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Much of the research regarding traffic crashes considers various geometric roadway features; however, ever-evolving urban watersheds and climate change increasingly impact roadway conditions. Little research has focused on the relationship between high-resolution drainage characteristics and the spatial distribution of crashes. This study incorporated local environmental and drainage risk factors in assessing network safety performance using spatial analysis techniques. Kernel density surfaces and the Local Getis Ord Gi* statistics were used to identify and visualize locations prone to experiencing crashes during wet conditions. Spatial regression modelling was used to link crashes to environmental and traffic risk factors across a citywide network. Proof-of-concept for this framework is demonstrated in the City of Philadelphia, Pennsylvania using publicly available spatial data. The results of this study show a relationship between local drainage, environmental characteristics, and wet crash distribution, providing novel insight into roadway safety during wet conditions.
Michael Crimmins; Seri Park; Virginia Smith; Peleg Kremer. A spatial assessment of high-resolution drainage characteristics and roadway safety during wet conditions. Applied Geography 2021, 133, 102477 .
AMA StyleMichael Crimmins, Seri Park, Virginia Smith, Peleg Kremer. A spatial assessment of high-resolution drainage characteristics and roadway safety during wet conditions. Applied Geography. 2021; 133 ():102477.
Chicago/Turabian StyleMichael Crimmins; Seri Park; Virginia Smith; Peleg Kremer. 2021. "A spatial assessment of high-resolution drainage characteristics and roadway safety during wet conditions." Applied Geography 133, no. : 102477.
Surface temperature influences human health directly and alters the biodiversity and productivity of the environment. While previous research has identified that the composition of urban landscapes influences the physical properties of the environment such as surface temperature, a generalizable and flexible framework is needed that can be used to compare cities across time and space. This study employs the Structure of Urban Landscapes (STURLA) classification combined with remote sensing of New York City’s (NYC) surface temperature. These are then linked using machine learning and statistical modeling to identify how greenspace and the built environment influence urban surface temperature. It was observed that areas with urban units composed of largely the built environment hosted the hottest temperatures while those with vegetation and water were coolest. Likewise, this is reinforced by borough-level spatial differences in both urban structure and heat. Comparison of these relationships over the period between2008 and 2017 identified changes in surface temperature that are likely due to the changes in prevalence in water, lowrise buildings, and pavement across the city. This research reinforces how human alteration of the environment changes ecosystem function and offers units of analysis that can be used for research and urban planning.
Justin Stewart; Peleg Kremer. Temporal Change in Relationships Between Urban Structure and Surface Temperature. 2021, 1 .
AMA StyleJustin Stewart, Peleg Kremer. Temporal Change in Relationships Between Urban Structure and Surface Temperature. . 2021; ():1.
Chicago/Turabian StyleJustin Stewart; Peleg Kremer. 2021. "Temporal Change in Relationships Between Urban Structure and Surface Temperature." , no. : 1.
Understanding the relationships between land cover/urban structure patterns and air pollutants is key to sustainable urban planning and development. In this study, we employ a mobile monitoring method to collect PM2.5 and BC data in the city of Philadelphia, PA during the summer of 2019 and apply the Structure of Urban Landscapes (STURLA) methodology to examine relationships between urban structure and atmospheric pollution. We find that, while PM2.5 and BC vary by STURLA class, many of the differences in pollutant concentrations between classes are not significant. However, we also find that the proportions in which STURLA components are present throughout the urban landscape can be used to predict urban air pollution. Among frequently sampled STURLA classes, gpl hosted the highest PM2.5 concentrations on average (16.60 ± 4.29 µg/m3), while tgbwp hosted the highest BC concentrations (2.31 ± 1.94 µg/m3). Furthermore, STURLA combined with machine learning modeling was able to correlate PM2.5 (R2= 0.68, RMSE 2.82 µg/m3) and BC (R2 = 0.64, RMSE 0.75 µg/m3) concentrations with the urban landscape and spatially interpolate concentrations where sampling did not take place. These results demonstrate the efficacy of the STURLA methodology in modeling relationships between air pollution and land cover/urban structure patterns.
Lucas Cummings; Justin Stewart; Peleg Kremer; Kabindra Shakya. Predicting Citywide Distribution of Air Pollution Using Mobile Monitoring and Three-dimensional Urban Structure. 2021, 1 .
AMA StyleLucas Cummings, Justin Stewart, Peleg Kremer, Kabindra Shakya. Predicting Citywide Distribution of Air Pollution Using Mobile Monitoring and Three-dimensional Urban Structure. . 2021; ():1.
Chicago/Turabian StyleLucas Cummings; Justin Stewart; Peleg Kremer; Kabindra Shakya. 2021. "Predicting Citywide Distribution of Air Pollution Using Mobile Monitoring and Three-dimensional Urban Structure." , no. : 1.
Discerning the relationship between urban structure and function is crucial for sustainable city planning and requires examination of how components in urban systems are organized in three-dimensional space. The Structure of Urban Landscape (STURLA) classification accounts for the compositional complexity of urban landcover structures including the built and natural environment. Building on previous research, we develop a STURLA classification for Philadelphia, PA and study the relationship between urban structure and land surface temperature. We evaluate the results in Philadelphia as compared to previous case studies in Berlin, Germany and New York City, United States. In Philadelphia, STURLA classes hosted ST that were unique and significantly different as compared to all other classes. We find a similar distribution of STURLA class composition across the three cities, though NYC and Berlin showed strong correlation with each other but not with Philadelphia. Our research highlights the use of STURLA classification to capture a physical property of the urban landscape
Erik Mitz; Peleg Kremer; Neele Larondelle; Justin Stewart. Structure of Urban Landscape and Surface Temperature: A Case Study in Philadelphia, PA. Frontiers in Environmental Science 2021, 9, 1 .
AMA StyleErik Mitz, Peleg Kremer, Neele Larondelle, Justin Stewart. Structure of Urban Landscape and Surface Temperature: A Case Study in Philadelphia, PA. Frontiers in Environmental Science. 2021; 9 ():1.
Chicago/Turabian StyleErik Mitz; Peleg Kremer; Neele Larondelle; Justin Stewart. 2021. "Structure of Urban Landscape and Surface Temperature: A Case Study in Philadelphia, PA." Frontiers in Environmental Science 9, no. : 1.
Lucas E Cummings; Justin Stewart; Radley Reist; Kabindra M Shakya; Peleg Kremer. Exploring the Spatiotemporal Variation of Air Pollution Throughout the Urban Landscape of Philadelphia, PA with Mobile Monitoring. 2020, 1 .
AMA StyleLucas E Cummings, Justin Stewart, Radley Reist, Kabindra M Shakya, Peleg Kremer. Exploring the Spatiotemporal Variation of Air Pollution Throughout the Urban Landscape of Philadelphia, PA with Mobile Monitoring. . 2020; ():1.
Chicago/Turabian StyleLucas E Cummings; Justin Stewart; Radley Reist; Kabindra M Shakya; Peleg Kremer. 2020. "Exploring the Spatiotemporal Variation of Air Pollution Throughout the Urban Landscape of Philadelphia, PA with Mobile Monitoring." , no. : 1.
Discerning the relationship between urban structure and function is crucial for sustainable city planning and requires examination of how components in urban systems are organized in three-dimensional space. The Structure of Urban Landscape (STURLA) classification accounts for the compositional complexity of urban landcover structures including the built and natural environment. Building on previous research, we develop a STURLA classification for Philadelphia, PA and study the relationship between urban 1 structure and land surface temperature. Finally, we evaluate the results in Philadelphia as compared to previous case studies in Berlin, Germany and New York City, USA. In Philadelphia, STURLA classes hosted ST that were unique and significantly different as compared to all other classes. We find a similar distribution of STURLA class composition across the three cities, though NYC and Berlin showed strong correlation with each other but not with Philadelphia. Our research highlights the use of STURLA classification to capture a physical property of the urban landscape.
Erik Mitz; Peleg KremeriD; Neele Larondelle; Justin StewartiD. Structure of Urban Landscape and Surface Temperature: a Case Study in Philadelphia, PA. 2020, 1 .
AMA StyleErik Mitz, Peleg KremeriD, Neele Larondelle, Justin StewartiD. Structure of Urban Landscape and Surface Temperature: a Case Study in Philadelphia, PA. . 2020; ():1.
Chicago/Turabian StyleErik Mitz; Peleg KremeriD; Neele Larondelle; Justin StewartiD. 2020. "Structure of Urban Landscape and Surface Temperature: a Case Study in Philadelphia, PA." , no. : 1.
Discerning the relationship between urban structure and function is crucial for sustainable city planning and requires examination of how components in urban systems are organized in three-dimensional space. The Structure of Urban Landscape (STURLA) classification accounts for the compositional complexity of urban landcover structures including the built and natural environment. Building on previous research, we develop a STURLA classification for Philadelphia, PA and study the relationship between urban 1 structure and land surface temperature. Finally, we evaluate the results in Philadelphia as compared to previous case studies in Berlin, Germany and New York City, USA. In Philadelphia, STURLA classes hosted ST that were unique and significantly different as compared to all other classes. We find a similar distribution of STURLA class composition across the three cities, though NYC and Berlin showed strong correlation with each other but not with Philadelphia. Our research highlights the use of STURLA classification to capture a physical property of the urban landscape.
Erik Mitz; Peleg KremeriD; Neele Larondelle; Justin StewartiD. Structure of Urban Landscape and Surface Temperature: a Case Study in Philadelphia, PA. 2020, 1 .
AMA StyleErik Mitz, Peleg KremeriD, Neele Larondelle, Justin StewartiD. Structure of Urban Landscape and Surface Temperature: a Case Study in Philadelphia, PA. . 2020; ():1.
Chicago/Turabian StyleErik Mitz; Peleg KremeriD; Neele Larondelle; Justin StewartiD. 2020. "Structure of Urban Landscape and Surface Temperature: a Case Study in Philadelphia, PA." , no. : 1.
Mitigating the effects of natural hazards through infrastructure planning requires integration of diverse types of information from a range of fields, including engineering, geography, social science, and geology. Challenges in data availability and previously siloed data have hindered the ability to obtain the information necessary to support decision making for disaster risk management. This is particularly challenging for areas susceptible to multiple types of natural hazards, especially in low-income communities that lack the resources for data collection. The data revolution is altering this landscape, due to the increased availability of remotely sensed data and global data repositories. This work seeks to leverage these advancements to develop a framework using open global datasets for identifying optimal locations for disaster relief shelters. The goal of this study is to empower low-income regions and make resilience more equitable by providing a multi-hazard shelter planning framework that is accessible to all decision-makers. The tool described integrates spatial multi-criteria decision analysis methods with a network analysis procedure to inform decisions regarding disaster shelter planning and siting.
Sarah Godschall; Virginia Smith; Jonathan Hubler; Peleg Kremer. A Decision Process for Optimizing Multi-Hazard Shelter Location Using Global Data. Sustainability 2020, 12, 6252 .
AMA StyleSarah Godschall, Virginia Smith, Jonathan Hubler, Peleg Kremer. A Decision Process for Optimizing Multi-Hazard Shelter Location Using Global Data. Sustainability. 2020; 12 (15):6252.
Chicago/Turabian StyleSarah Godschall; Virginia Smith; Jonathan Hubler; Peleg Kremer. 2020. "A Decision Process for Optimizing Multi-Hazard Shelter Location Using Global Data." Sustainability 12, no. 15: 6252.
Microbes are abundant inhabitants of the near-surface atmosphere in urban areas. The distribution of microbial communities may benefit or hinder human wellbeing and ecosystem function. Surveys of airborne microbial diversity are uncommon in both natural and built environments and those that investigate diversity are stationary in the city, thus missing continuous exposure to microbes that covary with three-dimensional urban structure. Individuals in cities are generally mobile and would be exposed to diverse urban structures outdoors and within indoor-transit systems in a day. We used mobile monitoring of microbial diversity and geographic information system spatial analysis, across Philadelphia, Pennsylvania, USA in outdoor and indoor-transit (subways and train cars) environments. This study identifies to the role of the three-dimensional urban landscape in structuring atmospheric microbiomes and employs mobile monitoring over ~1920 kilometers to measure continuous biodiversity. We found more diverse communities outdoors that significantly differ from indoor-transit air in microbial community structure, function, likely source environment, and potentially pathogenic fraction of the community. Variation in the structure of the urban landscape was associated with diversity and function of the near-surface atmospheric microbiome in outdoor samples.ImportanceGlobal nutrient cycling and human health depend on the rich biodiversity of microorganisms. The influence of the urban environment on microbiomes remains poorly described despite cities being the fastest growing ecosystems. All life is exposed to the atmosphere and thus discerning what microbes are present and what their functions may be is critical to create resistant and resilient cities under climate change. This study combines a spatially explicit analysis of urban structure with mobile monitoring of the atmospheric microbiome.
Jd Stewart; P. Kremer; Kabindra Shakya; M. Conway; A. Saad. Outdoor Atmospheric Microbial Diversity is Associated with Urban Landscape Structure and Differs from Indoor-Transit Systems as Revealed by Mobile Monitoring and Three-Dimensional Spatial Analysis. 2020, 1 .
AMA StyleJd Stewart, P. Kremer, Kabindra Shakya, M. Conway, A. Saad. Outdoor Atmospheric Microbial Diversity is Associated with Urban Landscape Structure and Differs from Indoor-Transit Systems as Revealed by Mobile Monitoring and Three-Dimensional Spatial Analysis. . 2020; ():1.
Chicago/Turabian StyleJd Stewart; P. Kremer; Kabindra Shakya; M. Conway; A. Saad. 2020. "Outdoor Atmospheric Microbial Diversity is Associated with Urban Landscape Structure and Differs from Indoor-Transit Systems as Revealed by Mobile Monitoring and Three-Dimensional Spatial Analysis." , no. : 1.
This research presents a fully automated framework for runoff estimation, applied to Philadelphia, Pennsylvania, a major urban area. Trends in global urbanization are exacerbating stormwater runoff, making it an increasingly critical challenge in urban areas. Understanding the fine-scale spatial distribution of local flooding is difficult due to the complexity of the urban landscape and lack of measured data, but it is critical for urban management and development. A one-meter resolution Digital Elevation Model (DEM) was used in conjunction with a model developed by using ArcGIS Pro software to create urban micro-subbasins. The DEM was manipulated to account for roof drainage and stormwater infrastructure, such as inlets. The generated micro-subbasins paired with 24-h storm data with a 10-year return period taken from the National Resources Conservation Service (NRCS) for the Philadelphia area was used to estimate runoff. One-meter land-cover and land-use data were used to estimate pervious and impervious areas and the runoff coefficients for each subbasin. Peak runoff discharge and runoff depth for each subbasin were then estimated by the rational and modified rational methods and the NRCS method. The inundation depths from the NRCS method and the modified rational method models were compared and used to generate percent agreement, maximum, and average of inundation maps of Philadelphia. The outcome of this research provides a clear picture of the spatial likelihood of experiencing negative effects of excessive precipitation, useful for stormwater management agencies, city managers, and citizen.
Hossein Hosseiny; Michael Crimmins; Virginia B. Smith; Peleg Kremer. A Generalized Automated Framework for Urban Runoff Modeling and Its Application at a Citywide Landscape. Water 2020, 12, 357 .
AMA StyleHossein Hosseiny, Michael Crimmins, Virginia B. Smith, Peleg Kremer. A Generalized Automated Framework for Urban Runoff Modeling and Its Application at a Citywide Landscape. Water. 2020; 12 (2):357.
Chicago/Turabian StyleHossein Hosseiny; Michael Crimmins; Virginia B. Smith; Peleg Kremer. 2020. "A Generalized Automated Framework for Urban Runoff Modeling and Its Application at a Citywide Landscape." Water 12, no. 2: 357.
Charlotte Shade; Peleg Kremer; Julia S. Rockwell; Keith G. Henderson. The effects of urban development and current green infrastructure policy on future climate change resilience. Ecology and Society 2020, 25, 1 .
AMA StyleCharlotte Shade, Peleg Kremer, Julia S. Rockwell, Keith G. Henderson. The effects of urban development and current green infrastructure policy on future climate change resilience. Ecology and Society. 2020; 25 (4):1.
Chicago/Turabian StyleCharlotte Shade; Peleg Kremer; Julia S. Rockwell; Keith G. Henderson. 2020. "The effects of urban development and current green infrastructure policy on future climate change resilience." Ecology and Society 25, no. 4: 1.
Peleg Kremer; Annegret Haase; Dagmar Haase. The future of urban sustainability: Smart, efficient, green or just? Introduction to the special issue. Sustainable Cities and Society 2019, 51, 1 .
AMA StylePeleg Kremer, Annegret Haase, Dagmar Haase. The future of urban sustainability: Smart, efficient, green or just? Introduction to the special issue. Sustainable Cities and Society. 2019; 51 ():1.
Chicago/Turabian StylePeleg Kremer; Annegret Haase; Dagmar Haase. 2019. "The future of urban sustainability: Smart, efficient, green or just? Introduction to the special issue." Sustainable Cities and Society 51, no. : 1.
Peleg Kremer. WITHDRAWN: The future of urban sustainability: Smart, efficient, green or just? Introduction to the Special Issue. Sustainable Cities and Society 2019, 101604 .
AMA StylePeleg Kremer. WITHDRAWN: The future of urban sustainability: Smart, efficient, green or just? Introduction to the Special Issue. Sustainable Cities and Society. 2019; ():101604.
Chicago/Turabian StylePeleg Kremer. 2019. "WITHDRAWN: The future of urban sustainability: Smart, efficient, green or just? Introduction to the Special Issue." Sustainable Cities and Society , no. : 101604.
Urbanization is a rapid global trend, leading to consequences such as urban heat islands and local flooding. Imminent climate change is predicted to intensify these consequences, forcing cities to rethink common infrastructure practices. One popular method of adaptation is green infrastructure implementation, which has been found to reduce local temperatures and alleviate excess runoff when installed effectively. As cities continue to change and adapt, land use/landcover modeling becomes an important tool for city officials in planning future land usage. This study uses a combination of cellular automata, machine learning, and Markov chain analysis to predict high resolution land use/landcover changes in Philadelphia, PA, USA for the year 2036. The 2036 landcover model assumes full implementation of Philadelphia’s green infrastructure program and past temporal trends of urbanization. The methodology used to create the 2036 model was validated by creating an intermediate prediction of a 2015 landcover that was then compared to an existing 2015 landcover. The accuracy of the validation was determined using Kappa statistics and disagreement scores. The 2036 model successfully met Philadelphia’s green infrastructure goals. A variety of landscape metrics demonstrated an overall decrease in fragmentation throughout the landscape due to increases in urban landcover.
Charlotte Shade; Peleg Kremer. Predicting Land Use Changes in Philadelphia Following Green Infrastructure Policies. Land 2019, 8, 28 .
AMA StyleCharlotte Shade, Peleg Kremer. Predicting Land Use Changes in Philadelphia Following Green Infrastructure Policies. Land. 2019; 8 (2):28.
Chicago/Turabian StyleCharlotte Shade; Peleg Kremer. 2019. "Predicting Land Use Changes in Philadelphia Following Green Infrastructure Policies." Land 8, no. 2: 28.
Understanding why some parks are used more regularly or intensely than others can inform ways in which urban parkland is developed and managed to meet the needs of a rapidly expanding urban population. Although geolocated social media (GSM) indicators have been used to examine park visitation rates, studies applying this approach are generally limited to flagship parks, national parks, or a small subset of urban parks. Here, we use geolocated Flickr and Twitter data to explore variation in use across New York City's 2143 diverse parks and model visitation based on spatially-explicit park characteristics and facilities, neighborhood-level accessibility features and neighborhood-level demographics. Findings indicate that social media activity in parks is positively correlated with proximity to public transportation and bike routes, as well as particular park characteristics such as water bodies, athletic facilities, and impervious surfaces, but negatively associated with green space and increased proportion of minority ethnicity and minority race in neighborhoods in which parks are located. Contrary to previous studies which describe park visitation as a form of nature-based recreation, our findings indicate that the kinds of green spaces present in many parks may not motivate visitation. From a social equity perspective, our findings may imply that parks in high-minority neighborhoods are not as accessible, do not accommodate as many visitors, and/or are of lower quality than those in low-minority neighborhoods. These implications are consistent with previous studies showing that minority populations disproportionately experience barriers to park access. In applying GSM data to questions of park access, we demonstrate a rapid, big data approach for providing information crucial for park management in a way that is less resource-intensive than field surveys.
Zoé A. Hamstead; David Fisher; Rositsa T. Ilieva; Spencer A. Wood; Timon McPhearson; Peleg Kremer. Geolocated social media as a rapid indicator of park visitation and equitable park access. Computers, Environment and Urban Systems 2018, 72, 38 -50.
AMA StyleZoé A. Hamstead, David Fisher, Rositsa T. Ilieva, Spencer A. Wood, Timon McPhearson, Peleg Kremer. Geolocated social media as a rapid indicator of park visitation and equitable park access. Computers, Environment and Urban Systems. 2018; 72 ():38-50.
Chicago/Turabian StyleZoé A. Hamstead; David Fisher; Rositsa T. Ilieva; Spencer A. Wood; Timon McPhearson; Peleg Kremer. 2018. "Geolocated social media as a rapid indicator of park visitation and equitable park access." Computers, Environment and Urban Systems 72, no. : 38-50.
Cambridge Core - Climatology and Climate Change - Climate Change and Cities - edited by Cynthia Rosenzweig
Tom Bowman; Daniel A. Bader; Reginald Blake; Alice Grimm; Rafiq Hamdi; Yeonjoo Kim; Radley Horton; Keith Alverson; Stuart Gaffin; Stuart Crane; Ebru Gencer; Regina Folorunsho; Megan Linkin; XiaoMing Wang; Claudia E. Natenzon; Shiraz Wajih; Nivedita Mani; Maricarmen Esquivel; Hori Tsuneki; Ricardo Castro; Mattia Federico Leone; Panjwani Dilnoor; Romero-Lankao Patricia; Solecki William; Brenda Lin; Abhilash Panda; Stelios Grafakos; Chantal Pacteau; Martha Delgado; Mia Landauer; Oswaldo Lucon; Patrick Driscoll; David Wilk; Carolina Zambrano; Sean O’Donoghue; Debra Roberts; Jeffrey Raven; Brian Stone; Gerald Mills; Joel Towers; Lutz Katzschner; Pascaline Gaborit; Matei Georgescu; Maryam Hariri; James Lee; Jeffrey Lejava; Ayyoob Sharifi; Cristina Visconti; Andrew Rudd; Diana Reckien; Shuaib Lwasa; David Satterthwaite; Darryn McEvoy; Felix Creutzig; Mark Montgomery; Daniel Schensul; Deborah Balk; Iqbal Alam Khan; Blanca Fernandez; Donald Brown; Juan Camilo Osorio; Marcela Tovar-Restrepo; Alex De Sherbinin; Wim Feringa; Alice Sverdlik; Emma Porio; Abhishek Nair; Sabrina McCormick; Eddie Bautista; Reimund Schwarze; Peter B. Meyer; Anil Markandya; Shailly Kedia; David Maleki; María Victoria Román De Lara; Tomonori Sudo; Swenja Surminski; Nancy Anderson; Marta Olazabal; Saliha Dobardzic; Timon McPhearson; Madhav Karki; Cecilia Herzog; Helen Santiago Fink; Luc Abbadie; Peleg Kremer; Christopher M. Clark; Matthew I. Palmer; Katia Perini; Marielle Dubbeling; Richard J. Dawson; M. Shah Alam Khan; Vivien Gornitz; Maria Fernanda Lemos; Larry Atkinson; Julie Pullen; Lindsay Usher; Martha M. L. Barata; Patrick L. Kinney; Keith Dear; Eva Ligeti; Kristie L. Ebi; Jeremy Hess; Thea Dickinson; Ashlinn K. Quinn; Martin Obermaier; Denise Silva Sousa; Darby Jack; Livia Marinho; Felipe Vommaro; Kai Chen; Claudine Dereczynski; Mariana Carvalho; Diana Pinheiro Marinho; Nathalie Jean-Baptiste; Veronica Olivotto; Wilbard Kombe; Antonia Yulo-Loyzaga; Mussa Natty. Climate Change and Cities. Climate Change and Cities 2018, 1 .
AMA StyleTom Bowman, Daniel A. Bader, Reginald Blake, Alice Grimm, Rafiq Hamdi, Yeonjoo Kim, Radley Horton, Keith Alverson, Stuart Gaffin, Stuart Crane, Ebru Gencer, Regina Folorunsho, Megan Linkin, XiaoMing Wang, Claudia E. Natenzon, Shiraz Wajih, Nivedita Mani, Maricarmen Esquivel, Hori Tsuneki, Ricardo Castro, Mattia Federico Leone, Panjwani Dilnoor, Romero-Lankao Patricia, Solecki William, Brenda Lin, Abhilash Panda, Stelios Grafakos, Chantal Pacteau, Martha Delgado, Mia Landauer, Oswaldo Lucon, Patrick Driscoll, David Wilk, Carolina Zambrano, Sean O’Donoghue, Debra Roberts, Jeffrey Raven, Brian Stone, Gerald Mills, Joel Towers, Lutz Katzschner, Pascaline Gaborit, Matei Georgescu, Maryam Hariri, James Lee, Jeffrey Lejava, Ayyoob Sharifi, Cristina Visconti, Andrew Rudd, Diana Reckien, Shuaib Lwasa, David Satterthwaite, Darryn McEvoy, Felix Creutzig, Mark Montgomery, Daniel Schensul, Deborah Balk, Iqbal Alam Khan, Blanca Fernandez, Donald Brown, Juan Camilo Osorio, Marcela Tovar-Restrepo, Alex De Sherbinin, Wim Feringa, Alice Sverdlik, Emma Porio, Abhishek Nair, Sabrina McCormick, Eddie Bautista, Reimund Schwarze, Peter B. Meyer, Anil Markandya, Shailly Kedia, David Maleki, María Victoria Román De Lara, Tomonori Sudo, Swenja Surminski, Nancy Anderson, Marta Olazabal, Saliha Dobardzic, Timon McPhearson, Madhav Karki, Cecilia Herzog, Helen Santiago Fink, Luc Abbadie, Peleg Kremer, Christopher M. Clark, Matthew I. Palmer, Katia Perini, Marielle Dubbeling, Richard J. Dawson, M. Shah Alam Khan, Vivien Gornitz, Maria Fernanda Lemos, Larry Atkinson, Julie Pullen, Lindsay Usher, Martha M. L. Barata, Patrick L. Kinney, Keith Dear, Eva Ligeti, Kristie L. Ebi, Jeremy Hess, Thea Dickinson, Ashlinn K. Quinn, Martin Obermaier, Denise Silva Sousa, Darby Jack, Livia Marinho, Felipe Vommaro, Kai Chen, Claudine Dereczynski, Mariana Carvalho, Diana Pinheiro Marinho, Nathalie Jean-Baptiste, Veronica Olivotto, Wilbard Kombe, Antonia Yulo-Loyzaga, Mussa Natty. Climate Change and Cities. Climate Change and Cities. 2018; ():1.
Chicago/Turabian StyleTom Bowman; Daniel A. Bader; Reginald Blake; Alice Grimm; Rafiq Hamdi; Yeonjoo Kim; Radley Horton; Keith Alverson; Stuart Gaffin; Stuart Crane; Ebru Gencer; Regina Folorunsho; Megan Linkin; XiaoMing Wang; Claudia E. Natenzon; Shiraz Wajih; Nivedita Mani; Maricarmen Esquivel; Hori Tsuneki; Ricardo Castro; Mattia Federico Leone; Panjwani Dilnoor; Romero-Lankao Patricia; Solecki William; Brenda Lin; Abhilash Panda; Stelios Grafakos; Chantal Pacteau; Martha Delgado; Mia Landauer; Oswaldo Lucon; Patrick Driscoll; David Wilk; Carolina Zambrano; Sean O’Donoghue; Debra Roberts; Jeffrey Raven; Brian Stone; Gerald Mills; Joel Towers; Lutz Katzschner; Pascaline Gaborit; Matei Georgescu; Maryam Hariri; James Lee; Jeffrey Lejava; Ayyoob Sharifi; Cristina Visconti; Andrew Rudd; Diana Reckien; Shuaib Lwasa; David Satterthwaite; Darryn McEvoy; Felix Creutzig; Mark Montgomery; Daniel Schensul; Deborah Balk; Iqbal Alam Khan; Blanca Fernandez; Donald Brown; Juan Camilo Osorio; Marcela Tovar-Restrepo; Alex De Sherbinin; Wim Feringa; Alice Sverdlik; Emma Porio; Abhishek Nair; Sabrina McCormick; Eddie Bautista; Reimund Schwarze; Peter B. Meyer; Anil Markandya; Shailly Kedia; David Maleki; María Victoria Román De Lara; Tomonori Sudo; Swenja Surminski; Nancy Anderson; Marta Olazabal; Saliha Dobardzic; Timon McPhearson; Madhav Karki; Cecilia Herzog; Helen Santiago Fink; Luc Abbadie; Peleg Kremer; Christopher M. Clark; Matthew I. Palmer; Katia Perini; Marielle Dubbeling; Richard J. Dawson; M. Shah Alam Khan; Vivien Gornitz; Maria Fernanda Lemos; Larry Atkinson; Julie Pullen; Lindsay Usher; Martha M. L. Barata; Patrick L. Kinney; Keith Dear; Eva Ligeti; Kristie L. Ebi; Jeremy Hess; Thea Dickinson; Ashlinn K. Quinn; Martin Obermaier; Denise Silva Sousa; Darby Jack; Livia Marinho; Felipe Vommaro; Kai Chen; Claudine Dereczynski; Mariana Carvalho; Diana Pinheiro Marinho; Nathalie Jean-Baptiste; Veronica Olivotto; Wilbard Kombe; Antonia Yulo-Loyzaga; Mussa Natty. 2018. "Climate Change and Cities." Climate Change and Cities , no. : 1.
Understanding the relationship between urban structure and ecological function—or environmental performance—is important for the planning of sustainable cities, and requires examination of how components in urban systems are organized. In this paper, we develop a Structure of Urban Landscape (STURLA) classification, identifying common compositions of urban components using Berlin, Germany as a case study. We compute the surface temperature corresponding to each classification grid cell, and perform within-cell and neighborhood analysis for the most common composite classes in Berlin. We found that with-class composition and neighborhood composition as well as the interaction between them drive surface temperature. Our findings suggest that the spatial organization of urban components is important in determining the surface temperature and that specific combinations, such as low-rise buildings surrounded by neighborhood trees, or mid-rise buildings surrounded by high-rise buildings, compound to create a cooling effect. These findings are important for developing an understanding of how urban planning can harness structure-function relationships and improve urban sustainability.
Peleg Kremer; Neele Larondelle; Yimin Zhang; Elise Pasles; Dagmar Haase. Within-Class and Neighborhood Effects on the Relationship between Composite Urban Classes and Surface Temperature. Sustainability 2018, 10, 645 .
AMA StylePeleg Kremer, Neele Larondelle, Yimin Zhang, Elise Pasles, Dagmar Haase. Within-Class and Neighborhood Effects on the Relationship between Composite Urban Classes and Surface Temperature. Sustainability. 2018; 10 (3):645.
Chicago/Turabian StylePeleg Kremer; Neele Larondelle; Yimin Zhang; Elise Pasles; Dagmar Haase. 2018. "Within-Class and Neighborhood Effects on the Relationship between Composite Urban Classes and Surface Temperature." Sustainability 10, no. 3: 645.
Greening cities, namely installing new parks, rooftop gardens or planting trees along the streets, undoubtedly contributes to an increase in wellbeing and enhances the attractiveness of open spaces in cities. At the same time, we observe an increasing use of greening strategies as ingredients of urban renewal, upgrading and urban revitalization as primarily market-driven endeavours targeting middle class and higher income groups sometimes at the expense of less privileged residents. This paper reflects on the current debate of the social effects of greening using selected examples. We discuss what trade-offs between social and ecological developments in cities mean for the future debate on greening cities and a socially balanced and inclusive way of developing our cities for various groups of urban dwellers. We conclude that current and future functions and features of greening cities have to be discussed more critically including a greater awareness of social impacts
Dagmar Haase; Sigrun Kabisch; Annegret Haase; Erik Andersson; Ellen Banzhaf; Francesc Baró; Miriam Brenck; Leonie K. Fischer; Niki Frantzeskaki; Nadja Kabisch; Kerstin Krellenberg; Peleg Kremer; Jakub Kronenberg; Neele Larondelle; Juliane Mathey; Stephan Pauleit; Irene Ring; Dieter Rink; Nina Schwarz; Manuel Wolff. Greening cities – To be socially inclusive? About the alleged paradox of society and ecology in cities. Habitat International 2017, 64, 41 -48.
AMA StyleDagmar Haase, Sigrun Kabisch, Annegret Haase, Erik Andersson, Ellen Banzhaf, Francesc Baró, Miriam Brenck, Leonie K. Fischer, Niki Frantzeskaki, Nadja Kabisch, Kerstin Krellenberg, Peleg Kremer, Jakub Kronenberg, Neele Larondelle, Juliane Mathey, Stephan Pauleit, Irene Ring, Dieter Rink, Nina Schwarz, Manuel Wolff. Greening cities – To be socially inclusive? About the alleged paradox of society and ecology in cities. Habitat International. 2017; 64 ():41-48.
Chicago/Turabian StyleDagmar Haase; Sigrun Kabisch; Annegret Haase; Erik Andersson; Ellen Banzhaf; Francesc Baró; Miriam Brenck; Leonie K. Fischer; Niki Frantzeskaki; Nadja Kabisch; Kerstin Krellenberg; Peleg Kremer; Jakub Kronenberg; Neele Larondelle; Juliane Mathey; Stephan Pauleit; Irene Ring; Dieter Rink; Nina Schwarz; Manuel Wolff. 2017. "Greening cities – To be socially inclusive? About the alleged paradox of society and ecology in cities." Habitat International 64, no. : 41-48.
Zoé A. Hamstead; Peleg Kremer; Neele Larondelle; Timon McPhearson; Dagmar Haase. Classification of the heterogeneous structure of urban landscapes (STURLA) as an indicator of landscape function applied to surface temperature in New York City. Ecological Indicators 2016, 70, 574 -585.
AMA StyleZoé A. Hamstead, Peleg Kremer, Neele Larondelle, Timon McPhearson, Dagmar Haase. Classification of the heterogeneous structure of urban landscapes (STURLA) as an indicator of landscape function applied to surface temperature in New York City. Ecological Indicators. 2016; 70 ():574-585.
Chicago/Turabian StyleZoé A. Hamstead; Peleg Kremer; Neele Larondelle; Timon McPhearson; Dagmar Haase. 2016. "Classification of the heterogeneous structure of urban landscapes (STURLA) as an indicator of landscape function applied to surface temperature in New York City." Ecological Indicators 70, no. : 574-585.
Mapping, modeling, and valuing urban ecosystem services are important for integrating the ecosystem services concept in urban planning and decision-making. However, decision-support tools able to consider multiple ecosystem services in the urban setting using complex and heterogeneous data are still in early development. Here, we use New York City (NYC) as a case study to evaluate and analyze how the value of multiple ecosystem services of urban green infrastructure shifts with shifting governance priorities. We first examined the spatial distribution of five ecosystem services – storm water absorption, carbon storage, air pollution removal, local climate regulation, and recreation – to create the first multiple ecosystem services evaluation of all green infrastructure in NYC. Then, combining an urban ecosystem services landscape approach with spatial multicriteria analysis weighting scenarios, we examine the distribution of these ecosystem services in the city. We contrast the current NYC policy preference – which is focused on heavy investment in stormwater absorption – with a valuation approach that also accounts for other ecosystem services. We find substantial differences in the spatial distribution of priority areas for green infrastructure for the valuation scenarios. Among the scenarios we examined for NYC, we find that a scenario in which only stormwater absorption is prioritized leads to the most unevenly distributed ES values. By contrast, we find least variation in ES values where stormwater absorption, local climate regulation, carbon storage, air pollution removal, and recreational potential are all weighted equally. We suggest that green infrastructure planning strategies should include all landscape components that contribute to the production of ecosystem services and consider how planning priority alternatives generate different ecosystem services values.
Peleg Kremer; Zoé A. Hamstead; Timon McPhearson. The value of urban ecosystem services in New York City: A spatially explicit multicriteria analysis of landscape scale valuation scenarios. Environmental Science & Policy 2016, 62, 57 -68.
AMA StylePeleg Kremer, Zoé A. Hamstead, Timon McPhearson. The value of urban ecosystem services in New York City: A spatially explicit multicriteria analysis of landscape scale valuation scenarios. Environmental Science & Policy. 2016; 62 ():57-68.
Chicago/Turabian StylePeleg Kremer; Zoé A. Hamstead; Timon McPhearson. 2016. "The value of urban ecosystem services in New York City: A spatially explicit multicriteria analysis of landscape scale valuation scenarios." Environmental Science & Policy 62, no. : 57-68.