In this dataset are 63 .csv files, each a cleaned datasheet of city tree inventories from a single US city. Refer to eLife manuscript for complete information on data acquisition, data cleaning, and preliminary analyses.
Refer to the file Column_Headers_Dryad for information about each column.
METHODS:
Data Acquisition Summary:
We limited our search to the 150 largest cities in the USA (by census population).
To acquire raw data on street tree communities, we used a search protocol on both Google and Google Datasets Search (https://datasetsearch.research.google.com/). We first searched the city name plus each of the following: street trees, city trees, tree inventory, urban forest, and urban canopy (all combinations totaled 20 searches per city, 10 each in Google and Google Datasets Search). We then read the first page of google results and the top 20 results from Google Datasets Search. If the same named city in the wrong state appeared in the results, we redid the 20 searches adding the state name. If no data were found, we contacted a relevant state official via email or phone with an inquiry about their street tree inventory. Datasheets were received and transformed to .csv format (if they were not already in that format). We received data on street trees from 64 cities. One city, El Paso, had data only in summary format and was therefore excluded from analyses.
Data Cleaning Summary:
First, we assembled and standardized a large dataset of N=5,132,890 city trees to enable the analysis of urban forests’ ecosystem structure. We acquired street tree inventories from 63 of the largest 150 US cities (those which had conducted inventories) and developed a standardization pipeline in R and Python. Each inventory was produced using different, city-specific methods: for example, some cities only reported a tree’s common name; some reported an address but no coordinates; some reported tree size in feet, some in meters; some scored tree health from 1-5 while others rated trees as “good” or “poor;” etc. Very few cities reported a tree’s native status. Therefore, we inspected metadata for all cities (and communicated with urban officials) to standardize column names, standardize metrics of tree health, and convert all units to metric. We converted all common names to scientific and manually corrected misspellings in all species names (see Data S9, and Materials and Methods, for full details). We manually coded all tree locations as being in a green space or in an urban environment to enable comparisons between location types. Finally, we referenced data from the Biota of North America Project on native status to classify each tree as native or not. The resulting dataset comprised 63 city datasheets each with 28 standardized columns.
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来自63个美国城市的500万棵城市树的数据集 数据说明: 可持续发展的城市依赖于城市森林。城市树木一一城市森林的支柱--改善我们的健康,清洁空气,储存二氧化碳,降低当地温度。相对较少了解城市森林生态系统,特别是它们的空间组成,出生状态,生物多样性和树木健康。在这里,我们收集并标准化了来自美国63个大城市的N=5,660,237棵树的新数据集。数据来源于在城市和/或社区-级进行的树木清查。每个数据表包括树木位置、物种、出生状况(树种是自然产生的还是引进的)、健康状况、大小、是在公园还是在市区等详细信息(每个数据表包含28个标准列》。该数据集可以与鸟类、昆虫或植物生物多样性的公民科学数据集、社会和人口数据或物理环境数据相结合进行分析。城市森林为有意设计生物多样性、异质性丰富的生态系统提供了一个难得的机会。
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archive.zip (65个子文件)
Boston_Final_2022-06-18.csv 229KB
Column_Headers_Dryad.csv 2KB
HuntingtonBeach_Final_2022-06-18.csv 11.52MB
AuroraCO_Final_2022-06-18.csv 14.08MB
Austin_Final_2022-06-18.csv 1.55MB
Ontario_Final_2022-06-18.csv 11.36MB
Fresno_Final_2022-06-18.csv 986KB
Arlington_Final_2022-06-18.csv 2.39MB
Louisville_Final_2022-06-18.csv 7.47MB
Knoxville_Final_2022-06-18.csv 2.37MB
Stockton_Final_2022-06-18.csv 19.24MB
Nashville_Final_2022-06-18.csv 363KB
Buffalo_Final_2022-06-18.csv 23.46MB
Seattle_Final_2022-06-18.csv 37.09MB
ColoradoSprings_Final_2022-06-18.csv 4.27MB
Denver_Final_2022-06-18.csv 67.09MB
OverlandPark_Final_2022-06-18.csv 10.09MB
Baltimore_Final_2022-06-18.csv 28.41MB
RanchoCucamonga_Final_2022-06-18.csv 15.25MB
Milwaukee_Final_2022-06-18.csv 6.69MB
Honolulu_Final_2022-06-18.csv 3.68MB
Minneapolis_Final_2022-06-18.csv 45.79MB
Richmond_Final_2022-06-18.csv 563KB
StLouis_Final_2022-06-18.csv 15.39MB
Jerseycity_Final_2022-06-18.csv 114KB
Madison_Final_2022-06-18.csv 18.96MB
Houston_Final_2022-06-18.csv 37.58MB
Albuquerque_Final_2022-06-18.csv 649KB
LosAngeles_Final_2022-06-18.csv 143.22MB
Miami_Final_2022-06-18.csv 121KB
Rochester_Final_2022-06-18.csv 12.08MB
Providence_Final_2022-06-18.csv 4.42MB
Columbus_Final_2022-06-18.csv 34.6MB
Plano_Final_2022-06-18.csv 4.87MB
GardenGrove_Final_2022-06-18.csv 3.99MB
Irvine_Final_2022-06-18.csv 10.28MB
GrandRapids_Final_2022-06-18.csv 13.53MB
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Oakland_Final_2022-06-18.csv 6.04MB
LasVegas_Final_2022-06-18.csv 9.68MB
NewYork_Final_2022-06-18.csv 159.89MB
Durham_Final_2022-06-18.csv 3.93MB
Atlanta_Final_2022-06-18.csv 7.34MB
Tampa_Final_2022-06-18.csv 6.16MB
README_Dryad.txt 3KB
Sacramento_Final_2022-06-18.csv 17.41MB
Pittsburgh_Final_2022-06-18.csv 10.27MB
DesMoines_Final_2022-06-18.csv 3.65MB
Phoenix_Final_2022-06-18.csv 38KB
Dallas_Final_2022-06-18.csv 991KB
CapeCoral_Final_2022-06-18.csv 7.61MB
NewOrleans_Final_2022-06-18.csv 16.78MB
SiouxFalls_Final_2022-06-18.csv 16.18MB
SanJose_Final_2022-06-18.csv 78.28MB
Greensboro_Final_2022-06-18.csv 1.43MB
Indianapolis_Final_2022-06-18.csv 15.12MB
Portland_Final_2022-06-18.csv 47.35MB
Detroit_Final_2022-06-18.csv 4.36MB
SanFrancisco_Final_2022-06-18.csv 39.42MB
Orlando_Final_2022-06-18.csv 10.78MB
SanDiego_Final_2022-06-18.csv 35.87MB
WashingtonDC_Final_2022-06-18.csv 48.25MB
Worcester_Final_2022-06-18.csv 2.59MB
SantaRosa_Final_2022-06-18.csv 701KB
Anaheim_Final_2022-06-18.csv 15.04MB
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