Social–ecological hotspots mapping: A spatial approach for identifying coupled social–ecological space

Alessa, Lilian; Kliskey, Andrew; Brown, Greg

doi:10.1016/j.landurbplan.2007.09.007

Cited by 329 publications

(197 citation statements)

References 31 publications

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“…We used the symbols HH for areas with local means higher than the global mean; LL for areas with local means lower than the global mean; HL for areas with values higher than the local mean and; LH for areas with values lower than the local mean (Anselin, Syabri, & Youngihn, 2006;Mitchell, 2005). Relationship with social factors Stray cats depend on anthropogenic food sources and may also be former domestic pets with strong dependence on humans; hence it was deemed valuable to investigate relationships between social factors and the presence of stray cats in the different areas (Alessa, Kliskey, & Brown, 2008, Levinthal, 2010. The New Zealand Deprivation Index or NZDep2006 was used as the determining variable because the data reflects social conditions of the entire country and the data is at the mesh block level, the smallest official census polygons that that make up the polygon or area used in geocoding.…”

Section: Global and Local Clusteringmentioning

confidence: 99%

Stray cats in Auckland, New Zealand: Discovering geographic information for exploratory spatial analysis

Aguilar

Farnworth

2012

Applied Geography

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Stray cats are a common feature of urban landscapes and are associated with issues of animal welfare and negative environmental impacts. Management, planning and decision-making require readily accessible information on stray cats. However, much of the existing data is not immediately useful for a geographic information system (GIS) in terms of format, content and explicit location information. Spreadsheets we obtained from a single large shelter in the Auckland region. They contained records of stray cat pickups and admissions for an entire year (n = 8573) of which 56.4% (n = 4834) contained data that could be processed to derive relevant spatial information. The resulting data consisted of identified roads and areas of Auckland where the stray cats were found. Published census databases and shapefiles were matched with the data to build a GIS of stray cats. Global and local regression analysis was employed to discover spatial distribution characteristics including the identification of areas with relatively high and low concentrations of stray cats and to explore relationships between socioeconomic condition and stray cat density. Significant clustering is more evident in South Auckland than elsewhere in the region. Specific geographical information is valuable, not only for understanding population dynamics of stray cats, but also to allow spatial and temporal targeting of resources to minimise their impact and promote responsible ownership.

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Section: Global and Local Clusteringmentioning

confidence: 99%

Stray cats in Auckland, New Zealand: Discovering geographic information for exploratory spatial analysis

Aguilar

Farnworth

2012

Applied Geography

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“…For example, value typologies have been variously described as landscape values (Alessa et al, 2008;Brown, 2005;Zhu, et al, 2010), landscape services (Fagerholm et al, 2012), place values (Brown and Reed, 2012), community values (Raymond et al, 2009), social values for ecosystem services (Sherrouse et al, 2011;van Riper et al, 2012), or simply social values (Bryan et al, 2010). In practice, participatory mapping typologies disproportionately contain values associated with cultural ecosystem services as described in the MEA (2005).…”

Section: Introductionmentioning

confidence: 99%

Physical landscape associations with mapped ecosystem values with implications for spatial value transfer: An empirical study from Norway

2015

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“…The less productive (53.88% of all non-productive grasslands by the results of the SolVES, 48.00% by the results of the RegMod) and non-productive grasslands show high relevant capacity (41.18% of all non-productive grasslands by the results of the SolVES, 54.40% by the results of the RegMod) to provide outdoor recreation activities, which creates a new view of their management as well as their use. The cultural services (for recreation) potential map can also assist in procedures such as hotspot identification, indicating important areas that might require special attention by managers (Reed & Brown 2003;Brown et al 2005Brown et al , 2006Alessa et al 2008). Without information on the factors influencing the quantity and value of the ecosystem services, it is difficult to design policies, incentives or payment schemes that can optimize the delivery of these services (Nelson et al 2009).…”

Section: Resultsmentioning

confidence: 99%

An approach to mapping the potential of cultural agroecosystem services

Makovníková¹,

Kobza²,

Pálka³

et al. 2016

Soil & Water Res.

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Makovníková J., Kobza J., Pálka B., Mališ J., Kanianska R., Kizeková M. (2016): An approach to mapping the potential of cultural agroecosystem services. Soil & Water Res., 11: 44-52.A system for evaluating outdoor recreation as a cultural agroecosystem service is presented. Every agroecosystem presumably has the potential for providing some kind of outdoor recreation. Two approaches to mapping the recreation potential were used and compared -the Social Values for Ecosystem Services (SolVES) model and the regional model (RegMOD). From the possibilities of recreation activities, hiking, biking, and cross-country skiing were chosen. The comparison of the two approaches showed that the RegMOD incorporates a wider range of categories than the SolVES model, particularly for hiking. The robust character of the SolVES model is reflected by narrowing the spectrum of categories of this recreation activity. The differences in the map view are marked in the case of biking and cross-country skiing. Overall, the grasslands of the study area in Slovakia offer mainly medium relevant capacity (53.90% by the results of the SolVES, 64.90% by the results of the RegMOD) for providing selected outdoor recreation activities. The less productive (53.88% of all non-productive grasslands by the results of the SolVES, 48.00% by the results of the RegMOD) and non-productive grasslands represent a higher relevant capacity (41.18% of all non-productive grasslands by the results of the SolVES, 54.40% by the results of the RegMOD) for providing outdoor recreation activities. This brings about a new view of their management as well as use. The RegMOD developed in this paper is replicable and could be applied by managers mainly at the regional level on condition of their proficiency in geographical information systems.

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Social–ecological hotspots mapping: A spatial approach for identifying coupled social–ecological space

Cited by 329 publications

References 31 publications

Stray cats in Auckland, New Zealand: Discovering geographic information for exploratory spatial analysis

Stray cats in Auckland, New Zealand: Discovering geographic information for exploratory spatial analysis

Physical landscape associations with mapped ecosystem values with implications for spatial value transfer: An empirical study from Norway

An approach to mapping the potential of cultural agroecosystem services

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