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West Baltimore vacancies plotted as cluster objects (Color figure online)

West Baltimore vacancies plotted as cluster objects (Color figure online)

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Conference Paper
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We explore the idea of spatial lenses as pieces of software interpreting data sets in a particular spatial view of an environment. The lenses serve to prepare the data sets for subsequent analysis in that view. Examples include a network lens to view places in a literary text, or a field lens to interpret pharmacy sales in terms of seasonal allergy...

Context in source publication

Context 1
... clustering characteristics, including minimum number of samples (in this case five other vacant parcels) and maximum sampling distance (in this case eight houses away), are supplied by the user. The results in Figure 2 show point objects for each vacant building plotted by location and with clusters differentiated by color. An automated count reveals that the Central Park Heights and Sandtown-Winchester neighborhoods contain the highest number of clusters, with 30 and 22 lots respectively. ...

Citations

... On this account, networks are one of a range of concepts needed for interpreting the environment and for reasoning with GIS. These concepts constitute conceptual "lenses" through which the environment can be studied independently of technical representations (Allen et al., 2016;Kuhn & Ballatore, 2015). Besides the base concept of location, allowing for metric distance assessments in space, Kuhn distinguished the following content concepts, which we interpret here in a broader research context: ...
Article
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Spatial network analysis is a collection of methods for measuring accessibility potentials as well as for analyzing flows over transport networks. Though it has been part of the practice of geographic information systems for a long time, designing network analytical workflows still requires a considerable amount of expertise. In principle, artificial intelligence methods for workflow synthesis could be used to automate this task. This would improve the (re)usability of analytic resources. However, though underlying graph algorithms are well understood, we still lack a conceptual model that captures the required methodological know‐how. The reason is that in practice this know‐how goes beyond graph theory to a significant extent. In this article we suggest interpreting spatial networks in terms of quantified relations between spatial objects, where both the objects themselves and their relations can be quantified in an extensive or an intensive manner. Using this model, it becomes possible to effectively organize data sources and network functions towards common analytical goals for answering questions. We tested our model on 12 analytical tasks, and evaluated automatically synthesized workflows with network experts. Results show that standard data models are insufficient for answering questions, and that our model adds information crucial for understanding spatial network functionality.
... The work thus uses Kuhn's (2012) spatial ontology (cf. also Allen et al., 2016;Vasardani & Winter 2016), which is centered on the following concepts. ...
... We believe that our taxonomy can double as an inference system for urban place and place name retrieval and organization. This is consistent with the view that semantic content can guide the development of spatial inference systems (e.g., Keßler, Janowicz & Bishr;Allen et al., 2016;Kuhn, 2012;Winter & Freksa, 2012). For instance, street directory apps may include inference systems that answer where-questions by parsing the generic terms included in a search. ...
Article
The goal of this paper is to offer an analysis of urbanonyms, names for urban places, and show how this analysis can inform a conceptual taxonomy of urban places via the cultural lens of language. To reach this goal, the paper offers a classification of Italian urbanonyms (e.g., Via Nazionale "National Street") based on data extraction from the Pagine Gialle directory, and a taxonomy of place concepts. This classification is obtained via a lexicographic analysis of extracted terms and their sense relations. A discussion of place concepts unique to cities across Italy is offered, as proof of the importance of cultural and linguistic facets. The paper concludes by discussing how these results can inform research on place ontologies across disciplines.
... They provide us with a set of interchangeable lenses through which research data can be spatialized and viewed. 43 To produce maps, we first produce a field of continuous topic values from the texts of research documents with a topic value at each position. This can be thought of as a landscape or surface of topic values. ...
Article
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The institutional review of interdisciplinary bodies of research lacks methods to systematically produce higher-level abstractions. Abstraction methods, like the “distant reading” of corpora, are increasingly important for knowledge discovery in the sciences and humanities. We demonstrate how abstraction methods complement the metrics on which research reviews currently rely. We model cross-disciplinary topics of research publications and projects emerging at multiple levels of detail in the context of an institutional review of the Earth Research Institute (ERI) at the University of California at Santa Barbara. From these, we design science maps that reveal the latent thematic structure of ERI's interdisciplinary research and enable reviewers to “read” a body of research at multiple levels of detail. We find that our approach provides decision support and reveals trends that strengthen the institutional review process by exposing regions of thematic expertise, distributions and clusters of work, and the evolution of these aspects.
... Their findings suggested that ANNs can be appropriate techniques for asthma detection. Due to a lack of research on the spatial complexities of COVID-19 at the national level, in this study, we leveraged the potential of ANNs in identifying complex spatial patterns and the power of geographic information systems (GIS) in spatial analysis [29,30] to predict county-level COVID-19 incidence rates in the continental United States. We employed one of the widely used topologies of ANNs that is described in Section 2.4. ...
Article
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Prediction of the COVID-19 incidence rate is a matter of global importance, particularly in the United States. As of 4 June 2020, more than 1.8 million confirmed cases and over 108 thousand deaths have been reported in this country. Few studies have examined nationwide modeling of COVID-19 incidence in the United States particularly using machine-learning algorithms. Thus, we collected and prepared a database of 57 candidate explanatory variables to examine the performance of multilayer perceptron (MLP) neural network in predicting the cumulative COVID-19 incidence rates across the continental United States. Our results indicated that a single-hidden-layer MLP could explain almost 65% of the correlation with ground truth for the holdout samples. Sensitivity analysis conducted on this model showed that the age-adjusted mortality rates of ischemic heart disease, pancreatic cancer, and leukemia, together with two socioeconomic and environmental factors (median household income and total precipitation), are among the most substantial factors for predicting COVID-19 incidence rates. Moreover, results of the logistic regression model indicated that these variables could explain the presence/absence of the hotspots of disease incidence that were identified by Getis-Ord Gi* (p < 0.05) in a geographic information system environment. The findings may provide useful insights for public health decision makers regarding the influence of potential risk factors associated with the COVID-19 incidence at the county level.
... We chose Kuhn's ontology of core concepts of spatial information (Allen et al., 2016;Kuhn, 2012) which, in its latest form, includes a base concept location, four content concepts: field, object, network, and event, and three information quality concepts: granularity, accuracy, and provenance. For the purpose of this article, the quality concepts are out of scope. ...
Article
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Human spatial concepts, such as the concept of place, are not immediately translatable to the geometric foundations of spatial databases and information systems developed over the past 50 years. These systems typically rest on the concepts of objects and fields, both bound to coordinates, as two general paradigms of geographic representation. The match between notions of place occurring in everyday where questions and the data available to answer such questions is unclear and hinders progress in place‐based information systems. This is particularly true in novel application areas such as the Digital Humanities or speech‐based human–computer interaction, but also for location‐based services. Although this shortcoming has been observed before, we approach the challenges of relating places to information system representations with a fresh view, based on a set of core concepts of spatial information. These concepts have been proposed in information science with the intent of serving human–machine spatial question asking and answering. Clarifying the relationship of the notion of place to these concepts is a significant step toward geographically intelligent systems. The main result of the article is a demonstration that the notion of place fits existing concepts of spatial information, when these are adequately exploited and combined.