Today, over half of the world's population lives in cities and by 2050 nearly two thirds of the global population will live in urban areas \citep{UnitedNations2014}. As a result of this rapid urbanization, cities around the world are fundamentally changing the natural landscape, which has important consequences for both local and global ecosystems \citep{grimm2008global}. At the local level, cities generate an enormous volume of pollution that can lead to problems such as poor air quality, contamination of fresh water and reduced biodiversity. At the global scale, urban centers are primary sources of greenhouse-gas emissions making them one of the main drivers of global climate change (CITATION). It is now well understood that the future of Earth's ecosystem is increasingly influenced by human activities at the urban scale and in order to ensure a sustainable future, it is essential to understand the complex and dynamic relationship between cities and nature \citep{alberti2008advances, albeverio2007dynamics}.
Historically, the study of cities has been separated from the study of nature. The two were considered distinct phenomena with differing methods to understand the dynamics and driving forces behind each. However, cities and nature are increasing being understood as intricately connected phenomena which cannot be studied or understood in isolation and must be examined from a holistic and integrated means of study.
Urban ecology is a rapidly developing scientific field that seeks to integrate the natural and social sciences to study urban ecosystems. Broadly, urban ecology can be divided into the study of ecology in cities, the ecology of cities and the ecology of socio-economic urban systems (Douglas, 2009). The study of ecology in cities aims to understand how environmental systems within urban areas are shaped and affected by the surrounding area. In contrast, the ecology of cities is based on a systems perspective that views the city as an organism with circulating material and energy inputs and outputs (CITATION). The urban metabolism is a well-known concept attributed to Karl Marx that describes the exchange of materials and energy between society and nature. The concept gained considerable traction during the 1960's when Abel Wolman depicted the city as an entity with flows of resources including energy, materials, water and waste, that circulates into and out of an urban area \citep{wolman1965metabolism}. Flows and circulation are essential ideas in urban ecology. A final strand of urban ecology focuses on a range of human activities that affect ecosystems and environmental processes including social, economic and political drivers that are mediated through cities. \\
[Examples of urban ecology work? ]\\
While significant advances have been made, the interactions and feedback systems between human processes and ecological systems are still poorly understood. In order to understand the impact of urbanization on biodiversity and ecosystems, and the relationship between cities and nature more broadly, a more comprehensive view of ecological systems in and of cities is still needed, especially at the global scale \citep{alberti2008advances} %(introduction that references McDonald and Marcotullio ch 4.1, Niemela).
Another evolving field focused on urban systems has developed out of the widespread adoption of new information communication technologies in cities and the availability of massive datasets from a variety of novel sources. Urban informatics is a field of study that integrates and analyzes new sources of data including in situ sensors, user-generated data and administrative datasets to study socio-ecological urban systems \citep{thakuriah2016seeing}. The instrumentation of cities, combined with the integration of complex and heterogeneous administrative datasets, allows for new exploration and insight with increased resolution. The applications of urban informatics include improved urban operations and resource management, increased theoretical insight into urban systems including infrastructural, physical and socioeconomic systems, strategies for improving public engagement and participation, and strategies for improved long-term planning and impact assessment of urban policies. \\
[Examples of urban informatics work...]\\
%\section{The Informatics Dimension of Urban Ecology}
Given this context, this thesis presents a new framework that integrates the fields of urban ecology and urban informatics. Ecological Urban Informatics (EUI) is the integration of novel data sources including administrative data, sensor data and user-generated datasets, to study the complex interactions between nature and cities. To put simply, EUI is the informatics dimension of urban ecology.\\
[Section that describes similar work, which could also be considered a part of EUI]\\
\section{Key Contributions}
This work develops and explores EUI through three case studies. Each case study aims to highlight and explore key aspects of the EUI framework. While these three case studies are important foundational examples that elucidate elements of the EUI framework, they are not intended to be comprehensive. Instead, these studies are presented as novel works that illustrate and establish the potential usefulness of the EUI framework.
Chapter 2 begins with a study of urban waste, which is exemplary of the EUI framework. Historical municipal solid waste (MSW) collection data supplied by the New York City Department of Sanitation (DSNY) was used in conjunction with other datasets related to New York City to forecast municipal solid waste generation across the city. Spatiotemporal tonnage data from the DSNY was integrated with administrative data sets, including the Longitudinal Employer Household Dynamics data, the American Community Survey, the New York City Department of Finance's Primary Land Use and Tax Lot Output data, and historical weather data to build a Gradient Boosting Regression Model. The model was trained on historical data from 2005 to 2011 and validation was performed both temporally and spatially. With this model, we are able to accurately (R2 $>$ 0.88) forecast weekly MSW generation tonnages for each of the 232 geographic sections in NYC across three waste streams of refuse, paper and metal/glass/plastic. Importantly, the model identifies regularity of urban waste generation and is also able to capture short timescale fluctuations associated with holidays, special events, seasonal variations, and weather related events. This research shows not only how administrative datasets can be combined and used to improve operational efficiencies and resource management, but also quantitatively describes the intricate relationship between society and waste generation.
Chapter 3, while thematically related to urban waste, moves beyond static administrative datasets and highlights the citizens' role in the EUI framework as generators of new urban data streams and sources of distributed computation. This chapter describes an online crowdsourcing tool - Landfill Hunter - that was built to facilitate public participation to collect and analyze data about landfill locations in the United States. User-contributed data were used to verify existing landfill locations and build a comprehensive dataset of landfills in the United States. A total of 729 landfills were identified, and based on user-generated data, we estimated the land area of individual landfills and calculated the cumulative land area of landfills in the US, which is approximately twice the size of New York City. This work not only demonstrates the participatory and analytical roles of the public, but also the potential creative and educational outcomes that can be achieved through public participation.
Chapter 4 presents a final case study that evaluates the viability of a low-cost aerosol monitor for spatially dense network configurations in order to provide real-time fine particulate matter measurements. The sensor's performance is assessed during a field calibration campaign from February 7th to March 25th 2017, with a federal equivalent method (FEM) instrument in New York City and a novel calibration approach is used based on a machine learning method that incorporates publicly available meteorological data in order to improve the sensor's performance. Findings from this work shows that while the PPD42 performs well in relation to the reference instrument using linear regression, a gradient boosting regression tree model can significantly improve device calibration. The study discusses the sensor's overall performance and reliability when deployed in a dense urban environment and the applicability of improving performance of low-cost air quality monitor networks using machine learning techniques. This chapter shows that several challenges and drawbacks exist when using commodity hardware, especially in their ability to meet federal standards. However, considering the cost, ease of use and scalability, these technologies may provide important supplemental information useful for studying air quality as well as enable local communities to collect information about their local environments.
The final chapter includes a discussion of the limitations of EUI and future work to advance the concept of ecological urban informatics.
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%Cities are a dynamic and complex phenomena. that cannot be easily understood by studying its individual elements, nor by separating elements into categories of "nature" and "city". As (Harvey) suggests, cities are 'created ecosystems' whose processes are by the social and political relationships in cities that reproduce these relationships.
%Scale is another key element in the study of cities and nature. Tremendous works at the urban scale has focused on aspects relating to x,y and z. The linkage of urban ecological relationships to the global scale is also explored by X, y and Z. Significantly, however, \cite{grimm2008global} argue that cross-scale environmental influences from local to global are not equal. They argue that local systems are large drivers for global environmental changes, while the effects pf global environmental changes are largely overshadowed by the intense local changes. For example, urban heat island is a well known urban phenomena that has serious local effects.
%\section{The Informatics Dimension of Urban Ecology}
%Air quality is a specific area that results from the complex interactions between urban morphology, meteorological conditions, climate and human activity. The developed platform is aimed to enable local communities to collect information about their local environment. While the dissemination and use of these tools by communities is outside the scope of this work, careful evaluation the device's accuracy and limitations of the platform are presented. The primary purpose of this work is to evaluate the performance and quality of commodity hardware through calibration and real-world deployments.
%A similar framework was presented by \cite{pickett1997conceptual}
%\section{Extended Background}
%Cities modify and affect the environment. Ecological footprint. Fundamental changes in land use. Increased temperatures "urban heat island".
%Cities respond to environmental changes.
%Cities have several major outputs. Trash. Air pollution.
%Cities generate noise. NYC soil contamination. Water contamination.