 104 | 38.5% |
| 0wf6w9p9 | Tweet Geolocation Error Estimation | 168 | 68 | 100 | 40.5% |
| 1gr5f2c6 | Geospatial Internet of Things: Framework for fugitive Methane Gas Leaks Monitoring | 167 | 69 | 98 | 41.3% |
| 4hp830d6 | Outlier Detection in OpenStreetMap Data using the Random Forest Algorithm | 167 | 52 | 115 | 31.1% |
| 1g87v8dg | Refugee Spatial Awareness: Evidence from Za’atari | 165 | 64 | 101 | 38.8% |
| 9fs8s68v | Accessing Distributed WFS Data Through A RDF Query Interface | 154 | 67 | 87 | 43.5% |
| 5bp4f7gj | Comparing Geospatial Ontologies with Indigenous Conceptualizations of Time | 151 | 57 | 94 | 37.7% |
| 5w54k25v | Using dynamic geospatial ontologies to support information extraction from big Earth observation data sets | 150 | 41 | 109 | 27.3% |
| 8mf4r9rw | A Density-Based Spatial Flow Cluster Detection Method | 150 | 65 | 85 | 43.3% |
| 1b9432ds | What are the Probabilities of Land-Use Transitions? The Answer Depends on the Classification Method | 149 | 51 | 98 | 34.2% |
| 3ht0m7hh | A Field-Based Time Geography for Wildlife Movement Analysis | 148 | 75 | 73 | 50.7% |
| 8nh5943s | Scalability in Participatory Planning: A comparison of online PPGIS methods with faceto- face meetings | 147 | 48 | 99 | 32.7% |
| 0cq8c6dd | Understanding spatial patterns of biodiversity: How sensitive is phylogenetic endemism to the randomisation model? | 146 | 54 | 92 | 37.0% |
| 6z0905hw | A Multidirectional Optimal Ecotope-Based Algorithm to Delineate a Commuter Shed | 146 | 78 | 68 | 53.4% |
| 0qh4t98s | Local variation in hedonic house pricing in Hanoi, Vietnam: a spatial analysis of status quality trade-off (SQTO) theory | 145 | 55 | 90 | 37.9% |
| 8cz973mw | Semantically Refining the Groundwater Markup Language (GWML2) with the Help of a Reference Ontology | 142 | 71 | 71 | 50.0% |
| 01b1r10h | Multi-Scale Extraction of Regular Activity Patterns in Spatio-Temporal Events Databases: A Study Using Geolocated Tweets from Central Mexico | 140 | 74 | 66 | 52.9% |
| 1jb92687 | Using GPS-enabled mobile phones to characterize individuals’ activity patterns for epidemiology applications | 140 | 69 | 71 | 49.3% |
| 3hc8k3js | Walk and Learn: An Empirical Framework for Assessing Spatial Knowledge Acquisition during Mobile Map Use | 140 | 48 | 92 | 34.3% |
| 1wx3m2cd | Land Use Regression of Particulate Matter in Calgary, Canada | 139 | 61 | 78 | 43.9% |
| 4dw721gn | Machine Learning on Spark for the Optimal IDW-based Spatiotemporal Interpolation | 139 | 46 | 93 | 33.1% |
| 4z30b09d | Space-Time Topological Graphs | 139 | 45 | 94 | 32.4% |
| 5vm764rp | Constructing a Routable Transit Network from a Real-time Vehicle Location Feed | 138 | 58 | 80 | 42.0% |
| 8rh987cz | Measuring Distance “As the Horse Runs”: Cross-Scale Comparison of Terrain-Based Metrics | 138 | 53 | 85 | 38.4% |
| 0fn9v0q8 | Assessing Spatiotemporal Agreement between Multi-Temporal Built-up Land Layers and Integrated Cadastral and Building Data | 136 | 66 | 70 | 48.5% |
| 6mg271rn | A 3D Virtual Environment for Spatio-Temporal Analysis: Theoretical Approach, Proof of Concept, and User Study | 136 | 49 | 87 | 36.0% |
| 8jd35618 | Retrieving Indigenous Knowledge to a Digital Map: the Case of the Traditional Farming System in a Hñahñu (Otomí) Community, Mexico | 135 | 60 | 75 | 44.4% |
| 971896bp | Curating Transient Population in Urban Dynamics System | 135 | 69 | 66 | 51.1% |
| 8n57h6zv | Understanding the Mapping Sequence of Online Volunteers in Disaster Response | 133 | 74 | 59 | 55.6% |
| 8nq409qz | Searching for common ground (again) | 133 | 61 | 72 | 45.9% |
| 9hf8b2wb | A Dasymetric-Based Monte Carlo Simulation Approach to the Probabilistic Analysis of Spatial Variables | 133 | 57 | 76 | 42.9% |
| 4b58k9tp | Pedestrian Navigation Aids, Spatial Knowledge and Walkability | 132 | 60 | 72 | 45.5% |
| 6x0199bg | Location Optimization of Fire Stations: Trade-off between Accessibility and Service Coverage | 131 | 55 | 76 | 42.0% |
| 5640633b | Extracting Accurate Building Information from Off-Nadir VHR Images | 130 | 47 | 83 | 36.2% |
| 94g0c634 | An Algorithm for Empirically Informed Random Trajectory Generation Between Two Endpoints | 130 | 52 | 78 | 40.0% |
| 5854c2jv | High resolution, multi-year compatible dasymetric models of US population | 129 | 50 | 79 | 38.8% |
| 5hc4d2q6 | Characterizing Volunteered Geographic Information using Fuzzy Clustering | 127 | 52 | 75 | 40.9% |
| 9v31f3m8 | Multi-resolution, pattern-based segmentation of very large raster datasets | 127 | 54 | 73 | 42.5% |
| 26q142pp | The Role of Space in Remote Collaborations: An Exploration of Immersive Technology Attributes on Co-presence and Team Membership | 126 | 61 | 65 | 48.4% |
| 04t0t6ds | A Closer Examination of Spatial-Filter-Based Local Models | 125 | 49 | 76 | 39.2% |
| 6pw0r1dz | Crowd Sensing System for Public Participation | 125 | 56 | 69 | 44.8% |
| 8kv3n3bq | Semi-parametric Geographically Weighted Regression (S-GWR): a Case Study on Invasive Plant Species Distribution in Subtropical Nepal | 124 | 45 | 79 | 36.3% |
| 5wt35578 | Crowd-sorting: reducing bias in decision making through consensus generated crowdsourced spatial information | 123 | 51 | 72 | 41.5% |
Note: Due to the evolving nature of web traffic, the data presented here should be considered approximate and subject to revision. Learn more.