Governing Urban Schools in the Future: What's Facing Philadelphia and Pennsylvania

In 2001, the state of Pennsylvania took over the School District of Philadelphia. The move, which came with the consent of the city's mayor and included an increase in state and city funds for the schools, followed years of turmoil during which the district twice sued the state over funding, the superintendent resigned in frustration, and state officials pushed to have a private company manage Philadelphia's schools. The School Reform Commission (SRC), which was created as part of the takeover, runs the district, with three members appointed by the governor and two by the mayor. That arrangement has been the subject of continuing debate, with education advocates calling for a return to local control -- and Pennsylvania Governor Tom Wolf and former Philadelphia Mayor Michael Nutter also advocating an end to the state takeover of the city's schools.Given that debate, The Pew Charitable Trusts commissioned an analysis comparing key elements of Philadelphia's school governance system with those of 15 other major urban districts. The districts -- serving Atlanta, Baltimore, Boston, Chicago, Cleveland, Denver, Detroit, Houston, Indianapolis, Los Angeles, Miami-Dade, Milwaukee, Newark (NJ), New York, and St. Paul (MN) -- were chosen for their size and their demographic and economic similarities to Philadelphia.Three key findings emerged:Ten of the 15 districts studied and more than 90 percent of those in the U.S. are run by elected school boards; those in Baltimore, Boston, Chicago, Cleveland, and New York are not. The School District of Philadelphia has never had an elected board.Of the 15 districts, only Baltimore, Boston, and New York lack the authority to raise revenue on their own -- relying instead on city government for the entire local share of the school system's operating funds. This has always been the case in Philadelphia, even before the state takeover. While the absence of taxing power is rare among districts nationally, it is not uncommon in large northeastern cities.In all of the districts studied that have experienced some form of state intervention, the governance change has been long-lasting; in Philadelphia, it is entering its 15th year in 2016.There is no consensus among researchers about whether any particular form of school governance -- including state takeovers, mayoral control, or elected local boards -- leads to better student performance or fiscal management. But there is strong agreement that any governance system must avoid uncertainty about responsibility and accountability in order for schools to make progress.If and when state control of the School District of Philadelphia comes to an end, policymakers will have to decide how to govern the city's schools. This brief is intended to inform those decisions

Philadelphia's Less Crowded, Less Costly Jails: Taking Stock of a Year of Change and the Challenges That Remain

Examines factors behind the 2009-10 decrease in Philadelphia's jail population; strategies for managing pretrial, sentenced, and probation or parole violator populations; and policies for further streamlining court processes and reducing jail populations

A New Way of Looking at Philadelphians

Pew has created a new way of looking at Philadelphians, one rooted in how they think about the city rather than where they show up in demographic categories. The analysis, based on a telephone survey of 1,603 randomly chosen Philadelphians in early 2015, sorts adult city residents into four groups. We have labeled those groups Dissatisfied Citizens, Die-Hard Loyalists, Uncommitted Skeptics, and Enthusiastic Urbanists. This effort was modeled on work done nationally by our colleagues at the Pew Research Center in Washington. Through this type of polling and analysis, the center has sorted Americans into groupings based on values and attitudes, going beyond the simple labels of liberal and conservative. For Philadelphia, we set out to do something similar -- although not on the left-right spectrum -- in hopes of increasing public understanding of the city and its residents

Philadelphia Research Initiative Public Opinion Poll 2009

Presents survey results on Philadelphians' views on the mayor's performance, the city's fiscal situation, the mayor's budget cut proposals, and the balance between services and taxes. Analyzes results by demographics and in comparison with past surveys

Convolutional Neural Networks accurately predict cover fractions of plant species and communities in Unmanned Aerial Vehicle imagery

Unmanned Aerial Vehicles (UAV) greatly extended our possibilities to acquire high resolution remote sensing data for assessing the spatial distribution of species composition and vegetation characteristics. Yet, current pixel‐ or texture‐based mapping approaches do not fully exploit the information content provided by the high spatial resolution. Here, to fully harness this spatial detail, we apply deep learning techniques, that is, Convolutional Neural Networks (CNNs), on regular tiles of UAV‐orthoimagery (here 2–5 m) to identify the cover of target plant species and plant communities. The approach was tested with UAV‐based orthomosaics and photogrammetric 3D information in three case studies, that is, (1) mapping tree species cover in primary forests, (2) mapping plant invasions by woody species into forests and open land and (3) mapping vegetation succession in a glacier foreland. All three case studies resulted in high predictive accuracies. The accuracy increased with increasing tile size (2–5 m) reflecting the increased spatial context captured by a tile. The inclusion of 3D information derived from the photogrammetric workflow did not significantly improve the models. We conclude that CNN are powerful in harnessing high resolution data acquired from UAV to map vegetation patterns. The study was based on low cost red, green, blue (RGB) sensors making the method accessible to a wide range of users. Combining UAV and CNN will provide tremendous opportunities for ecological applications

Convolutional Neural Networks accurately predict cover fractions of plant species and communities in Unmanned Aerial Vehicle imagery

Abstract Unmanned Aerial Vehicles (UAV) greatly extended our possibilities to acquire high resolution remote sensing data for assessing the spatial distribution of species composition and vegetation characteristics. Yet, current pixel- or texture-based mapping approaches do not fully exploit the information content provided by the high spatial resolution. Here, to fully harness this spatial detail, we apply deep learning techniques, that is, Convolutional Neural Networks (CNNs), on regular tiles of UAV-orthoimagery (here 2–5 m) to identify the cover of target plant species and plant communities. The approach was tested with UAV-based orthomosaics and photogrammetric 3D information in three case studies, that is, (1) mapping tree species cover in primary forests, (2) mapping plant invasions by woody species into forests and open land and (3) mapping vegetation succession in a glacier foreland. All three case studies resulted in high predictive accuracies. The accuracy increased with increasing tile size (2–5 m) reflecting the increased spatial context captured by a tile. The inclusion of 3D information derived from the photogrammetric workflow did not significantly improve the models. We conclude that CNN are powerful in harnessing high resolution data acquired from UAV to map vegetation patterns. The study was based on low cost red, green, blue (RGB) sensors making the method accessible to a wide range of users. Combining UAV and CNN will provide tremendous opportunities for ecological applications

Convolutional Neural Networks accurately predict cover fractions of plant species and communities in Unmanned Aerial Vehicle imagery

Abstract Unmanned Aerial Vehicles (UAV) greatly extended our possibilities to acquire high resolution remote sensing data for assessing the spatial distribution of species composition and vegetation characteristics. Yet, current pixel- or texture-based mapping approaches do not fully exploit the information content provided by the high spatial resolution. Here, to fully harness this spatial detail, we apply deep learning techniques, that is, Convolutional Neural Networks (CNNs), on regular tiles of UAV-orthoimagery (here 2–5 m) to identify the cover of target plant species and plant communities. The approach was tested with UAV-based orthomosaics and photogrammetric 3D information in three case studies, that is, (1) mapping tree species cover in primary forests, (2) mapping plant invasions by woody species into forests and open land and (3) mapping vegetation succession in a glacier foreland. All three case studies resulted in high predictive accuracies. The accuracy increased with increasing tile size (2–5 m) reflecting the increased spatial context captured by a tile. The inclusion of 3D information derived from the photogrammetric workflow did not significantly improve the models. We conclude that CNN are powerful in harnessing high resolution data acquired from UAV to map vegetation patterns. The study was based on low cost red, green, blue (RGB) sensors making the method accessible to a wide range of users. Combining UAV and CNN will provide tremendous opportunities for ecological applications