دانلود پایان نامه علوم جغرافیایی pdf

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عنوان فارسی:

بررسی فعالیت های انسانی در شهرها با استفاده از داده ها و مدل ها

عنوان انگلیسی:

Exploring Human Activities in Cities Using Data and Model

Abstract
Human activities in cities, such as eating foods in restaurants and attending special events, are traditionally measured using surveys, questionnaires, and interviews. While such methods have provided valuable insights, there are issues, such as cost when exploring large geographical areas during long periods of time. With the development of social media platforms and open data initiatives, a large amount of data that contains information on people’s interests and opinions, geolocation, and timestamps are becoming available to the general public. The question is, how can we utilize these datasets with three dimensions (i.e., textual, geographical, and temporal) to explore questions related to human activities in cities? How do we use the results discovered from the data to better study and plan our cities? This dissertation research used a multi-disciplinary Computational Social Science approach to explore these questions. The research specifically demonstrates how to explore food related discussions, special events and their impact on traffic flow using open sources data. It also shows the potential and methods to explore human activities in cities using social media data together with other data sources, and through agent-based simulation and analysis, the results can help planners and engineers to study, plan, and manage our cities.

عنوان فارسی:

استفاده از داده‌های بزرگ حس شده اجتماعی برای مدل‌سازی الگوها و زمینه جغرافیایی فعالیت‌های انسانی در شهرها

عنوان انگلیسی:

Using Socially Sensed Big Data to Model Patterns and Geographic Context of Human Activities in Cities

Abstract
Understanding dynamic interactions between human activities and land-use structure in a city is a key lens to explore the city as a complex system. This dissertation contributes to understanding the complexity of urban dynamics by gaining knowledge of the interactions between human activities and city land-use structures by utilizing free-accessible socially sensed data sources, and building upon recent research trend and technologies in geographical information science, urban study, and computer science. This dissertation addresses three main questions related to human dynamics: 1) how human activities in an urban environment are shaped by socioeconomic status and the intra-city land-use structure, and how in turn, the knowledge of socioeconomic status-activity relationships can contribute to understanding the social landscape of a city; 2) how different types of activities are located in space and time in three U.S. cities and how the spatiotemporal activity patterns in these cities characterize the activity profile of different neighborhoods in the cities; and 3) how recent socially sensed information on human activities can be integrated with widely-used remotely sensed geographical data to create a novel approach for discovering patterns of land use in cities that are otherwise lacking in up to date land use information. This dissertation models the associations between socioeconomics and mobility in the Washington, D.C. metropolitan area as a case study and applies the learned associations for inferring geographical patterns of socioeconomic status (SES) solely using the socially sensed data. This dissertation also implements a semi-automated workflow to retrieve activity details from socially sensed Twitter data in Washington, D.C., the City of Baltimore, and New York City. The dissertation integrates remotely-sensed imagery and socially sensed data to model the dynamics associated with changing land-use types in the Washington, D.C.-Baltimore metropolitan area over time.