Publications

@article{Zhang2024,

title = {Geoprivacy knowledge, attitudes, and behaviours in contemporary China},

author = {Hongyu Zhang and Grant McKenzie},

url = {https://grantmckenzie.com/academics/Zhang2024.pdf

https://www.tandfonline.com/doi/full/10.1080/00167428.2024.2422873},

doi = {10.1080/00167428.2024.2422873},

year  = {2024},

date = {2024-11-14},

urldate = {2024-11-14},

journal = {Geographical Review},

publisher = {Taylor & Francis},

abstract = {China has an internet penetration rate of over 70% and a massive user base of social media. However, the topic of privacy attitudes among Chinese individuals remains understudied. We analyzed geoprivacy concerns in China through an online survey and regression analysis. Our findings suggest a positive relation among privacy knowledge, attitude, and behaviour, consistent with related literature. Declarative knowledge (e.g., privacy rights), on the other hand, was found to have a negative relation with privacy concerns, which has not been reported previously. In terms of demographic moderators, females had less privacy knowledge but more privacy protection behaviours, while the impact of age on privacy concerns was inconclusive. A notable discovery was the regional difference in privacy concerns within China, suggesting the potential geopolitical influence on individuals' values and beliefs. Combined with the uncovering of behavioural change in response to involuntary location disclosure, the results of this article challenge the conventional notion that Chinese individuals are indifferent to their online privacy, thus re-introducing an under-explored perspective from the global south into geoprivacy studies. },

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@article{nokey,

title = {Navigating the Post-Pandemic Urban Landscape: Disparities in Transportation Recovery & Regional Insights from New York City},

author = {Dan Qiang and Grant McKenzie},

url = {https://grantmckenzie.com/academics/Qiang_2024.pdf},

doi = {10.1016/j.compenvurbsys.2024.102111},

year  = {2024},

date = {2024-04-06},

journal = {Computers, Environment and Urban Systems},

volume = {110},

pages = {102111},

abstract = {The onset of the global Covid-19 pandemic in early 2020 brought many transportation systems in North America to a standstill. As life returned to normal, various modes of transportation exhibited differing rates of recovery, with disparities across regions. Limited research has delved into the regional variations in the recovery of these modes of transit over the past years. Such analysis is crucial for gaining insights into urban recovery and resilience, as well as understanding the factors influencing such recovery. In this work, we investigate the usage recovery of taxis, ride-hailing services, and subway ridership following the Covid-19 pandemic. We focus on New York City as our case study, employing clustering techniques to identify neighborhoods with similar recovery patterns. Furthermore, we examine the socio-economic, demographic, and built-environment factors contributing to regional variations in this recovery. Our research findings reveal that different modes of transportation responded differently to the pandemic, and these responses exhibited regional disparities. These findings hold significance for future health-related emergency response strategies and the regulation of existing transportation infrastructure.

},

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@article{Verma2024,

title = {Regional Comparison of Socio-Demographic Variation in Urban E-scooter Usage},

author = {Priyanka Verma and Grant McKenzie},

url = {https://grantmckenzie.com/academics/Verma_2024.pdf},

year  = {2024},

date = {2024-03-26},

urldate = {2024-03-09},

journal = {Environment and Planning B: Urban Analytics and City Science},

abstract = {In recent years we have witnessed explosive growth in the shared, free-floating, electric scooter industry. While still controversial in many North American cities, a number of large e-scooter operators have managed to carve out a piece of the urban transportation landscape. As these vehicles shift from novelty services to increasingly reliable modes of short personal travel, the discussion has turned to investigating who exactly benefits from these micromobility services and who are being left behind. Though population surveys have been administered to identify the socio-demographic characteristics of e-scooter riders in the past, little work has linked these characteristics through trips, or investigated the regional variation in these demographic factors. In this work we explore the variability and similarities in e-scooter rider characteristics across three major U.S. cities. To accomplish this, we apply a Moran’s Eigenvector Spatial Filtering linear regression model and compare our results to more commonly used spatial regression approaches. Our results indicate that the spatial filtering approach outperforms other methods in identifying socio-demographic characteristics of e-scooter users, across multiple regions. We find that many socio-demographics associated with e-scooter usage are regionally variant, despite younger users making up the core user base in all cities. There are variations in usage based on gender, income, and race across cities with Black and Hispanic populations remaining underserved. The implications of these findings are discussed.},

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@article{McKenzie2023,

title = {MixMap: A user-driven approach to place-based semantic similarity},

author = {Grant McKenzie and Sarah Battersby and Vidya Setlur},

doi = {10.1080/15230406.2023.2176930},

year  = {2023},

date = {2023-03-02},

urldate = {2023-03-02},

journal = {Cartography and Geographic Information Science},

pages = {1-16},

publisher = {Taylor & Francis},

abstract = {What other locations are like my neighborhood? How? Why? The heart of many spatial analyses is in finding similarities or dissimilarities between locations. Discovering patterns and interpreting similarity is a complicated process that is based on both the spatial characteristics and the semantics or meaning that we assign to place. Human conceptualization of similarity in locations is multi-faceted and cannot be captured with a simple assessment of single numeric attributes like population density or median income; however, these quantifiable attributes are the basis for an initial pass of sense-making. MixMap facilitates the incorporation of similarity measures and spatial analytics to provide an information reduction (or semantic generalization) that brings the user closer to actionable insights. Through a preliminary evaluation of MixMap, we found that the tool supports the geospatial inquiry of determining similarity between regions, where participants can manipulate individual weights of the various attributes describing these locations. Based on feedback and observations from the study, we discuss potential implications and considerations for exploring the role of context and additional place-specific parameters for computing similarity, as well as understanding the nuances of semantics for place similarity in geospatial analysis tools.},

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@article{Romm2022,

title = {Differences in First-Mile and Last-Mile Behaviour in Candidate Multi-Modal Boston Bike-share Micromobility Trips},

author = {Daniel Romm and Priyanka Verma and Elizabeth Karpinski and Tracy Sanders and Grant McKenzie},

url = {https://doi.org/10.1016/j.jtrangeo.2022.103370},

year  = {2022},

date = {2022-06-07},

urldate = {2022-06-07},

journal = {Journal of Transport Geography},

volume = {102},

issue = {June},

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pubstate = {published},

tppubtype = {article}

}

@article{McKenzie2022,

title = {PrivyTo: A privacy preserving location sharing platform},

author = {Grant McKenzie and Daniel Romm and Hongyu Zhang and Mikael Brunila},

url = {https://grantmckenzie.com/academics/McKenzie2022_PrivyTo.pdf

https://onlinelibrary.wiley.com/doi/full/10.1111/tgis.12924},

doi = {10.1111/tgis.12924},

year  = {2022},

date = {2022-05-12},

urldate = {2022-03-11},

journal = {Transactions in GIS},

publisher = {Wiley Press},

abstract = {Concern over the privacy of one's personal location is at an all-time high, yet the desire to share our lives with friends, family, and the public persists. Current methods and applications for sharing location content with the range of people in our lives are sorely lacking.  Application users are often limited to sharing a single spatial resolution with all individuals, regardless of relation, and with little control over how this content is shared.  Processes for sharing typically involve allowing a for-profit company access to your location before it can be transmitted to the intended recipient.  In this work we propose a set of design goals and a design pattern for sharing personal location information that are realized through a prototype mobile web application.  Our approach is built on the novel idea of obfuscated and encrypted location views, and promotes a uniquely open method for sharing.  The intention of this paper is to demonstrate that location sharing need not require one to expose private location information to third parties, and that methods exist to put an individual back in control of their content.},

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@article{Zhang2022,

title = {Rehumanize geoprivacy: From disclosure control to human perception},

author = {Hongyu Zhang and Grant McKenzie},

doi = {https://doi.org/10.1007/s10708-022-10598-4},

year  = {2022},

date = {2022-02-16},

urldate = {2022-01-31},

journal = {GeoJournal},

publisher = {Springer},

abstract = {Traditional boundaries between people are vanishing due to the rise of Internet of Things technology. Our smart devices keep us connected to the world, but also monitor our daily lives through an unprecedented amount data collection. As a result, defining privacy has become more complicated. Individuals want to leverage new technology (e.g., making friends through sharing private experiences) and also avoid unwanted consequences (e.g., targeted advertising).  In the age of ubiquitous digital content, geoprivacy is unique because concerns in this area are constantly changing and context-dependent. Multiple factors influence people’s location disclosure decisions, including time, culture, demographics, spatial granularity, and trust. Existing research primarily focuses on the computational efforts of protecting  geoprivacy, while the variation of geoprivacy perceptions has yet to receive adequate attention in the data science literature. In this work, we explore geoprivacy from a cognate-based perspective and tackle our changing perception of the concept from multiple angles. Our objectives are to rehumanize this field from contextual, cultural, and economic dimensions and highlight the uniqueness of geodata under the broad topic of privacy. It is essential that we understand the spatial variations of geoprivacy perceptions in the era of big data. Masking geographic coordinates can no longer fully anonymize spatial data, and targeted geoprivacy protection needs to be further investigated to improve user experience.},

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@article{McKenzie2021c,

title = {Identifying regional variation in place visit behavior during a global pandemic},

author = {Grant McKenzie and Kevin Mwenda},

doi = {10.5311/JOSIS.2021.23.170},

year  = {2021},

date = {2021-12-24},

urldate = {2021-11-11},

journal = {Journal of Spatial Information Science},

volume = {2021},

number = {23},

pages = {95-124},

abstract = {The emergence of the SARS-CoV-2 virus in 2019 lead to a global pandemic that altered the activity behavior of most people on our planet.  While government regulations and public concern modified visitation patterns to places of interest, little research has examined the nuanced changes in the length of time someone spends at a place, nor the regional variability of these changes. In this work, we examine place visit duration in four major U.S. cities, identify which place types saw the largest and smallest changes, and quantify variation between cities.  Furthermore, we identify socio-economic and demographic factors that contribute to changes in visit duration and demonstrate the varying influence of these factors by region.  The results of our analysis indicate that the pandemic's impact on visiting behavior varies between cities, though there are commonalities found in certain types of places.  Our findings suggest that places of interest within lower income communities experienced less change in visit duration than others.  An increase in the percentage of younger, Black or Hispanic populations within a community also resulted in a smaller decrease in visit duration than in other communities.   These findings offer insight into the factors that contribute to changes in visiting behavior and the resilience of communities to a global pandemic.},

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@article{McKenzie2021,

title = {Measuring urban regional similarity through mobility signatures},

author = {Grant McKenzie and Daniel Romm},

url = {https://grantmckenzie.com/academics/CitySim_2021.pdf

https://platial.science/citysim/

https://www.sciencedirect.com/science/article/pii/S0198971521000910},

doi = {10.1016/j.compenvurbsys.2021.101684},

year  = {2021},

date = {2021-09-01},

journal = {Computers, Environment and Urban Systems},

volume = {89},

pages = {101684},

abstract = {The task of identifying similar regions within and between cities is an important aspect of urban data science as well as applied domains such as real estate, tourism, and urban planning. Regional similarity is typically assessed through comparing socio-demographic variables, resource availability, or urban infrastructure. An essential dimension, often overlooked for this task, is the spatiotemporal mobility patterns of people within a city. In this work we present a novel approach to identifying regional similarity based on human mobility as proxied through micromobility trips. We use a dataset consisting of e-scooter trip origins and destinations for two major European cities that differ in population size and urban structure. Three dimensions of these data are used in modeling the spatial and temporal variability in movement between regions in cities, allowing us to compare regions through a mobility lens. The result is a parameterized similarity model and interactive web platform for comparing regions across different urban environments. The application of this model suggests that human mobility patterns are a quantifiable, unique, and appropriate characteristic through which to measure urban similarity.},

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@article{McKenzie2020,

title = {A country comparison of place-based activity response to COVID-19 policies},

author = {Grant McKenzie and Benjamin Adams},

url = {https://grantmckenzie.com/academics/McKenzieAdams2020.pdf},

doi = {10.1016/j.apgeog.2020.102363},

year  = {2020},

date = {2020-05-19},

journal = {Applied Geography},

volume = {125},

number = {2020},

pages = {102363},

organization = {arXiv.org},

abstract = {The emergence of the novel Coronavirus Disease in late 2019 (COVID-19) and subsequent pandemic led to an immense disruption in the daily lives of almost everyone on the planet.  Faced with the consequences of inaction, most national governments responded with policies that restricted the activities conducted by their inhabitants.  As schools and businesses shuttered, the mobility of these people decreased.  This reduction in mobility, and related activities, was recorded through ubiquitous location-enabled personal mobile devices.  Patterns emerged that varied by place-based activity.  In this work the differences in these place-based activity patterns are investigated across nations, specifically focusing on the relationship between government enacted policies and changes in community activity patterns.  We show that people's activity response to government action varies widely both across nations as well as regionally within them.  Three assessment measures are devised and the results correlate with a number of global indices.  We discuss these findings and the relationship between government action and residents' response.},

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@article{McKenzieCEUS2019,

title = {Urban mobility in the sharing economy: A spatiotemporal comparison of shared mobility services},

author = {Grant McKenzie },

url = {https://www.grantmckenzie.com/academics/McKenzie_CEUS2019.pdf},

year  = {2020},

date = {2020-01-01},

journal = {Computers, Environment and Urban Systems},

volume = {79},

pages = {101418},

abstract = {The influx of micro-mobility services, such as dockless scooter-share and e-bikes, in many cities are contributing to a substantial change in urban transportation with adoption rates reminiscent of other shared-mobility services, such as ride-hailing, years prior.  Touted as a solution to the last mile problem, a multitude of micro-mobility companies have situated themselves in urban centers promising low cost alternative transportation options for short, urban travel.  The rapid arrival of these companies, however, has left little time for city officials, transportation planners, and citizens to assess the demand for these services and compare them to existing transportation options.  In this work, we investigate two key aspects of these micro-mobility services. First, we identify the spatial and temporal differences between these mobility companies and highlight the nuanced differences in usage patterns.  Second, we compare these new services to an existing mode of transportation, namely automobile-based ride-hailing, with regards to differences in travel time within a city.  The results of these analyses indicate that while many micro-mobility companies are spatiotemporally similar, there are notable differences in where and when these services are used.  Similarly, we find that automobile travel is not always the fastest means of transportation within an urban setting.  During periods of heavy traffic congestion, e.g., rush hour, micro-mobility services offer a faster means of travel within the city.  The findings presented in this work offer evidence on which to inform urban planning and transportation policy with respect to shared mobility services, free floating vehicles, and alternative urban transportation.},

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@article{80,

title = {Spatiotemporal comparative analysis of scooter-share and bike-share usage patterns in Washington, D.C.},

author = {Grant McKenzie},

url = {https://www.grantmckenzie.com/academics/McKenzie_JTG2019.pdf},

doi = {10.1016/j.jtrangeo.2019.05.007},

year  = {2019},

date = {2019-06-01},

journal = {Journal of Transport Geography},

volume = {78},

pages = {19-28},

chapter = {19},

abstract = {The United States is currently in the midst of a micro-mobility revolution of sorts. Almost overnight, U.S. cities have been inundated with short-term rental scooters owned and operated by start-up companies promising a disruption to the urban transportation status-quo. These scooter-share services are presented as a dockless alternative to traditionally government-funded, docking station-based bike-sharing programs. Given the rapid rise of electric scooter companies, and how little is known about their operations, there is pressing public interest in understanding the impact of these transportation-sharing platforms. By exploring the nuanced spatial and temporal activity patterns of each of these platforms, this research identifies differences and similarities between dockless e-scooters and existing bike-sharing services. The findings from this research contribute to our understanding of urban transportation behavior and differences within mobility platforms.},

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@article{71,

title = {A user-generated data based approach to enhancing location prediction of financial services in sub-Saharan Africa},

author = {Grant McKenzie and Todd R Slind},

url = {https://www.sciencedirect.com/science/article/pii/S0143622818302078},

doi = {10.1016/j.apgeog.2019.02.005},

year  = {2019},

date = {2019-01-01},

journal = {Applied Geography},

abstract = {The recent increase in user-generated content and social media adoption in developing countries offers an unprecedented opportunity to better understand the accessibility and spatial distribution of financial services in sub-Saharan Africa. Financial inclusion has been identified as a priority by multiple agencies in the region and on-the-ground efforts are currently underway to identify previously unknown financial access points in numerous developing African countries. Existing techniques for estimating the location of these access points rely on spatial analysis of often outdated or unsuitable publicly available datasets such as population density, road networks, etc. as well as expensive and time consuming surveys of locals in the region. In this work we propose an approach to augment existing spatial data analysis techniques through the inclusion of user-generated geo-content and geo-social media data. Though a comparison of standard regression models and machine learning techniques, this work proposes the use of alternative data sources to build prediction models for identifying financial access locations in countries where current estimation models are insufficient. Through a better understanding of geospatial distribution patterns this work aims at reducing data acquisition costs and providing decision makers with critical data more quickly and efficiently. Finally, we present a mobile application build on the outcomes of this analysis that is currently being used to better inform on-the-ground data collection efforts.},

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@article{65,

title = {Identifying urban neighborhood names through user-contributed online property listings},

author = {Grant McKenzie and Zheng Liu and Yingjie Hu and Myeong Lee},

url = {https://grantdmckenzie.com/academics/McKenzie_Neighborhoods2018.pdf},

year  = {2018},

date = {2018-10-01},

journal = {ISPRS International Journal of Geo-Information},

abstract = {Neighborhoods are vaguely defined, localized regions that share similar characteristics. They are most often defined, delineated, and named by the citizens that inhabit them rather than municipal government or commercial agencies. The names of these neighborhoods play an important role as a basis for community and sociodemographic identity, geographic communication, and historical context. In this work we take a data-driven approach to identifying neighborhood names based on the geospatial properties of user-contributed rental listings. Through a random forest ensemble learning model applied to a set of spatial statistics for all n-grams in listing descriptions, we show that neighborhood names can be uniquely identified within urban settings. We train a model based on data from Washington, DC and test it on listings in Seattle, WA and Montreal, QC. The results indicate that a model trained on housing data from one city can successfully identify neighborhood names in another. In addition, our approach identifies less common neighborhood names and suggestions alternative or potentially new names in each city. These findings represent a first step in the process of urban neighborhood identification and delineation.},

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@article{59,

title = {A data-driven approach to exploring similarities of tourist attractions through online reviews},

author = {Grant McKenzie and Benjamin Adams},

url = {http://www.grantmckenzie.com/academics/McKenzieAdams_Tourism2018.pdf},

year  = {2018},

date = {2018-07-01},

journal = {Journal of Location Based Services},

volume = {12},

pages = {94-118},

chapter = {94},

abstract = {The motivation for tourists to visit a city is often driven by the uniqueness of the attractions accessible within the region. The draw to these locations varies by visitor as some travelers are interested in a single specific attraction while others prefer thematic travel. Tourists today have access to detailed experiences of other visitors to these locations in the form of user-contributed text reviews, opinions, photographs, and videos, all contributed through online tourism platforms. The data available through these platforms offer a unique opportunity to examine the similarities and difference between these attractions, their cities, and the visitors that contribute the reviews. In this work we take a data-driven approach to assessing similarity through textual analysis of user-contributed reviews, uncovering nuanced differences and similarities in the ways that reviewers write about attractions and cities.},

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@article{63,

title = {Identifying spatiotemporal urban activities through linguistic signatures},

author = {Cheng Fu and Grant McKenzie and Vanessa Frias-Martinez and Kathleen Stewart},

url = {https://www.sciencedirect.com/science/article/pii/S0198971517303472},

doi = {10.1016/j.compenvurbsys.2018.07.003},

issn = {0198-9715},

year  = {2018},

date = {2018-07-01},

journal = {Computers, Environment and Urban Systems},

abstract = {Identifying the activities that individuals conduct in a city is key to understanding urban dynamics. It is difficult, however, to identify different human activities on a large scale without incurring significant costs. This study focuses on modeling the spatiotemporal patterns of different activity types within cities by employing user-contributed, geosocial content as a proxy for human activities. In this work, we use linguistic topic modeling to analyze georeferenced twitter data in order to differentiate different activity types. We then examine the spatial and temporal patterns of the derived activity types in three U.S. cities: Baltimore, MD., Washington, D.C., and New York City, NY. The linguistic patterns reflect the spatiotemporal context of the places where the social media content is posted. We further construct a method to link what people post online to the activities conducted within a city. We then use these derived activities to profile the characteristics of neighborhoods in the three cities, and apply the activity signatures to discover similar neighborhoods both within and between the cities. This approach represents a novel activity-based method for assessing similarity between neighborhoods.},

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@article{46,

title = {Crowdsourcing the Character of a Place: Character-Level Convolutional Networks for Multilingual Geographic Text Classification},

author = {Benjamin Adams and Grant McKenzie},

url = {http://www.grantmckenzie.com/academics/CharacterOfPlace_2017.pdf},

doi = {10.1111/tgis.12317},

year  = {2018},

date = {2018-01-01},

journal = {Transactions in GIS},

abstract = {This article presents a new character-level convolutional neural network model that can classify multilingual text written using any character set that can be encoded with UTF-8, a standard and widely used 8-bit character encoding. For geographic classification of text, we demonstrate that this approach is competitive with state-of-the-art word-based text classification methods. The model was tested on four crowdsourced data sets made up of Wikipedia articles, online travel blogs, Geonames toponyms, and Twitter posts. Unlike word-based methods, which require data cleaning and pre-processing, the proposed model works for any language without modification and with classification accuracy comparable to existing methods. Using a synthetic data set with introduced character-level errors, we show it is more robust to noise than word-level classification algorithms. The results indicate that UTF-8 character-level convolutional neural networks are a promising technique for georeferencing noisy text, such as found in colloquial social media posts and texts scanned with optical character recognition. However, word-based methods currently require less computation time to train, so are currently preferable for classifying well-formatted and cleaned texts in single languages.},

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@article{50,

title = {A Natural Language Processing and Geospatial Clustering Framework for Harvesting Local Place Names from Geotagged Housing Advertisements},

author = {Yingjie Hu and Huina Mao and Grant McKenzie},

year  = {2018},

date = {2018-01-01},

journal = {International Journal of Geographical Information Science},

abstract = {Local place names are frequently used by residents living in a geographic region. Such place names may not be recorded in existing gazetteers, due to their vernacular nature, relative insignificance to a gazetteer covering a large area (e.g., the entire world), recent establishment (e.g., the name of a newly-opened shopping center), or other reasons. While not always recorded, local place names play important roles in many applications, from supporting public participation in urban planning to locating victims in disaster response. In this paper, we propose a computational framework for harvesting local place names from geotagged housing advertisements. We make use of those advertisements posted on local-oriented websites, such as Craigslist, where local place names are often mentioned. The proposed framework consists of two stages: natural language processing (NLP) and geospatial clustering. The NLP stage examines the textual content of housing advertisements, and extracts place name candidates. The geospatial stage focuses on the coordinates associated with the extracted place name candidates, and performs multi-scale geospatial clustering to filter out the non-place names. We evaluate our framework by comparing its performance with those of six baselines. We also compare our result with four existing gazetteers to demonstrate the not-yet-recorded local place names discovered by our framework.},

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@article{40,

title = {A Geoprivacy Manifesto},

author = {Carsten Kessler and Grant McKenzie},

url = {http://grantmckenzie.com/academics/GeoprivacyManifesto2017.pdf},

doi = {10.1111/tgis.12305},

year  = {2017},

date = {2017-12-01},

journal = {Transactions in GIS},

volume = {22},

pages = {3-19},

abstract = {As location-enabled technologies are becoming ubiquitous, our location is being shared with an ever-growing number of external services. Issues revolving around location privacy – or geoprivacy – therefore concern the vast majority of the population, largely without knowing how the underlying technologies work and what can be inferred from an individualtextquoterights location, especially if recorded over longer periods of time. Research, on the other hand, has largely treated this topic from isolated standpoints, most prominently from the technological and ethical point of view. This article therefore reflects upon the current state of geoprivacy from a broader perspective. It integrates technological, ethical, legal, and educational aspects and clarifies how they interact and shape how we deal with the corresponding technology, both individually and as a society. It does so in the form of a manifesto, consisting of 21 theses that summarize the main arguments made in the article. These theses argue that location information is different from other kinds of personal information and, in combination, show why geoprivacy (and privacy in general) needs to be protected and should not become a mere illusion. The fictional couple of Jane and Tom is used as a running example to illustrate how common it has become to share our location information, and how it can be used – both for good and for worse.},

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@article{39,

title = {A data-synthesis-driven method for detecting and extracting vague cognitive regions},

author = {Song Gao and Krzysztof Janowicz and Daniel R Montello and Yingjie Hu and Jiue-An Yang and Grant McKenzie and Yiting Ju and Li Gong and Benjamin Adams and Bo Yan},

url = {http://grantmckenzie.com/academics/VagueRegions2017.pdf},

doi = {10.1080/13658816.2016.1273357},

year  = {2017},

date = {2017-01-01},

journal = {International Journal of Geographical Information Science},

volume = {31},

pages = {1245–1271},

abstract = {Cognitive regions and places are notoriously difficult to represent in geographic information science and systems. The exact delineation of cognitive regions is challenging insofar as borders are vague, membership within the regions varies non-monotonically, and raters cannot be assumed to assess membership consistently and homogeneously. In a recent study, Montello et al. (2014) devised a novel grid-based task in which participants rated the membership of individual cells in a given region and contrasted this approach to a standard boundarydrawing task. Specifically, the authors assessed the vague cognitive regions of Northern California and Southern California. The boundary between these cognitive regions was found to have variable width, and region membership peaked not at the most northern or southern cells but at substantially less extreme latitudes. The authors thus concluded that region membership is about attitude, not just latitude. In the present work, we reproduce this study by approaching it from a computational fourth-paradigm perspective, i.e., by the synthesis of high volumes of heterogeneous data from various sources. We compare the regions which we identify to those from Montello et al. (2014), identifying differences and commonalities. Our results show a significant positive correlation to those in the original study. Beyond the extracted regions themselves, we compare and contrast the empirical and analytical approaches of these two methods, one a conventional human-participants study and the other an application of increasingly popular data-synthesis-driven research methods in GIScience},

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@article{42,

title = {ISED: Constructing a high-resolution elevation road dataset from massive, low-quality in-situ observations derived from geosocial fitness tracking data},

author = {Grant McKenzie and Krzysztof Janowicz},

url = {http://grantmckenzie.com/academics/McKenzie_ISED_2017.pdf},

year  = {2017},

date = {2017-01-01},

journal = {PLoS ONE},

abstract = {Gaining access to inexpensive, high-resolution, up-to-date, three-dimensional road network data is a top priority beyond research, as such data would fuel applications in industry, governments, and the broader public alike. Road network data are openly available via user-generated content such as OpenStreetMap (OSM) but lack the resolution required for many tasks, e.g., emergency management. More importantly, however, few publicly available data offer information on elevation and slope. For most parts of the world, up-to-date digital elevation products with a resolution of less than 10 meters are a distant dream and, if available, those datasets have to be matched to the road network through an error-prone process. In this paper we present a radically different approach by deriving road network elevation data from massive amounts of in-situ observations extracted from user-contributed data from an online social fitness tracking application. While each individual observation may be of low-quality in terms of resolution and accuracy, taken together they form an accurate, high-resolution, up-to-date, three-dimensional road network that excels where other technologies such as LiDAR fail, e.g., in case of overpasses, overhangs, and so forth. In fact, the 1m spatial resolution dataset created in this research based on 350 million individual 3D location fixes has an RMSE of approximately 3.11m compared to a LiDAR-based ground-truth and can be used to enhance existing road network datasets where individual elevation fixes differ by up to 60m. In contrast, using interpolated data from the National Elevation Dataset (NED) results in 4.75m RMSE compared to the base line. We utilize Linked Data technologies to integrate the proposed high-resolution dataset with OpenStreetMap road geometries without requiring any changes to the OSM data model.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{14,

title = {Crowdsensing Smart Ambient Environments and Services},

author = {Blake Regalia and Grant McKenzie and Song Gao and Krzysztof Janowicz},

url = {http://grantmckenzie.com/academics/McKenzie_TGIS2016_2.pdf},

doi = {10.1111/tgis.12233},

year  = {2016},

date = {2016-06-01},

journal = {Transactions in GIS},

volume = {20},

pages = {382–398},

abstract = {Whether it be Smart Cities, Ambient Intelligence, or the Internet of Things, current visions for future urban spaces share a common core, namely the increasing role of distributed sensor networks and the on-demand integration of their data to power real-time services and analytics. Some of the greatest hurdles to implementing these visions include security risks, user privacy, scalability, the integration of heterogeneous data, and financial cost. In this work, we propose a crowdsensing mobile-device platform that empowers citizens to collect and share information about their surrounding environment via embedded sensor technologies. This approach allows a variety of urban areas (e.g., university campuses, shopping malls, city centers, suburbs) to become equipped with a free ad-hoc sensor network without depending on proprietary instrumentation. We present a framework, namely the GeoTracer application, as a proof of concept to conduct multiple experiments simulating use-case scenarios on a university campus. First, we demonstrate that ambient sensors (e.g., temperature, pressure, humidity, magnetism, illuminance, and audio) can help determine a change in environment (e.g., moving from indoors to outdoors, or floor changes inside buildings) more accurately than typical positioning technologies (e.g., global navigation satellite system, Wi-Fi, etc.). Furthermore, each of these sensors contributes a different amount of data to detecting events. for example, illuminance has the highest information gain when trying to detect changes between indoors and outdoors. Second, we show that through this platform it is possible to detect and differentiate place types on a university campus based on inferences made through ambient sensors. Lastly, we train classifiers to determine the activities that a place can afford at different times (e.g., good for studying or not, basketball courts in use or empty) based on sensor-driven semantic signatures},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{15,

title = {Spatial signatures for geographic feature types: examining gazetteer ontologies using spatial statistics},

author = {Rui Zhu and Yingjie Hu and Krzysztof Janowicz and Grant McKenzie},

url = {http://grantmckenzie.com/academics/McKenzie_TGIS2016_1.pdf},

doi = {10.1111/tgis.12232},

year  = {2016},

date = {2016-06-01},

journal = {Transactions in GIS},

volume = {20},

pages = {333–355},

abstract = {Digital gazetteers play a key role in modern information systems and infrastructures. They facilitate (spatial) search, deliver contextual information to recommended systems, enrich textual information with geographical references, and provide stable identifiers to interlink actors, events, and objects by the places they interact with. Hence, it is unsurprising that gazetteers, such as GeoNames, are among the most densely interlinked hubs on the Web of Linked Data. A wide variety of digital gazetteers have been developed over the years to serve different communities and needs. These gazetteers differ in their overall coverage, underlying data sources, provided functionality, and geographic feature type ontologies. Consequently, place types that share a common name may differ substantially between gazetteers, whereas types labeled differently may, in fact, specify the same or similar places. This makes data integration and federated queries challenging, if not impossible. To further complicate the situation, most popular and widely adopted geo-ontologies are lightweight and thus under-specific to a degree where their alignment and matching become nothing more than educated guesses. The most promising approach to addressing this problem, and thereby enabling the meaningful integration of gazetteer data across feature types, seems to be a combination of top-down knowledge representation with bottom-up data-driven techniques such as feature engineering and machine learning. In this work, we propose to derive indicative spatial signatures for geographic feature types by using spatial statistics. We discuss how to create such signatures by feature engineering and demonstrate how the signatures can be applied to better understand the differences and commonalities of three major gazetteers, namely DBpedia Places, GeoNames, and TGN.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{5h,

title = {Assessing the effectiveness of different visualizations for judgments of positional uncertainty},

author = {Grant McKenzie and Mary Hegarty and Trevor Barrett and Michael Goodchild},

url = {http://grantmckenzie.com/academics/McKenzie_PositionalUncertainty2015_preprint.pdf},

doi = {10.1080/13658816.2015.1082566},

year  = {2016},

date = {2016-01-01},

journal = {International Journal of Geographic Information Science},

volume = {30},

pages = {221-239},

abstract = {Many techniques have been proposed for visualizing uncertainty in geospatial data. Previous empirical research on the effectiveness of visualizations of geospatial uncertainty has focused primarily on user intuitions rather than objective measures of performance when reasoning under uncertainty. Framed in the context of Googletextquoterights blue dot, we examined the effectiveness of four alternative visualizations for representing positional uncertainty when reasoning about self-location data. Our task presents a mobile mapping scenario in which GPS satellite location readings produce location estimates with varying levels of uncertainty. Given a known location and two smartphone estimates of that known location, participants were asked to judge which smartphone produces the better location reading, taking uncertainty into account. We produced visualizations that vary by glyph type (uniform blue circle with border vs. Gaussian fade) and visibility of a centroid dot (visible vs. not visible) to produce the four visualization formats. Participants viewing the uniform blue circle are most likely to respond in accordance with the actual probability density of points sampled from bivariate normal distributions and additionally respond most rapidly. Participants reported a number of simple heuristics on which they based their judgments, and consistency with these heuristics was highly predictive of their judgments.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}