International Conference on GIScience Short Paper Proceedings
Parent: Department of Geography
eScholarship stats: Breakdown by Item for December, 2024 through March, 2025
| Item | Title | Total requests | Download | View-only | %Dnld |
|---|---|---|---|---|---|
| 8nh5943s | Scalability in Participatory Planning: A comparison of online PPGIS methods with faceto- face meetings | 132 | 9 | 123 | 6.8% |
| 71h533kp | Fast Computation of Continental-Sized Isochrones | 115 | 7 | 108 | 6.1% |
| 6x0199bg | Location Optimization of Fire Stations: Trade-off between Accessibility and Service Coverage | 98 | 17 | 81 | 17.3% |
| 4hp830d6 | Outlier Detection in OpenStreetMap Data using the Random Forest Algorithm | 96 | 12 | 84 | 12.5% |
| 1g87v8dg | Refugee Spatial Awareness: Evidence from Za’atari | 88 | 6 | 82 | 6.8% |
| 0dk1t0vc | TerraEx – a GeoWeb app for world-wide content-based search and distribution of elevation and landforms data | 81 | 11 | 70 | 13.6% |
| 67f0678p | Can we use OpenStreetMap POIs for the Evaluation of Urban Accessibility? | 73 | 29 | 44 | 39.7% |
| 04b2d8xp | Identifying Local Spatiotemporal Autocorrelation Patterns of Taxi Pick-ups and Dropoffs | 68 | 17 | 51 | 25.0% |
| 5bp4f7gj | Comparing Geospatial Ontologies with Indigenous Conceptualizations of Time | 57 | 13 | 44 | 22.8% |
| 9z90g3zd | A Function-based model of Place | 57 | 15 | 42 | 26.3% |
| 8nq409qz | Searching for common ground (again) | 56 | 8 | 48 | 14.3% |
| 3hc8k3js | Walk and Learn: An Empirical Framework for Assessing Spatial Knowledge Acquisition during Mobile Map Use | 52 | 7 | 45 | 13.5% |
| 0cq8c6dd | Understanding spatial patterns of biodiversity: How sensitive is phylogenetic endemism to the randomisation model? | 51 | 11 | 40 | 21.6% |
| 1wx3m2cd | Land Use Regression of Particulate Matter in Calgary, Canada | 50 | 9 | 41 | 18.0% |
| 8ff033ht | An Ontological Analysis of Water Features | 49 | 7 | 42 | 14.3% |
| 4b58k9tp | Pedestrian Navigation Aids, Spatial Knowledge and Walkability | 48 | 11 | 37 | 22.9% |
| 54n7c1z2 | Deriving Hospital Catchment Areas from Mobile Phone Data | 48 | 11 | 37 | 22.9% |
| 8mf4r9rw | A Density-Based Spatial Flow Cluster Detection Method | 48 | 14 | 34 | 29.2% |
| 7gc866qs | Time-Geography in Four Dimensions: Potential Path Volumes around 3D Trajectories | 47 | 9 | 38 | 19.1% |
| 4dw721gn | Machine Learning on Spark for the Optimal IDW-based Spatiotemporal Interpolation | 46 | 6 | 40 | 13.0% |
| 46p8p31g | The Life Cycle of Volunteered Geographic Information (VGI) Contributors: the OpenStreetMap Example | 45 | 5 | 40 | 11.1% |
| 05h6f9b1 | An Object Based Approach for Submarine Canyon Identification from Surface Networks | 43 | 7 | 36 | 16.3% |
| 4824r3wg | Hybrid Indexing for Parallel Analysis of Spatiotemporal Point Patterns | 42 | 10 | 32 | 23.8% |
| 5640633b | Extracting Accurate Building Information from Off-Nadir VHR Images | 41 | 8 | 33 | 19.5% |
| 6928w12f | The Cognitive Aspect of Place Properties | 41 | 6 | 35 | 14.6% |
| 8rh987cz | Measuring Distance “As the Horse Runs”: Cross-Scale Comparison of Terrain-Based Metrics | 41 | 10 | 31 | 24.4% |
| 9w316472 | Modeling Place as a Relationship between a Person and a Location | 40 | 10 | 30 | 25.0% |
| 0080r0n8 | Which Kobani? A Case Study on the Role of Spatial Statistics and Semantics for Coreference Resolution Across Gazetteers | 39 | 8 | 31 | 20.5% |
| 6dd1m0j2 | Stress Supports Spatial Knowledge Acquisition during Wayfinding with Mobile Maps | 39 | 10 | 29 | 25.6% |
| 9hf8b2wb | A Dasymetric-Based Monte Carlo Simulation Approach to the Probabilistic Analysis of Spatial Variables | 39 | 11 | 28 | 28.2% |
| 0qh4t98s | Local variation in hedonic house pricing in Hanoi, Vietnam: a spatial analysis of status quality trade-off (SQTO) theory | 38 | 12 | 26 | 31.6% |
| 6mg271rn | A 3D Virtual Environment for Spatio-Temporal Analysis: Theoretical Approach, Proof of Concept, and User Study | 38 | 7 | 31 | 18.4% |
| 8kv3n3bq | Semi-parametric Geographically Weighted Regression (S-GWR): a Case Study on Invasive Plant Species Distribution in Subtropical Nepal | 38 | 8 | 30 | 21.1% |
| 94g0c634 | An Algorithm for Empirically Informed Random Trajectory Generation Between Two Endpoints | 38 | 9 | 29 | 23.7% |
| 1b9432ds | What are the Probabilities of Land-Use Transitions? The Answer Depends on the Classification Method | 37 | 7 | 30 | 18.9% |
| 76z9g4vb | Deserts in the Deluge: TerraPopulus and Big Human-Environment Data | 37 | 8 | 29 | 21.6% |
| 17m4482r | Context-sensitive spatiotemporal simulation model for movement | 36 | 9 | 27 | 25.0% |
| 1bw2c801 | Characterizing place: an empirical comparison between user-generated content and freelisting data | 36 | 8 | 28 | 22.2% |
| 5vm764rp | Constructing a Routable Transit Network from a Real-time Vehicle Location Feed | 36 | 11 | 25 | 30.6% |
| 1gr5f2c6 | Geospatial Internet of Things: Framework for fugitive Methane Gas Leaks Monitoring | 35 | 9 | 26 | 25.7% |
| 4zk9g37h | Uber vs. Taxis: Event detection and differentiation in New York City | 35 | 7 | 28 | 20.0% |
| 6k02850k | A Data-Driven Approach for Detecting and Quantifying Modeling Biases in Geo- Ontologies by Using a Discrepancy Index | 35 | 9 | 26 | 25.7% |
| 8jd35618 | Retrieving Indigenous Knowledge to a Digital Map: the Case of the Traditional Farming System in a Hñahñu (Otomí) Community, Mexico | 35 | 10 | 25 | 28.6% |
| 8wg042kw | Preface | 35 | 2 | 33 | 5.7% |
| 0fn9v0q8 | Assessing Spatiotemporal Agreement between Multi-Temporal Built-up Land Layers and Integrated Cadastral and Building Data | 34 | 7 | 27 | 20.6% |
| 2913r4x8 | A spatial mixture model to account for risk discontinuities: Analyzing attempted suicide in Waterloo Region, Ontario | 34 | 12 | 22 | 35.3% |
| 3969c18v | Predicting Influenza Dynamics using a Deep Learning Approach | 34 | 4 | 30 | 11.8% |
| 52g534d3 | Comparing digital traces of modern travellers to journeys of two 18th-19th century British poets | 34 | 7 | 27 | 20.6% |
| 971896bp | Curating Transient Population in Urban Dynamics System | 34 | 5 | 29 | 14.7% |
| 5wt35578 | Crowd-sorting: reducing bias in decision making through consensus generated crowdsourced spatial information | 33 | 4 | 29 | 12.1% |
Note: Due to the evolving nature of web traffic, the data presented here should be considered approximate and subject to revision. Learn more.