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«Urban Problems and sPatial methods VolUme 17, nUmber 1 • 2015 U.S. Department of Housing and Urban Development | Office of Policy Development and ...»

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Using Google Earth To Spatially Reference Sense of Place This section provides an example of how Google Earth can be used during the interview process to complement narrative ethnographic research and cognitive mapping exercises. Having Google Earth images during interviews enables the researcher to map and locate points discussed when

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asking participants to identify significant spaces and places in the community. Approaches will differ based on location and access, but researchers can either have printed images, which may limit discussions to the printed frame, or conduct interviews with Google Earth open to enable the interviewee to navigate and to help document and reference data. In the context presented previously, the objective of this approach is to consolidate and spatially reference meanings associated with social spaces and places, because such insight offers perspective into what is not always inherently visible. Interviews make experiences visible, because data gathered during the interview process are often not available otherwise. In general, interviews fill voids, and Google Earth becomes a tool to spatially reference interview data.

Visual reference points that spark cognitive memories during interviews help participants elaborate on past experiences and social activities—based on space and place. Participants may refer to positive experiences in particular spaces, where interactions and community building have occurred. Sometimes people relate to a particular incident or physical feature in the landscape. These memories can also be unsettling to participants, because they may identify an area that is off limits because of a crime or an area of the city or town that is associated with some negative connotation. Nevertheless, experiences are spatially referenced to exact site locations. This insight enables researchers to see (new) meanings significant to community and identity formation—or sense of place.

The pilot-study example was conducted in a rural community in the Dominican Republic. As noted, this method was tested as part of a wider ethnographic study in which the researcher spent one semester residing in Villa Ascension de Caraballo (hereafter, Villa Ascension) as a volunteer assisting with community development. Residing in a community and observing everyday life and activities enable researchers to elaborate and reflect on participants’ responses to add value to the meaning being communicated and to add supplemental depth. To gather a representative sample, participants were selected based on a range of age, gender, employment, and role in the community. Each participant involved was presented with a laminated Google Earth image of Villa Ascension and markers; they were asked to locate important community spaces on the map.

After the participants identified spaces and places on the laminated image, the interviewer used the marked map to guide the semistructured interview about the meanings of identified spaces and places in relation to their actual significance to the participants and the community. Exhibits 1 and 2 are digitized examples of locations identified or circled on the Google Earth transparencies.

Participants had the freedom to discuss experiences and relate to social activities in these spaces, offering insight into the making of places.

This approach represents a visual qualitative mapping method that engages participants with their local geography and adds meanings to the places where they reside. Researchers and planners can critically evaluate meanings that emerge to better understand everyday perceptions and uses of space. It is important to note specific visible features recognized by each participant and to complement insight from local community members with data collected from observations, queries, and conversations during research. Bringing together a wide range of data helps a researcher fill the void of what is not visible. Certain elements in an image may take on alternative meanings that should not be assumed, so for clarity the researcher must facilitate a discussion with each participant regarding why certain spaces or places were identified.

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Example 2 of Community Spaces Identified As Important in Villa Ascension de Caraballo, Dominican Republic As mentioned previously, placemarks, a Google Earth feature, enables the researcher to store data on specific spaces and places from interviews or observations in placemark textboxes. The researcher can also add images and links, as necessary; can edit data entered into each placemark by selecting “properties”; and can save data as.kmz files that can be edited at a later time. For the pilot study conducted in Villa Ascension, interviewees were asked to identify the five points in

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the community they deemed most significant to sense of place and sense of community. Exhibit 3 identifies all the points discussed by those who participated in this study. Exhibit 4 shows one of the points and the corresponding spatially referenced interview data. Each placemark has embedded latitude and longitude coordinates, which can then be easily spatially referenced in GIS. Using Exhibit 3 Google Earth Placemarks Identifying All Locations Identified As Important by Study Participants in Villa Ascension de Caraballo, Dominican Republic Exhibit 4 Example of Spatially Referenced Data From Participant Interviews in Villa Ascension de Caraballo, Dominican Republic

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digitizing commands, the researcher can then identify the points discussed by participants (see exhibit 3) and add supplemental data from the interview to an attribute table (exhibit 4). Entering qualitative data into Google Earth or GIS is an efficient way to organize and spatially reference interviews or photographs collected to inform the analysis and assess similarities or differences in understanding spaces and place.

Concluding Remarks The data entered into Google Earth placemarks are useful for academic researchers to engage with the meanings embedded in significant spaces and places identified by members of a local community. Such data are also useful for planners who are seeking insight into the effect of new community buildings, parks, or spaces based on location. Google Earth is a tool for storing and spatially referencing qualitative data collected in the field as a means for understanding particular spaces and places. The wider purpose of this method and approach is to produce and store new local knowledge from community participants to consult, or inform, when planning new projects.

This article not only is relevant to understanding people’s perceptions of place and community in urban areas but also offers insight into how to strategically plan for and promote community development by enabling participants to spatially reference their experiences. Using easily and readily accessible technologies such as Google Earth encourages researchers to fully develop practical understandings of spatial interactions and to georeference meanings in actual locations. Moreover, Google Earth promotes the underlying spatial emphasis of this work to gather, identify, and locate data to make sense of place more visible and spatially informed, which makes it relevant to social science researchers and community planners.

Author Nicholas Wise is a lecturer in international sport, events, and tourism management in the Glasgow School for Business and Society at Glasgow Caledonian University.

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Bender, Oliver, Hans Juergen Boehmer, Doreen Jens, and Kim Philip Schumacher. 2005. “Using GIS To Analyze Long-Term Cultural Landscape Change in Southern Germany,” Landscape and Urban Planning 70 (1–2): 111–125.

Brunn, Stanley, and Matthew W. Wilson. 2013. “Cape Town’s Million Plus Black Township to Khayelitsha: Terrae Incognitae and the Geographies and Cartographies of Silence,” Habitat International 39: 284–294.

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Cope, Megan, and Sarah Elwood, eds. 2009. Qualitative GIS: A Mixed Methods Approach. London, United Kingdom: SAGE Publications.

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Kennedy, David, and M.C. Bishop. 2011. “Google Earth and the Archaeology of Saudi Arabia:

A Case Study From the Jeddah Area,” Journal of Archeological Science 38: 1284–1293.

Kusek, Weronika, and Nicholas Wise. 2014. “Artistic Perceptions of Poles in London,” Scottish Geographical Journal 130 (2): 99–115.

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Watson, Annette, and Karen E. Till. 2010. “Ethnography and Participant Observation.” In The SAGE Handbook of Qualitative Geography, edited by Dydia DeLyser, Steve Herbert, Stuart Aitken, Mike Crang, and Linda McDowell. London, United Kingdom: SAGE Publications: 121–137.

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“Layers of the Landscape: Representations and Perceptions of an Ordinary (Shared) Sports Landscape in a Haitian and Dominican Community,” Geographical Research 52 (2):

212–222.

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150 Urban Problems and Spatial Methods

Small Stories in Big Data:

Gaining Insights From Large Spatial Point Pattern Datasets Ate Poorthuis Matthew Zook University of Kentucky Abstract With the onset of big data, it is now relatively easy to gain access to a wide variety and great magnitude of data sources. Data, however, do not necessarily equate to useful insights and meaningful analysis. In this article, we outline a specific step-by-step approach to gaining insight into the spatial footprint of online, point-based data—in this particular case, data from the popular social media service Twitter.

Introduction A key aspect of current research directions in urban studies is that researchers are inundated by both a flurry of “big” datasets and persistent writing about the importance of that data deluge.

From cellphone records to open government datasets and online social media shared using application programming interfaces (APIs), the topic of big data—both in terms of possible applications and critique—is ever more present.

The relative ease with which a researcher can gain access to a variety of new data sources, however, does not necessarily mean that insights from those data can be achieved as easily. Putting aside key questions of what an indicator actually measures (an issue present in various forms across datasets), researchers are also confronted with the fact that many trusted analytical and mapping methods cannot be directly applied in standard ways to spatial big data. To partly alleviate this issue, we outline a step-by-step approach to gaining insight into the spatial footprint of online big data—in this particular case, the popular social media service Twitter. As with most geosocial online data (for example, Foursquare check-ins, geotagged Flickr photos), tweets are stored as spatial points with a longitude and latitude coordinate pair and a variety of other metadata (timestamp, text, images, activity records, etc.) that can be leveraged to gain useful insight on the spatial distribu

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tion of daily life (Poorthuis and Zook, 2014). It should be noted that the approach outlined in this article would also be applicable to large spatial point pattern datasets generated from other, more traditional, sources.

So Much Data… Now What?

In principle, anyone with an interest in geosocial online data and some programming skills can access a wide range of APIs made available by almost every major social media platform. Although this article does not address the techniques for API access, tutorials and code are widely available.



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