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1 - 10 of 10 results for: data for sustainable development

BIO 138: Ecosystem Services: Frontiers in the Science of Valuing Nature (BIO 238, EARTHSYS 139, EARTHSYS 239)

This course explores the science of valuing nature, beginning with its historical origins and then a primary focus on its recent development and frontiers. The principal aim of the course is to enable new research and real-world applications of InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) tools and approaches. We will discuss the interconnections between people and nature and key research frontiers, such as in the realms of biodiversity, resilience, human health, poverty alleviation, and sustainable development. The science we¿ll explore is in the service of decisions, and we will use examples from real life to illustrate why this science is so critical to informing why, where, how, and how much people need nature. Prerequisite. Basic to intermediate GIS skills are required (including working with raster, vector and tabular data; loading and editing rasters, shapefiles, and tables into a GIS; understanding coordinate systems; and performing basic raster math).
Terms: Aut | Units: 3

BIO 238: Ecosystem Services: Frontiers in the Science of Valuing Nature (BIO 138, EARTHSYS 139, EARTHSYS 239)

This course explores the science of valuing nature, beginning with its historical origins and then a primary focus on its recent development and frontiers. The principal aim of the course is to enable new research and real-world applications of InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) tools and approaches. We will discuss the interconnections between people and nature and key research frontiers, such as in the realms of biodiversity, resilience, human health, poverty alleviation, and sustainable development. The science we¿ll explore is in the service of decisions, and we will use examples from real life to illustrate why this science is so critical to informing why, where, how, and how much people need nature. Prerequisite. Basic to intermediate GIS skills are required (including working with raster, vector and tabular data; loading and editing rasters, shapefiles, and tables into a GIS; understanding coordinate systems; and performing basic raster math).
Terms: Aut | Units: 3

CS 325B: Data for Sustainable Development (EARTHSYS 162, EARTHSYS 262)

The sustainable development goals (SDGs) encompass many important aspects of human and ecosystem well-being that are traditionally difficult to measure. This project-based course will focus on ways to use inexpensive, unconventional data streams to measure outcomes relevant to SDGs, including poverty, hunger, health, governance, and economic activity. Students will apply machine learning techniques to various projects outlined at the beginning of the quarter. The main learning goals are to gain experience conducting and communicating original research. Prior knowledge of machine learning techniques, such as from CS 221, CS 229, CS 231N, STATS 202, or STATS 216 is required. Open to both undergraduate and graduate students. Enrollment limited to 24. Students must apply for the class by filling out the form at https://goo.gl/forms/9LSZF7lPkHadix5D3. A permission code will be given to admitted students to register for the class.
Terms: Aut | Units: 3-5 | Repeatable for credit

EARTHSYS 139: Ecosystem Services: Frontiers in the Science of Valuing Nature (BIO 138, BIO 238, EARTHSYS 239)

This course explores the science of valuing nature, beginning with its historical origins and then a primary focus on its recent development and frontiers. The principal aim of the course is to enable new research and real-world applications of InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) tools and approaches. We will discuss the interconnections between people and nature and key research frontiers, such as in the realms of biodiversity, resilience, human health, poverty alleviation, and sustainable development. The science we¿ll explore is in the service of decisions, and we will use examples from real life to illustrate why this science is so critical to informing why, where, how, and how much people need nature. Prerequisite. Basic to intermediate GIS skills are required (including working with raster, vector and tabular data; loading and editing rasters, shapefiles, and tables into a GIS; understanding coordinate systems; and performing basic raster math).
Terms: Aut | Units: 3

EARTHSYS 162: Data for Sustainable Development (CS 325B, EARTHSYS 262)

The sustainable development goals (SDGs) encompass many important aspects of human and ecosystem well-being that are traditionally difficult to measure. This project-based course will focus on ways to use inexpensive, unconventional data streams to measure outcomes relevant to SDGs, including poverty, hunger, health, governance, and economic activity. Students will apply machine learning techniques to various projects outlined at the beginning of the quarter. The main learning goals are to gain experience conducting and communicating original research. Prior knowledge of machine learning techniques, such as from CS 221, CS 229, CS 231N, STATS 202, or STATS 216 is required. Open to both undergraduate and graduate students. Enrollment limited to 24. Students must apply for the class by filling out the form at https://goo.gl/forms/9LSZF7lPkHadix5D3. A permission code will be given to admitted students to register for the class.
Terms: Aut | Units: 3-5 | Repeatable for credit

EARTHSYS 239: Ecosystem Services: Frontiers in the Science of Valuing Nature (BIO 138, BIO 238, EARTHSYS 139)

This course explores the science of valuing nature, beginning with its historical origins and then a primary focus on its recent development and frontiers. The principal aim of the course is to enable new research and real-world applications of InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) tools and approaches. We will discuss the interconnections between people and nature and key research frontiers, such as in the realms of biodiversity, resilience, human health, poverty alleviation, and sustainable development. The science we¿ll explore is in the service of decisions, and we will use examples from real life to illustrate why this science is so critical to informing why, where, how, and how much people need nature. Prerequisite. Basic to intermediate GIS skills are required (including working with raster, vector and tabular data; loading and editing rasters, shapefiles, and tables into a GIS; understanding coordinate systems; and performing basic raster math).
Terms: Aut | Units: 3

EARTHSYS 262: Data for Sustainable Development (CS 325B, EARTHSYS 162)

The sustainable development goals (SDGs) encompass many important aspects of human and ecosystem well-being that are traditionally difficult to measure. This project-based course will focus on ways to use inexpensive, unconventional data streams to measure outcomes relevant to SDGs, including poverty, hunger, health, governance, and economic activity. Students will apply machine learning techniques to various projects outlined at the beginning of the quarter. The main learning goals are to gain experience conducting and communicating original research. Prior knowledge of machine learning techniques, such as from CS 221, CS 229, CS 231N, STATS 202, or STATS 216 is required. Open to both undergraduate and graduate students. Enrollment limited to 24. Students must apply for the class by filling out the form at https://goo.gl/forms/9LSZF7lPkHadix5D3. A permission code will be given to admitted students to register for the class.
Terms: Aut | Units: 3-5 | Repeatable for credit

ESS 268: Empirical Methods in Sustainable Development (INTLPOL 272)

The determinants of human well-being over the short and long-run, including the role of environmental factors in shaping development outcomes. A focus on the empirical literature across both social and natural sciences, with discussion and assignments emphasizing empirical analysis of environment-development linkages, application of methods in causal inference, and data visualization.
Terms: Win | Units: 3-5
Instructors: Burke, M. (PI)

INTLPOL 272: Empirical Methods in Sustainable Development (ESS 268)

The determinants of human well-being over the short and long-run, including the role of environmental factors in shaping development outcomes. A focus on the empirical literature across both social and natural sciences, with discussion and assignments emphasizing empirical analysis of environment-development linkages, application of methods in causal inference, and data visualization.
Last offered: Winter 2019

SINY 172: Sustainable and Resilient Urban Systems in New York City

The objective of the course is to develop a qualitative and quantitative understanding of sustainability and resilience for major urban areas. The first part of the quarter will focus on sustainability and the second on resilience. n nThe course will commence with an overview of the 17 Sustainable Development Goals (SDG) as defined by the United Nations and how New York City is working towards these goals. The components and pillars that lead to a sustainable urban environment will be defined and corresponding metrics will be used to quantify sustainability utilizing simple data analytics tools. Challenges to meeting these goals will be an important part of reading and project assignments. Examples of sustainability efforts within New York City (NYC) include the development of new parks or renewal of industrial areas for recreational use, design of green buildings, rooftop farms and gardens, and the renovation of transportation facilities as focal points. Field trips will include visit more »
The objective of the course is to develop a qualitative and quantitative understanding of sustainability and resilience for major urban areas. The first part of the quarter will focus on sustainability and the second on resilience. n nThe course will commence with an overview of the 17 Sustainable Development Goals (SDG) as defined by the United Nations and how New York City is working towards these goals. The components and pillars that lead to a sustainable urban environment will be defined and corresponding metrics will be used to quantify sustainability utilizing simple data analytics tools. Challenges to meeting these goals will be an important part of reading and project assignments. Examples of sustainability efforts within New York City (NYC) include the development of new parks or renewal of industrial areas for recreational use, design of green buildings, rooftop farms and gardens, and the renovation of transportation facilities as focal points. Field trips will include visits to the Highline, the Hudson Yard and the Bank of America building. n nResilience of urban systems will be the focus of the second half of the class. Issues of resilience will be discussed in relation to major disasters including floods, extreme storms and climate change. The 7 global resilience targets will be identified according to the Sendai Framework for Disaster Risk Reduction. The major components of resilience will be reviewed in class followed by a visit to or by a representative from the 100 Resilient City Initiative supported by the Rockefeller Foundation. The Superstorm Sandy in 2012 will be used as a case study to identify the disaster impact to the urban environment in NYC. We will discuss the emergency response of the City after Sandy and assess the performance of the NYC resilience plan. We will explore how New York City is preparing to respond and recover from future major disasters as part of the 100 Resilient Cities Initiative supported by the Rockefeller Foundation. n nNovel technologies will be discussed in terms of their implications for disaster resilience and sustainability. We will delve into a case study showing how modern and decentralized power systems with rooftop solar panels and microgrids can make NYC more resilient and sustainable.
Terms: Spr | Units: 4
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