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Geo-Questionnaire: A Spatially Explicit Method for Eliciting Public Preferences, Behavioural Patterns, and Local Knowledge – An Overview

Michał Czepkiewicz
Michał Czepkiewicz
, 
Piotr Jankowski
Piotr Jankowski
 y 
Zbigniew Zwoliński
Zbigniew Zwoliński
   | 06 sept 2018
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Publicado en línea: 06 sept 2018
Páginas: 177 - 190
Recibido: 12 dic 2017
DOI: https://doi.org/10.2478/quageo-2018-0033
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© 2018 Michał Czepkiewicz et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Fig. 1

Example page of a geo-questionnaire used in Kasprowicz Park case study in Poznan, Poland, described in section 6. The mapping tools on the page allow respondents to sketch polygons representing their development preferences and answer questions about preference details. The geo-questionnaire interface allows the respondents to toggle between a satellite image and a street map, pan and zoom.
Example page of a geo-questionnaire used in Kasprowicz Park case study in Poznan, Poland, described in section 6. The mapping tools on the page allow respondents to sketch polygons representing their development preferences and answer questions about preference details. The geo-questionnaire interface allows the respondents to toggle between a satellite image and a street map, pan and zoom.

Fig. 2

Aggregated and individual analysis of point markings. The markings represent places evaluated negatively in respect to mobility in downhall Poznań. Individual point markings were aggregated with kernel density estimation using 5 m grid cell size and 100 m search radius. Then, clusters were delineated using a density threshold value, and individual answers to open-ended questions were pooled to summarize problems and solution ideas related to each cluster. Sources: topographic data © OpenStreetMap contributors; thematic data collected in the Local Needs Map case study.
Aggregated and individual analysis of point markings. The markings represent places evaluated negatively in respect to mobility in downhall Poznań. Individual point markings were aggregated with kernel density estimation using 5 m grid cell size and 100 m search radius. Then, clusters were delineated using a density threshold value, and individual answers to open-ended questions were pooled to summarize problems and solution ideas related to each cluster. Sources: topographic data © OpenStreetMap contributors; thematic data collected in the Local Needs Map case study.

Fig. 3

Aggregated analysis of polygon markings. The polygons represent areas that, according to the geo-questionnaire respondents, should change its function to sport and recreation facilities. The map frame on the left presents raw polygon features as sketched by participants. The map frame on the right presents an aggregated map, in which the raster cell values represent the number of polygons overlapping each cell. Sources: topographic data © OpenStreetMap contributors; thematic data collected in the Kasprowicz Park case study.
Aggregated analysis of polygon markings. The polygons represent areas that, according to the geo-questionnaire respondents, should change its function to sport and recreation facilities. The map frame on the left presents raw polygon features as sketched by participants. The map frame on the right presents an aggregated map, in which the raster cell values represent the number of polygons overlapping each cell. Sources: topographic data © OpenStreetMap contributors; thematic data collected in the Kasprowicz Park case study.

Fig. 4

Agreement-disagreement map. The map was created by calculating the difference between two aggregate maps with polygons representing preferences: one in favour of the preservation of current function, and the other in favor of function change. Negative values on the map represent areas where views in favour of function change dominate, positive values represent areas where views in favour of function preservation dominate. Sources: topographic data © OpenStreetMap contributors; thematic data collected in the Kasprowicz Park case study.
Agreement-disagreement map. The map was created by calculating the difference between two aggregate maps with polygons representing preferences: one in favour of the preservation of current function, and the other in favor of function change. Negative values on the map represent areas where views in favour of function change dominate, positive values represent areas where views in favour of function preservation dominate. Sources: topographic data © OpenStreetMap contributors; thematic data collected in the Kasprowicz Park case study.

Description of selected geo-questionnaire cases in Poland.

CaseCityYearTopicParticipantsRecipients
Kasprowicz ParkPoznań (population 545,000)2015Local urban planninglocal area residents and users (N = 1,009)Poznań Urban Planning Office
Local needs mapPoznań (population 545,000)2016Downtown urban renewalDowntown residents and users (N = 709)Poznań City Hall, Poznań neighborhood councils
Sustainable public transportation modelŁódź (population 701,000)2016Public transportation planningPublic transportation users (N = 2,387)Łódź City Hall
New Rokietnica centreRokietnica (population 5,500)2016Suburban town planningRokietnica residents (N = 435)Rokietnica Municipal Office, land use plan designer
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Geosciences, Geography
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