Where to improve cycling infrastructure? Assessing bicycle suitability and bikeability with open data in the city of Paris

This study proposes a method that can help in identifying potential locations for improvements of cycling infrastructures. It addresses the need for simple and effective methods to support decision-making in bicycle planning. The city of Paris is used as a case study area because it has made considerable efforts to improve cycling infrastructures and to become more bicycle-friendly in recent years. The method (1) identifies potential locations for improvements of bicycle infrastructures on a street level and (2) on a city level considering accessibility to important destinations. The main data used in this project is street data from OpenStreetMap (OSM) and cycling infrastructure data from the Atelier parisien d’urbanisme (Apur). The proposed method can be applied with commonly available data, has clear outcomes, is reproducible, and can be applied to different case study areas. We produced a map of bicycle suitability across all of Paris, and validated it for the 30 longest segments in the city with lower bike suitability. Our validation showed that combining OSM and Apur data led to a reliable dataset, with which we modelled bikeability using the underlying network overlain on a 250 m resolution grid and destinations representing leisure activities, education, shopping, city functions and public transport. The resulting map identifies regions of the city with poor bikeability, where improvements to cycling infrastructure should be investigated

Effects of traffic perturbations on bike sharing demand – a case study of public transport strikes and protests in Paris

This paper aims to contribute to a better understanding of the interactions between traffic perturbations and bike sharing use. More specifically we propose a framework for comparative spatial temporal analyses of public transport strikes and massive protests effects on bike sharing program in Paris. We find opposite effects on bike sharing demand due to public transport strikes and protests. The former causes a considerable rise in bike sharing demand particularly during the daily rush hours, while the latter precipitates a drop of activity constantly during the protest day. Our approach allows tracing bike sharing demand changes induced by traffic perturbations on an hourly level

Window Expeditions: A playful approach to crowdsourcing natural language descriptions of everyday lived landscapes

Measuring what citizens perceive and value about landscapes is important for landscape monitoring. Capturing temporal, spatial and cultural variation requires collection of data at scale. One potential proxy data source are textual descriptions of landscapes written by volunteers. We implemented a gamified application and crowdsourced a multilingual corpus of in-situ descriptions of everyday lived landscapes. Our implementation focused on the aesthetics of exploration, expression and fellowship in the mechanics, dynamics, aesthetics (MDA) framework. We collected 503 natural language landscape descriptions from 384 participants in English (69.7%), German (25.1%) and French (5.3%) and most contributions were made in urban areas (54.7%). The most frequent noun lemma in English was “tree” and in German “Fenster” (window). By comparing our English collection to corpora of everyday English and landscape descriptions, we identified frequent lemmas such as “tree”, “window”, “light”, “street”, “garden” and “sky” which occurred significantly more than expected. These terms hint as to important components of the everyday landscapes of our users. We suggest a number of ways in which our corpus could be used in ongoing research on landscapes, complementing existing PPGIS approaches, providing data for domain specific lexicons for landscape analysis and as an input to landscape character assessment

Identifying landscape relevant natural language using actively crowdsourced landscape descriptions and sentence-transformers

Natural language has proven to be a valuable source of data for various scientific inquiries including landscape perception and preference research. However, large high quality landscape relevant corpora are scare. We here propose and discuss a natural language processing workflow to identify landscape relevant documents in large collections of unstructured text. Using a small curated high quality collection of actively crowdsourced landscape descriptions we identify and extract similar documents from two different corpora (Geograph and WikiHow) using sentence-transformers and cosine similarity scores. We show that 1) sentence-transformers combined with cosine similarity calculations successfully identify similar documents in both Geograph and WikiHow effectively opening the door to the creation of new landscape specific corpora, 2) the proposed sentence-transformer approach outperforms traditional Term Frequency - Inverse Document Frequency based approaches and 3) the identified documents capture similar topics when compared to the original high quality collection. The presented workflow is transferable to various scientific disciplines in need of domain specific natural language corpora as underlying data

Characterising and mapping potential and experienced tranquillity : From a state of mind to a cultural ecosystem service

Funding Information: Many thanks to Graeme Willis (Campaign to Protect Rural England) and Nick Groome (Ordnance Survey) for their help in accessing the National Tranquillity Mapping Data. We would like to thank all the contributors to Geograph British Isles (Creative Commons Attribution-ShareAlike 2.5 License) whose contributions were used to map tranquil and silent locations in the Lake District.Peer reviewedPublisher PD

Investigating sense of place as a cultural ecosystem service in different landscapes through the lens of language

We are grateful for the comments and feedback of three anonymous reviewers. We thank Olga Chesnokova for her help in calculating cosine similarity measures. The research on which this paper is based was financially supported by the cogito foundation through the project ‘How language shapes our sense of place’, grant no. 15-129-R.Peer reviewedPublisher PD

‘This is not the jungle, this is my barbecho’ : semantics of ethnoecological landscape categories in the Bolivian Amazon

This work was supported from the ‘Forschungskredit’ by the University of Zurich [grant number FK-13-104]; Hans Vontobel Foundation; Maya Behn-Eschenburg Foundation; Ormella Foundation; and Parrotia Foundation.Peer reviewedPublisher PD

How is avalanche danger described in textual descriptions in avalanche forecasts in Switzerland? Consistency between forecasters and avalanche danger

Effective and efficient communication of expected avalanche conditions and danger to the public is of great importance, especially where the primary audience of forecasts are recreational, non-expert users. In Europe, avalanche danger is communicated using a pyramid, starting with ordinal levels of avalanche danger and progressing through avalanche-prone locations and avalanche problems to a danger description. In many forecast products, information relating to the trigger required to release an avalanche, the frequency or number of potential triggering spots, and the expected avalanche size is described exclusively in a textual danger description. These danger descriptions are, however, the least standardized part of avalanche forecasts. Taking the perspective of the avalanche forecaster and focusing particularly on terms describing these three characterizing elements of avalanche danger, we investigate first which meaning forecasters assign to the text characterizing these elements and second how these descriptions relate to the forecast danger level. We analyzed almost 6000 danger descriptions in avalanche forecasts published in Switzerland and written using a structured catalogue of phrases with a limited number of words. Words and phrases representing information describing these three elements were labeled and assigned to ordinal classes by Swiss avalanche forecasters. These classes were then related to avalanche danger. Forecasters were relatively consistent in assigning labels to words and phrases with Cohen's kappa values ranging from 0.64 to 0.87. Avalanche danger levels were also described consistently using words and phrases, with for example avalanche size classes increasing monotonically with avalanche danger. However, especially for danger level 2 (moderate), information about key elements of avalanche danger, for instance the frequency or number of potential triggering spots, was often missing in danger descriptions. In general, the analysis of the danger descriptions showed that extreme conditions are described in more detail than intermediate values, highlighting the difficulty of communicating conditions that are neither rare nor frequent or neither small nor large. Our results provide data-driven insights that could be used to refine the ways in which avalanche danger could be communicated. Furthermore, through the perspective of the semiotic triangle, relating a referent (the avalanche situation) through thought (the processing process) to symbols (the textual danger description), we provide an alternative starting point for future studies of avalanche forecast consistency and communication

Development and evaluation of a geographic information retrieval system using fine grained toponyms

Geographic information retrieval (GIR) is concerned with returning information in response to an information need, typically expressed in terms of a thematic and spatial component linked by a spatial relationship. However, evaluation initiatives have often failed to show significant differences between simple text baselines and more complex spatially enabled GIR approaches. We explore the effectiveness of three systems (a text baseline, spatial query expansion, and a full GIR system utilizing both text and spatial indexes) at retrieving documents from a corpus describing mountaineering expeditions, centred around fine grained toponyms. To allow evaluation, we use user generated content (UGC) in the form of metadata associated with individual articles to build a test collection of queries and judgments. The test collection allowed us to demonstrate that a GIR-based method significantly outperformed a text baseline for all but very specific queries associated with very small query radii. We argue that such approaches to test collection development have much to offer in the evaluation of GIR