Local Information Landscapes: Theory, Measures, and Evidence

To understand issues about information accessibility within communities, research studies have examined human, social, and technical factors by taking a sociotechnical view. While this view provides a profound understanding of how people seek, use, and access information, this approach tends to overlook the impact of the larger structures of information landscapes that constantly shape peoples access to information. When it comes to local community settings where local information is embedded in diverse material entities such as urban places and technical infrastructures, the e ect of information landscapes should be taken into account in addition to particular strategies for solving information-seeking issues.

However, characterizing the information landscape of a local community at the community level is a non-trivial problem due to diverse contexts, users, and their interactions with each other. One way to conceptualize local information landscapes in a way that copes with the complexity of the interplay between information, contexts, and human factors is to focus on the materiality of information. By focusing on the material aspects of information, it becomes possible to understand how local information is provided to social entities and infrastructures and how it exists, forming structures at the community level. Through an extensive literature review, this paper develops a theory of local information landscapes (LIL Theory) to better conceptualize the community-level, material structure of local information. Speci cally, the LIL theory adapts a concept of the virtual as an ontological view of the interplay between technical infrastructures, spaces, and people as a basis for assessing and explaining community-level structures of local information. By complementing existing theories such as information worlds and information grounds, this work provides a new perspective on how information deserts manifest as a material precondition of information inequality.

Using this framework, an empirical study was conducted to examine the explicit e ects of information deserts on other community characteristics. Speci cally, the study aims to provide an initial assessment of LIL theory by examining how the fragmentation of local information, a form of information deserts, is related to important community characteristics such as socio-economic inequality, deprivation, and community engagement. Building upon previous work in sociology and political science, this study shows that the fragmentation of local information (1) is shaped by socio-economic deprivation/inequality that is confounded with ethnoracial heterogeneity, (2) the fragmentation of local information is highly correlated to people's community gatherings, (3) the fragmentation of local information moderates the e ects of socio-economic inequality on cultural activity diversity, and (4) the fragmentation of local information mediates the relationship between socio-economic inequality and community engagement. By making use of three local event datasets over 20 months in 14 U.S. cities (about two million records) and over 3 months in 28 U.S. cities (about 620K records), respectively, this study develops computational frameworks to operationalize information deserts in a scalable way.

This dissertation provides a theorization of community-level information inequality and computational models that support the quantitative examination of it. Further theorizations of the conceptual constructs and methodological improvements on measurements will bene t information policy-makers, local information system designers, and researchers who study local communities with conceptual models, vocabularies, and assessment frameworks.

 

* Dissertation Committee: Dr. Brian Butler (Chair), Dr. Vanessa Frias-Martinez, Dr. Grant McKenzie, Dr. Paul Jaeger, and Dr. Hiroyuki Iseki (Dean's Rep)

Venue: 
Doctoral Dissertation, University of Maryland Press.
Authors: 
Myeong Lee
Citation: 

Lee, M. (2019). Local Information Landscapes: Theory, Measures, and Evidence. Doctoral Dissertation. College Park, MD: University of Maryland Press.