NYC Green Roof Footprints

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NYC Green Roof Footprints

Complete Data and Trends in 2016

By [source]

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About this dataset

This dataset provides a comprehensive look into the green roof footprints in New York City and is made up of multiple sources that have been meticulously collected and verified for accuracy from aerial imagery and Google Earth Engine. Not only does it provide building IDs, construction year, roof height, building type, ground elevation, owner type as well as centroids and area of green roofs vs total building areas; but you also get access to the tax lot number associated with Digital Tax Maps and PLUTO/MapPLUTO datasets.

The data available here is crucial for researchers or businesses such as urban planners specializing in sustainable-tech to get insight into our urban environmental sustainability efforts. This initiative has fine-tuned our vision of developing a landscape that boasts high energy efficiency values while accenting environmentally friendly alternatives be it on an individual level or industrial scale.

Aside from helping map out achievable paths towards an eco-friendly future this invaluable dataset allows one to assess trends in NYC, create research projects based off real data models all while ensuring roof utilization to its fullest potential when it comes down to new green infrastructure opportunities for New York City

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How to use the dataset

This dataset provides comprehensive information on green roof footprints in NYC in 2016, comprised of multiple sources. This data can be used by researchers and businesses such as urban planners, sustainability experts and green infrastructure initiatives in order to create research projects, assess urban rooftops for green infrastructure opportunities and analyze trends in the city.

The data includes columns such as bin, bbl (Borough Block and Lot Number), gr_area (Area of green roof), bldg_area (Total area of building) , prop_gr (Proportion of green roof to total building area), cnstrct_yr (Year of building construction), heightroof(Height of roof ), feat_code(Feature code ),groundelev (Ground elevation ),qa(Quality assurance), notes(Notes ),classified(Classification of roof type), digitized(Digitized ) , newlyadded(Newly added) , original source , address borough, ownertypezonedist1, spdist1.

Here are a few tips for how to get started with this dataset:

• Start by exploring the different fields available in the dataset – examine them closely and consider what questions you might want to ask or which trend/patterns you’d like to uncover. Think about both short-term as well as long-term goals related to your analysis – this will help you plan accordingly when importing/cleaning/analyzing the data.

• Begin importing the data into your preferred software program - it may be beneficial store the entire dataset within its own folder so that it is more easily accessible over time. It may also be helpful transfer the different fields from one format (.csv)to another (.xlsx).
  If a particular field is not useful or necessary for analysis further along . 3is can help simplify tools used later on for forecasting or prediction modeling techniques so that only relevant information is included).

3Note any missing values across different fields during importation process - these are usually indicated either a blank space or an Unknown category -and decide how best replace them: dropping unnecessary observations all together, filling with most common values associated with that particular rows act correctly or replacing each observation with mean value observed among other entries depending upon situation 4as necessary filtering any outliers contained within specific entries These could skew results significantly if left unchecked prior beginning actual analysis segment which should generally avoided before starting modeling process through machine learning algorithms like

Research Ideas

• Utilize the green roof area and total building area features to create interactive visualizations that illustrate clearn roofing alternatives (i.e., implementing green roofs) for particular areas in NYC, with the goal of inspiring sustainability efforts and promoting environmental awareness.
• Utilize the ground elevation and borough information to study flood mitigation options such as rainwater harvesting systems or green infrastructure in vulnerable communities throughout NYC.
• Use the construction year, owner type, tax lot number and zoning district features to research correlations between development trends, land use/zoning regulations, housing prices/rental rates, ownership patterns and greenhouse gas emission levels among others; these analyses could be used to inform better land use policies and guide future urban planning efforts for NYC

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: GreenRoofData2016_20180917.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .