Thirty-three brothels in rural and small-town Nevada, which contain between 225 and 250 prostitutes, are legal or openly tolerated and strictly controlled by state statute, city and county ordinances, and local rules. Twenty-two of the brothels are in places with populations between 500 and 8,000, and the remaining eleven are in rural areas. The legal and quasi-legal restrictions placed on prostitutes severely limit their activities outside brothels. These restrictions in conjunction with historical inertia, perceived benefits of crime and venereal disease control, and the good image of madams contribute to widespread positive local attitudes toward brothel prostitution. Interactions between clients and prostitutes in brothel parlors are also restricted and limited to a few basic types which are largely determined by entrepreneurial philosophy. KEY WORDS : Nevada, Political geography, Prostitution, Restricted activity spaces.

ANNALS of the Association of American Geographers

WiFi localization, the task of determining the physical location of a mobile device from wireless signal strengths, has been shown to be an accurate method of indoor and outdoor localization and a powerful building block for location-aware applications. However, most localization techniques require a training set of signal strength readings labeled against a ground truth location map, which is prohibitive to collect and maintain as maps grow large. In this paper we propose a novel technique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GPLVM) to determine the latent-space locations of unlabeled signal strength data. We show how GPLVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topological connectivity graph from a signal strength sequence which, in combination with the learned Gaussian Process signal strength model, can be used to perform efficient localization.

WiFi-SLAM Using G aussian Process Latent Variable Models

Data about points of interest (POI) have been widely used in studying urban land use types and for sensing human behavior. However, it is difficult to quantify the correct mix or the spatial relations among different POI types indicative of specific urban functions. In this research, we develop a statistical framework to help discover semantically meaningful topics and functional regions based on the co-occurrence patterns of POI types. The framework applies the latent Dirichlet allocation (LDA) topic modeling technique and incorporates user check-in activities on location-based social networks. Using a large corpus of about 100,000 Foursquare venues and user check-in behavior in the 10 most populated urban areas of the US, we demonstrate the effectiveness of our proposed methodology by identifying distinctive types of latent topics and, further, by extracting urban functional regions using K-means clustering and Delaunay triangulation spatial constraints clustering. We show that a region can support multiple functions but with different probabilities, while the same type of functional region can span multiple geographically non-adjacent locations. Since each region can be modeled as a vector consisting of multinomial topic distributions, similar regions with regard to their thematic topic signatures can be identified. Compared with remote sensing images which mainly uncover the physical landscape of urban environments, our popularity-based POI topic modeling approach can be seen as a complementary social sensing view on urban space based on human activities.

Extracting urban functional regions from points of interest and human activities on location-based social networks

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Global Positioning System Theory And Practice

The analysis of data from social media and social networking sites may be instrumental in achieving a better understanding of human-environment interactions and in shaping future conservation and environmental management. In this study, we systematically map the application of social media data in environmental research. The quantitative review of 169 studies reveals that most studies focus on the analysis of people’s behavior and perceptions of the environment, followed by environmental monitoring and applications in environmental planning and governance. The literature testifies to a very rapid growth in the field, with Twitter (52 studies) and Flickr (34 studies) being most frequently used as data sources. A growing number of studies combine data from multiple sites and jointly investigates multiple types of media. A broader, more qualitative review of the insights provided by the investigated studies suggests that while social media data offer unprecedented opportunities in terms of data volume, scale of analysis, and real-time monitoring, researchers are only starting to cope with the challenges of data’s heterogeneity and noise levels, potential biases, ethics of data acquisition and use, and uncertainty about future data availability. Critical areas for the development of the field include integration of different types of information in data mashups, development of quality assurance procedures and ethical codes, improved integration with existing methods, and assurance of long-term, free and easy-to-access provision of public social media data for future environmental researchers.

Passive crowdsourcing of social media in environmental research: A systematic map

Extracting and understanding urban areas of interest using geotagged photos

Geoportals provide integrated access to geospatial resources, and enable both authorities and the general public to contribute and share data and services. An essential goal of geoportals is to facilitate the discovery of the available resources. Such process heavily relies on the quality of metadata. While multiple metadata standards have been established, data contributers may adopt different standards when sharing their data via the same geoportal. This is especially the case for user-generated content where various terms and topics can be introduced to describe similar datasets. While this heterogeneity provides a wealth of perspectives, it also complicates resource discovery. With the fast development of the Semantic Web technologies, there is a rise of Linked-Data-driven portals. Although these novel portals open up new ways to organizing metadata and retrieving resources, they lack effective semantic search methods. This paper addresses the two challenges discussed above, namely the topic heterogeneity brought by multiple metadata standards as well as the lack of established semantic search in Linked-Data-driven geoportals. To harmonize the metadata topics, we employ a natural language processing method, namely Labeled Latent Dirichlet Allocation (LLDA), and train it using standardized metadata from Data.gov. With respect to semantic search, we construct thematic and geographic matching features from the textual metadata descriptions, and train a regression model via a human participants experiment. We evaluate our methods by examining their performances in addressing the two issues. Finally, we implement a semantics-enabled and Linked-Data-driven prototypical geoportal using a sample dataset from Esri’s ArcGIS Online.

/pdf/metadata-topic-harmonization-and-semantic-search-for-linked-2eoo5w86e8.pdf

Metadata Topic Harmonization and Semantic Search for Linked‐Data‐Driven Geoportals: A Case Study Using ArcGIS Online

Place name disambiguation is an important task for improving the accuracy of geographic information retrieval. This task becomes more challenging when the input texts are short. Wikipedia provides information about places and has often been employed for named entity recognition. However, the natural language representation of Wikipedia articles limits more effective use of this rich knowledge base. DBpedia is the Semantic Web version of Wikipedia, which provides structured and machine-understandable knowledge mined from Wikipedia articles. This paper presents an approach for combining Wikipedia and DBpedia to disambiguate place names in short texts. We discuss the pros and cons of the two knowledge bases, and argue that a combination of both performs better than each of them alone. We evaluate our proposed method by conducting experiments against baselines of three established methods. The result indicates that our method has a generally higher precision and recall. While our study employs DBpedia, the proposed method is generic and can be extended to other structured Linked Datasets such as Freebase or Wikidata.

/pdf/improving-wikipedia-based-place-name-disambiguation-in-short-1auj9tzxjo.pdf

Improving wikipedia-based place name disambiguation in short texts using structured data from DBpedia

ArcGIS Online is a unified Web portal designed by Environment System Research Institute (ESRI). It contains a rich collection of Web maps, layers, and services contributed by GIS users throughout the world. The metadata about these GIS resources reside in data silos that can be accessed via a Web API. While this is sufficient for simple syntax-based searches, it does not support more advanced queries, e.g., finding maps based on the semantics of the search terms, or performing customized queries that are not pre-designed in the API. In metadata, titles and descriptions are commonly available attributes which provide important information about the content of the GIS resources. However, such data cannot be easily used since they are in the form of unstructured natural language. To address these difficulties, we combine data-driven techniques with theory-driven approaches to enable semantic search and knowledge discovery for ArcGIS Online. We develop an ontology for ArcGIS Online data, convert the metadata into Linked Data, and enrich the metadata by extracting thematic concepts and geographic entities from titles and descriptions. Based on a human participant experiment, we calibrate a linear regression model for semantic search, and demonstrate the flexible queries for knowledge discovery that are not possible in the existing Web API. While this research is based on the ArcGIS Online data, the presented methods can also be applied to other GIS cloud services and data infrastructures.

/pdf/enabling-semantic-search-and-knowledge-discovery-for-arcgis-3nm5vw9297.pdf

Enabling Semantic Search and Knowledge Discovery for ArcGIS Online: A Linked-Data-Driven Approach

A practical WiFi-based positioning system has to be adaptable to the variations of indoor environmental dynamic factors. In this work, we propose a novel Wi-Fi indoor positioning and tracking framework which employs the spatial analysis and image processing techniques. The Wi-Fi surfaces can be dynamically constructed and updated and thus help to address the challenges of signal spatial heterogeneity and environmental variations. A mobile app for indoor positioning application has been developed as a proof of concept. Based on the experiments we conducted at the Esri campus, this method can achieve about 2-meter positioning accuracy. The proposed methodology and theoretical frame-work can guide engineers to implement cost-effective indoor positioning infrastructure, and thus offer insights on future smart campus applications. The introduced spatial analysis and geoprocessing workflow may also bring the attention of GIScientists to make more efforts to conquer the indoor positioning and tracking challenges.

Sathya Prasad

Papers

Extracting and understanding urban areas of interest using geotagged photos

Metadata Topic Harmonization and Semantic Search for Linked‐Data‐Driven Geoportals: A Case Study Using ArcGIS Online

Improving wikipedia-based place name disambiguation in short texts using structured data from DBpedia

Enabling Semantic Search and Knowledge Discovery for ArcGIS Online: A Linked-Data-Driven Approach

Employing spatial analysis in indoor positioning and tracking using wi-fi access points