Introduction

For my project, I’m looking at ecological data, specifically about a variety of different tree species. The data is collected from an undergraduate ecology course about a variety of tree species around campus. This data contributes to an overarching phenological problem regarding carbon emissions and the rate at which leaves fall off the tree. I’m building some graphs that show some interesting aspects of trees and their leaves. Since the National Phenology Network uses data provided from Citizen Scientists, I’m going to look at the implications of using their data for analysis while also doing some analysis regarding the different species of trees around campus.

Materials and methods

Collecting the data:

Students are enrolled in undergrad Environmental Science class.
Students were assigned up to four trees around campus.
Each tree was identified and assigned a tag by the professor.
Students had to be make 4 observations a week (one for each tree).
Students then input data into an app which is then recorded in the database.

Running the analysis:

Install/Download the appropriate libraries
Retrieve the data that is stored online using UB’s network ID.
Pull the two data sources together (student’s data and tree’s data) and perform some exploratory analysis.
Create some exploratory tables that can be used for analysis.
Generate some graphs that show some interesting finds about the data.

Load all packages (you may need to install some packages):

library(tidyverse)
library(devtools)
#devtools::install_github("usa-npn/rnpn")
library(rnpn)
library(dplyr)
library(lubridate)
library(leaflet)
library(readxl)
library(sf)
library(spData)
library(ggmap)
library(kableExtra)
library(knitr)
library(data.table)
library(broom)
library(widgetframe)
library(DT)
knitr::opts_chunk$set(cache=TRUE)  ##cache the results for quick compiling

When you run the code to install the libraries, make sure to uncomment the install the ‘rnpn’ library from github so in the next chunk, you will be able to retrieve the data.

Download and clean all required data

#npn_groups() %>% filter(grepl("Buffalo",network_name)) <- Identifies network ID

d=npn_download_status_data("Testing",
                           years=c('2019'),
                           additional_fields=list("Plant_Nickname",
                                                  "ObservedBy_Person_ID"),
                           network_ids=c(891))

d_filtered <- d %>%
  rename("a"="intensity_value") %>% 
  mutate(
    date = as.Date(observation_date),
    month = month(date),
    tag = as.numeric(substr(plant_nickname,1,3)),
    intensity=case_when(
      a == "Less than 5%" ~ 2.5,
      a == "Less than 25%" ~ 20,
      a == "5-24%" ~ 14.5,
      a == "25-49%" ~ 37,
      a == "50-74%" ~ 62,
      a == "75-94%" ~ 84.5,
      a == "95% or more"  ~ 97.5,
      a == "Less than 3"  ~ 2,  
      a == "3 to 10" ~ 6.5,
      a == "11 to 100" ~ 50,
      a == "101 to 1,000" ~ 500,
      a == "1,001 to 10,000" ~ 5000,
      a == "Little" ~ 5,
      a == "Some" ~ 3,  
      TRUE ~ as.numeric(NA)
    ))%>%
  filter(phenophase_description%in%c("Leaves","Colored leaves"))

d_filtered <- d_filtered %>%
  mutate(leaf_drop = case_when(
    intensity < 50 ~ "Dropped",
    intensity >= 50 ~ "Not Dropped"
  ))

trees <- read_csv("trees.csv")

trees <- trees %>% 
  mutate(color = case_when (
    common_name == "Red Oak" ~ "red",
    common_name == "Black Oak" ~ "black",
    common_name == "Silver Maple" ~ "darkblue",
    common_name == "Red Maple" ~ "orange",
    common_name == "Black Locust" ~ "purple",
    common_name == "Sugar Maple" ~ "darkpurple",
    common_name == "Staghorn Sumac" ~ "blue",
    common_name == "Eastern Cottonwood" ~ "beige",
    common_name == "Apple" ~ "yellow",
    common_name == "American Basswood" ~ "pink",
    common_name == "River Birch" ~ "lightred",
    common_name == "White Birch" ~ "cadetblue",
    common_name == "Ginkgo" ~ "lightgray"
  ))

To download the data that is being stored on another server, we must remember to install the ‘rnpn’ package from github listed above and put in the correct ‘network id’

Exploratory Data Analysis

table <- prop.table(table(d$common_name)) * 100
table %>%
  kable(col.names = c("Common Name","Percentage of Observations"), digits = 2) %>% kable_styling(full_width = FALSE, fixed_thead = TRUE) %>%
  scroll_box(width = "500px", height = "200px", fixed_thead = TRUE)

Common Name	Percentage of Observations
American basswood	8.54
apple	7.44
black locust	8.90
black oak	5.21
Canada goose	0.57
eastern cottonwood	3.07
maidenhair tree	10.91
northern red oak	8.27
paper birch	6.52
red maple	8.28
river birch	9.66
silver maple	3.57
staghorn sumac	7.55
sugar maple	11.50

The table above shows the proportion of observations for each tree. This helps show what trees contributed to most of the observations. We can easily see that the tree with the most observations is the ‘Sugar Maple’ while the tree that was the least common was the ‘Canada Goose’.

d_filtered %>%
  group_by(common_name, month) %>%
  summarize(var_intensity = var(intensity,na.rm = TRUE),
            mean_intensity = mean(intensity, na.rm = TRUE)) %>%
  filter(var_intensity != 0) %>%
  kable(digits = 2, col.names = c("Name","Month","Intensity Variance","Intensity Average")) %>% kable_styling() %>% scroll_box(width = "500px", height = "200px")

Name	Month	Intensity Variance	Intensity Average
American basswood	9	1354.79	51.69
American basswood	10	959.07	53.56
American basswood	11	1574.28	46.22
apple	9	1153.33	53.44
apple	10	891.99	42.52
apple	11	1189.74	39.28
black locust	9	1110.18	62.58
black locust	10	1051.78	50.94
black locust	11	1273.13	30.57
black oak	9	1698.06	51.66
black oak	10	1117.18	48.63
black oak	11	1014.96	63.86
eastern cottonwood	9	1192.67	48.09
eastern cottonwood	10	603.41	40.77
eastern cottonwood	11	1633.33	49.50
maidenhair tree	9	1483.71	64.64
maidenhair tree	10	1041.16	56.22
maidenhair tree	11	1426.18	49.42
northern red oak	9	1725.51	55.33
northern red oak	10	1043.45	52.81
northern red oak	11	1100.92	59.44
paper birch	9	1382.37	56.55
paper birch	10	862.80	51.46
paper birch	11	1453.00	51.68
red maple	9	1223.27	54.82
red maple	10	834.25	60.34
red maple	11	1310.82	38.51
river birch	1	126.56	31.38
river birch	9	1240.32	63.19
river birch	10	1007.42	54.76
river birch	11	1156.11	52.14
silver maple	9	1222.62	52.24
silver maple	10	849.96	50.42
silver maple	11	1209.10	38.83
staghorn sumac	9	1047.50	45.44
staghorn sumac	10	767.07	51.07
staghorn sumac	11	1545.31	48.01
sugar maple	9	1562.26	56.89
sugar maple	10	997.16	56.74
sugar maple	11	1272.38	52.50

The table above shows the variance in observations for the same species of trees and how they differ from month to month. This shows that even though students are looking at the same tree, the observer may see the tree differently.

d_filtered %>%
  group_by(tag,observedby_person_id, month) %>%
  summarize(var_intensity = var(intensity,na.rm = TRUE)) %>%
  filter(var_intensity != 0) %>%
  kable(digits = 2,
        col.names = c("Tag ID","Person ID","Month","Intensity Variance")) %>%
  kable_styling() %>%
  scroll_box(width = "500px", height = "200px")

Tag ID	Person ID	Month	Intensity Variance
101	49450	9	4512.50
101	49464	11	4512.50
101	49468	9	2578.57
101	49468	10	1542.14
101	49468	11	302.70
101	49545	9	1941.07
101	49545	10	874.03
101	49545	11	2503.50
101	49549	9	2707.50
101	49549	10	1649.69
101	49549	11	308.50
101	49606	9	1949.33
101	49606	10	505.83
101	49608	9	1594.40
101	49613	11	4512.50
101	49616	9	1128.12
101	49627	10	3362.00
101	49657	9	1749.45
101	49657	10	606.19
101	49657	11	1800.40
101	49661	10	1830.12
101	49688	10	1798.57
101	49688	11	238.54
101	49689	9	4512.50
101	49703	9	84.50
101	49971	11	4512.50
101	50044	10	3008.33
102	49450	9	4512.50
102	49468	9	2578.57
102	49468	10	1601.58
102	49468	11	302.70
102	49545	9	1727.99
102	49545	10	1115.32
102	49545	11	2503.50
102	49549	9	676.88
102	49549	10	873.25
102	49549	11	1800.40
102	49606	9	1180.08
102	49606	10	869.29
102	49657	9	1749.45
102	49657	10	746.57
102	49657	11	2213.40
102	49683	9	2450.00
102	49688	10	1502.48
102	49688	11	792.27
102	50044	9	2653.00
102	50044	10	1949.33
103	49450	9	4512.50
103	49468	9	2578.57
103	49468	10	1625.62
103	49468	11	302.70
103	49545	9	2494.30
103	49545	10	989.96
103	49545	11	2503.50
103	49549	9	1470.00
103	49549	10	1172.39
103	49606	9	208.33
103	49606	10	460.52
103	49657	9	2264.20
103	49657	10	979.40
103	49657	11	1546.23
103	49688	9	2017.20
103	49688	10	1215.77
103	49688	11	555.57
103	50044	9	2638.92
103	50044	10	1949.33
104	49549	11	630.12
104	49579	10	1584.67
104	49579	11	1232.20
104	49601	10	389.38
104	49619	9	1662.00
104	49619	10	1400.00
104	49652	9	2296.33
104	49683	9	2450.00
104	49683	10	641.67
104	49683	11	126.56
105	49450	9	4512.50
105	49468	9	1968.29
105	49468	10	1625.62
105	49468	11	302.70
105	49545	9	2494.30
105	49545	10	1004.40
105	49545	11	2433.71
105	49549	9	1470.00
105	49549	10	1306.67
105	49549	11	706.42
105	49606	9	2241.33
105	49606	10	1160.03
105	49657	9	1749.45
105	49657	10	875.69
105	49657	11	2213.40
105	49688	9	1844.40
105	49688	10	1514.54
105	49688	11	558.57
105	50044	9	2653.00
105	50044	10	2241.33
106	49579	10	280.40
106	49579	11	1060.03
106	49601	9	48.00
106	49601	10	564.38
106	49619	9	2264.20
106	49619	10	1670.86
106	49652	9	3008.33
106	49683	9	3444.50
106	49683	10	648.61
106	49683	11	376.56
107	49546	9	1722.25
107	49546	10	961.90
107	49546	11	1642.21
107	49614	9	1681.00
107	49614	10	932.92
107	49614	11	108.48
107	49679	9	2494.30
107	49679	10	1232.64
107	49692	9	2638.92
107	49692	10	1500.06
107	49710	9	1770.12
107	49710	10	1470.00
107	49710	11	752.08
107	49873	9	168.75
108	49579	10	1331.73
108	49579	11	1182.06
108	49601	9	48.00
108	49601	10	445.62
108	49619	9	2092.67
108	49619	10	1400.00
108	49652	9	2296.33
108	49683	9	2450.00
108	49683	10	1101.11
108	49683	11	512.50
109	49546	9	1722.25
109	49546	10	961.90
109	49546	11	1642.21
109	49614	9	2017.20
109	49614	10	932.92
109	49614	11	204.91
109	49679	9	2707.50
109	49679	10	1007.14
109	49692	9	1633.33
109	49692	10	3362.00
109	49710	9	3362.00
109	49710	10	376.56
109	49710	11	302.70
109	49873	9	168.75
110	49024	9	3362.00
110	49579	11	1232.20
110	49601	9	48.00
110	49601	10	389.38
110	49619	9	2092.67
110	49619	10	1670.86
110	49652	9	3008.33
110	49683	9	4512.50
110	49683	10	999.24
110	49683	11	156.25
110	49692	10	1128.12
111	49546	9	1722.25
111	49546	10	961.90
111	49546	11	856.32
111	49614	9	2017.20
111	49614	10	1080.57
111	49614	11	204.91
111	49653	9	439.29
111	49679	9	2196.70
111	49679	10	933.48
111	49692	9	1089.06
111	49692	10	1128.12
111	49710	9	2450.00
111	49710	11	313.94
112	49483	9	3362.00
112	49546	9	1722.25
112	49546	10	961.90
112	49546	11	856.32
112	49614	9	1062.07
112	49614	10	555.25
112	49614	11	108.48
112	49629	10	630.12
112	49679	9	2196.70
112	49679	10	695.42
112	49692	9	2653.00
112	49692	10	2450.00
112	49710	9	3362.00
112	49710	10	376.56
112	49710	11	1166.90
112	49873	9	208.33
113	49452	9	168.75
113	49452	10	541.71
113	49491	9	2450.00
113	49558	9	1224.32
113	49558	10	303.12
113	49558	11	1335.04
113	49629	9	1884.70
113	49629	10	1083.48
113	49629	11	2664.33
113	49653	9	439.29
113	49653	10	894.57
113	49653	11	2503.50
113	49695	10	894.75
113	49695	11	72.00
114	49452	9	1662.00
114	49452	10	999.24
114	49452	11	72.00
114	49491	9	3444.50
114	49558	9	1513.39
114	49558	10	303.12
114	49558	11	1015.17
114	49604	9	168.75
114	49629	9	2707.50
114	49629	10	486.64
114	49629	11	2664.33
114	49653	9	470.42
114	49653	10	1037.86
114	49653	11	2664.33
114	49695	9	1633.33
114	49695	10	851.46
114	49695	11	48.00
115	49452	9	384.38
115	49452	10	1244.50
115	49491	9	2450.00
115	49558	9	1385.92
115	49558	10	274.00
115	49558	11	907.54
115	49604	9	253.12
115	49629	9	2494.30
115	49629	10	1030.75
115	49653	9	517.29
115	49653	10	841.13
115	49653	11	3008.33
115	49695	9	920.83
115	49695	10	907.97
116	49438	9	1696.21
116	49438	10	613.96
116	49470	9	1882.57
116	49566	9	975.00
116	49566	10	348.61
116	49566	11	3008.33
116	49577	9	4512.50
116	49609	9	253.12
116	49609	11	312.50
116	49627	9	935.62
116	49627	10	478.00
116	49699	9	1331.73
116	49699	10	389.17
116	49835	9	1662.00
116	49835	10	901.67
116	49835	11	1633.33
117	49438	10	816.88
117	49470	9	1662.00
117	49566	9	1662.00
117	49566	10	626.74
117	49566	11	752.08
117	49577	9	595.12
117	49609	11	253.12
117	49627	9	885.06
117	49627	10	1206.94
117	49627	11	312.50
117	49699	9	892.58
117	49699	10	944.07
117	49835	9	1413.38
117	49835	10	476.79
117	49835	11	1234.90
118	49555	9	2017.20
118	49555	10	909.79
118	49555	11	4512.50
118	49632	9	3008.33
118	49632	10	742.55
118	49632	11	2320.92
118	49966	10	672.29
119	49555	9	2256.25
119	49555	10	1304.64
119	49555	11	4512.50
119	49632	9	3008.33
119	49632	10	1238.36
119	49632	11	2320.92
119	49966	10	672.29
119	49966	11	24.00
120	49024	9	4512.50
120	49446	9	1989.98
120	49446	10	1690.96
120	49446	11	3444.50
120	49539	9	714.29
120	49539	10	935.53
120	49560	9	1504.17
120	49560	10	1399.20
120	49560	11	38.40
120	49611	9	985.88
120	49611	10	312.50
120	49611	11	978.08
120	49620	9	476.88
120	49620	10	120.39
120	49620	11	1800.40
120	49995	9	2450.00
121	49446	9	422.48
121	49446	10	839.60
121	49446	11	4512.50
121	49539	9	630.95
121	49539	10	783.95
121	49560	9	2066.70
121	49560	10	546.10
121	49566	9	3444.50
121	49611	9	769.94
121	49611	10	312.50
121	49611	11	978.08
121	49620	9	476.88
121	49620	10	120.39
121	49620	11	1800.40
121	49995	9	253.12
122	49438	9	126.56
122	49438	10	156.25
122	49566	9	2295.07
122	49566	10	1069.17
122	49566	11	36.00
122	49627	9	1089.06
122	49627	10	2320.92
122	49699	9	1633.33
122	49699	10	1225.00
122	49835	9	1982.20
122	49835	10	901.67
122	49835	11	1234.90
123	49438	9	126.56
123	49438	10	522.40
123	49566	9	56.33
123	49566	10	1069.17
123	49566	11	36.00
123	49609	10	312.50
123	49627	9	1225.00
123	49627	10	2241.33
123	49699	9	752.08
123	49699	10	1225.00
123	49835	9	1470.00
123	49835	10	778.12
123	49835	11	1949.33
124	49555	9	1681.00
124	49555	10	1223.00
124	49632	10	168.75
124	49693	9	4512.50
124	49966	10	614.24
125	49555	9	1225.00
125	49555	10	1336.10
125	49632	10	168.75
125	49693	9	4512.50
125	49966	9	42.25
125	49966	10	614.24
126	49446	9	126.56
126	49446	10	1400.75
126	49539	10	1665.78
126	49560	9	56.33
126	49560	10	1774.44
126	49560	11	253.12
126	49611	9	168.75
126	49611	11	802.06
126	49620	9	2394.30
126	49620	10	860.60
126	49620	11	376.56
126	49995	9	2450.00
127	49446	9	1127.67
127	49446	10	1812.46
127	49446	11	4512.50
127	49539	9	1485.20
127	49539	10	1683.58
127	49560	10	1365.50
127	49611	9	168.75
127	49611	11	72.00
127	49620	9	2394.30
127	49620	10	701.79
127	49620	11	1800.40
127	49995	9	3362.00
128	49447	9	445.62
128	49447	10	1194.67
128	49554	9	826.23
128	49554	10	816.07
128	49840	10	2304.25
128	50194	9	512.50
128	50194	10	860.60
128	50194	11	2450.00
128	50686	10	1504.98
128	50686	11	2664.33
129	49447	9	1233.67
129	49447	10	861.31
129	49554	9	826.23
129	49554	10	857.48
129	49840	10	1277.08
129	50194	9	688.50
129	50194	10	942.55
129	50686	10	1941.07
129	50686	11	2664.33
130	49447	9	1770.12
130	49452	9	1131.00
130	49452	10	434.79
130	49452	11	72.00
130	49491	9	3444.50
130	49558	9	1312.54
130	49558	10	303.12
130	49558	11	661.54
130	49604	9	253.12
130	49629	9	1884.70
130	49629	10	789.17
130	49629	11	1978.92
130	49653	9	814.88
130	49653	10	833.48
130	49653	11	4512.50
130	49695	9	1633.33
130	49695	10	541.74
130	49695	11	104.17
131	49447	9	655.39
131	49447	10	396.43
131	49447	11	1277.08
131	49554	9	2289.90
131	49554	10	1087.24
131	49840	9	1633.33
131	49840	10	2450.00
131	49840	11	1546.23
131	50194	9	752.08
131	50194	10	472.77
131	50194	11	1553.00
131	50686	10	1777.29
131	50686	11	2320.92
132	49447	9	1844.40
132	49447	10	314.17
132	49447	11	1128.12
132	49471	9	1844.40
132	49471	10	226.04
132	49471	11	1800.40
132	49554	9	2018.78
132	49554	10	813.93
132	49557	9	2394.30
132	49574	9	2258.57
132	49597	9	1513.39
132	49597	10	369.11
132	49628	9	1900.92
132	49628	10	1411.04
132	49628	11	3444.50
132	49840	9	1978.92
132	49840	10	1128.12
132	49840	11	2664.33
132	50194	9	1234.90
132	50194	10	390.07
132	50194	11	1553.00
132	50686	10	1834.98
132	50686	11	2664.33
133	49476	9	899.74
133	49554	10	253.12
133	49559	9	1770.12
133	49583	9	2494.30
133	49583	10	1112.20
133	49621	9	2707.50
133	49621	10	761.00
133	49621	11	2707.50
133	49714	9	1234.75
133	49714	10	725.39
133	49714	11	2664.33
134	49476	9	1722.25
134	49484	9	4512.50
134	49621	9	2707.50
134	49621	10	1922.80
134	49621	11	258.07
134	49714	9	1414.20
134	49714	10	1399.20
134	49714	11	1059.32
134	50218	10	1633.33
135	49444	10	1803.12
135	49444	11	872.17
135	49484	9	2461.36
135	49484	10	1609.44
135	49484	11	1378.64
135	49564	10	3362.00
135	49626	9	2256.25
135	49626	10	2099.50
135	49626	11	208.33
135	49770	9	2017.20
135	49770	10	1231.34
135	49770	11	156.25
135	50218	10	72.00
136	49444	9	2256.25
136	49444	10	1690.25
136	49444	11	1546.23
136	49484	9	2394.68
136	49484	10	1609.44
136	49484	11	1666.87
136	49564	10	4512.50
136	49626	9	2256.25
136	49626	10	1934.71
136	49626	11	208.33
136	49684	9	3362.00
136	49770	9	2017.20
136	49770	10	1335.88
136	49770	11	208.33
137	49451	9	3362.00
137	49463	9	587.67
137	49463	10	388.61
137	49463	11	1560.50
137	49563	9	2295.07
137	49563	10	564.06
137	49563	11	1664.37
137	49594	9	676.88
137	49594	10	304.24
137	49594	11	1800.40
137	49617	9	2091.87
137	49617	10	1467.77
137	49617	11	3444.50
137	49702	9	1650.44
137	49702	10	381.08
137	49702	11	2066.70
137	49971	10	1304.64
137	49971	11	3008.33
138	49451	9	3362.00
138	49463	9	769.94
138	49463	10	358.43
138	49463	11	2102.25
138	49563	9	1662.00
138	49563	10	1266.42
138	49563	11	2061.44
138	49583	9	2488.67
138	49583	10	1112.20
138	49594	9	451.67
138	49594	10	381.08
138	49594	11	2664.33
138	49617	9	2075.37
138	49617	10	1387.39
138	49617	11	4512.50
138	49702	9	1965.78
138	49702	10	971.71
138	49702	11	2707.50
138	49971	10	1320.18
138	49971	11	2213.40
139	49444	9	2075.37
139	49444	10	612.61
139	49444	11	1800.40
139	49484	9	1007.14
139	49484	10	314.17
139	49484	11	2099.50
139	49564	9	1400.75
139	49564	10	1128.12
139	49626	9	298.27
139	49626	10	1073.71
139	49770	9	451.67
139	49770	10	404.17
139	50218	10	253.12
140	49442	9	1128.12
140	49444	9	1882.57
140	49444	10	649.10
140	49444	11	1830.12
140	49484	9	1551.12
140	49484	10	741.67
140	49484	11	2018.78
140	49564	9	1400.75
140	49564	10	1128.12
140	49626	9	298.27
140	49626	10	1145.39
140	49770	9	314.17
140	49770	10	348.61
141	49442	9	1128.12
141	49472	9	1793.07
141	49472	10	889.04
141	49472	11	1800.40
141	49658	9	2450.00
141	49684	9	3362.00
142	49472	9	1643.36
142	49472	10	889.04
142	49472	11	1220.08
142	49628	11	48.00
142	49658	9	2450.00
143	49455	9	2450.00
143	49471	10	547.50
143	49471	11	1840.24
143	49557	9	1982.20
143	49574	9	1749.45
143	49597	9	314.17
143	49597	10	1349.03
143	49628	9	56.33
143	49628	10	1436.07
143	49628	11	1681.00
143	49701	9	253.12
143	50009	9	2450.00
144	49455	9	2450.00
144	49471	10	547.50
144	49471	11	1531.73
144	49557	9	1982.20
144	49574	9	2017.20
144	49597	10	1007.14
144	49628	9	2264.20
144	49628	10	1436.07
144	49628	11	3362.00
144	50009	9	1128.12
145	49454	9	3444.50
145	49454	10	1128.12
145	49479	9	2241.33
145	49479	10	48.00
145	49556	9	676.88
145	49556	10	276.58
145	49556	11	3444.50
145	49612	9	376.56
145	49612	10	226.04
145	49612	11	1128.12
145	49623	9	368.54
145	49659	9	676.88
145	49659	10	178.57
145	49659	11	38.40
145	49661	9	314.17
145	49661	10	789.14
145	49997	9	56.33
145	49997	10	401.04
146	49454	9	3444.50
146	49454	10	1128.12
146	49479	9	2241.33
146	49479	10	1553.00
146	49556	9	1062.08
146	49556	10	411.02
146	49556	11	3444.50
146	49612	9	522.40
146	49612	10	104.17
146	49612	11	2450.00
146	49623	9	135.00
146	49623	10	253.12
146	49659	9	676.88
146	49659	10	178.57
146	49659	11	38.40
146	49661	9	151.88
146	49661	10	788.78
147	49454	9	595.12
147	49479	10	990.08
147	49556	9	2707.50
147	49556	10	1524.46
147	49556	11	1830.12
147	49612	9	1277.08
147	49612	10	84.38
147	49612	11	2450.00
147	49623	9	1234.75
147	49659	9	1239.38
147	49659	10	322.77
147	49659	11	38.40
147	49661	9	1653.44
147	49661	10	936.89
147	49997	10	187.50
148	49454	9	1770.12
148	49479	10	1275.20
148	49556	9	2707.50
148	49556	10	1524.46
148	49556	11	1830.12
148	49612	9	1842.58
148	49612	10	226.04
148	49612	11	2450.00
148	49623	9	1234.75
148	49659	9	1306.67
148	49659	10	396.43
148	49659	11	38.40
148	49661	9	1266.18
148	49661	10	1113.69
148	49997	10	187.50
149	49471	9	261.68
149	49471	10	151.88
149	49471	11	1666.87
149	49574	9	2258.57
149	49597	9	920.83
149	49597	10	420.90
149	49628	9	2017.20
149	49628	10	1306.67
149	49628	11	36.00
150	49476	9	1239.38
150	49559	9	1770.12
150	49583	9	654.58
150	49583	10	680.05
150	49621	9	1234.75
150	49621	10	1179.27
150	49621	11	1430.04
150	49714	10	229.88
150	49714	11	158.78
151	49476	9	879.17
151	49559	9	1770.12
151	49583	9	1062.07
151	49583	10	680.05
151	49621	9	885.06
151	49621	10	1026.89
151	49621	11	1650.44
151	49714	9	168.75
151	49714	10	229.88
151	49714	11	1200.86
152	49451	9	1770.12
152	49463	9	684.30
152	49463	10	396.43
152	49463	11	4512.50
152	49563	9	1234.75
152	49563	10	564.58
152	49563	11	1666.87
152	49594	9	43.20
152	49594	10	343.06
152	49594	11	2320.92
152	49617	9	1234.75
152	49617	10	1141.57
152	49624	9	3362.00
152	49702	9	1882.57
152	49702	10	504.79
152	49702	11	2707.50
152	49971	10	948.28
152	49971	11	4512.50
153	49451	9	3362.00
153	49463	9	564.06
153	49463	10	226.04
153	49563	9	1681.00
153	49563	10	752.08
153	49563	11	1850.07
153	49594	9	43.20
153	49594	10	343.06
153	49594	11	2638.92
153	49617	9	885.06
153	49617	10	1577.78
153	49702	9	1455.07
153	49702	10	1023.49
153	49702	11	2707.50
153	49971	10	948.28
153	49971	11	4512.50
154	49442	9	2450.00
154	49472	9	56.33
154	49472	10	1218.45
154	49472	11	168.75
154	49701	11	28.80
154	50025	9	84.50
155	49442	9	3362.00
155	49472	9	1504.17
155	49472	10	1272.17
155	49472	11	156.25
155	49701	11	36.00
156	49474	9	654.58
156	49542	9	1331.73
156	49542	10	601.67
156	49542	11	752.08
156	49553	10	945.04
156	49553	11	1505.90
156	49618	9	280.40
156	49618	10	140.62
156	49618	11	208.33
157	49443	9	151.88
157	49474	9	43.20
157	49542	9	1258.37
157	49542	10	102.23
157	49542	11	1234.90
157	49618	9	42.25
157	49618	11	512.50
158	49610	10	1180.08
158	49615	9	168.75
158	49615	10	493.17
158	49615	11	1234.90
158	49703	9	42.25
158	49863	10	2450.00
158	49863	11	1835.27
159	49610	10	1180.08
159	49615	9	168.75
159	49615	10	493.17
159	49615	11	1234.90
159	49703	9	42.25
159	49863	10	2450.00
159	49863	11	1835.27
160	49573	9	1830.12
160	49622	9	1470.00
160	49622	10	1250.43
160	49622	11	3008.33
160	49681	9	3362.00
160	49700	10	208.33
160	49700	11	2450.00
160	50027	9	1633.33
160	50027	10	543.54
161	49477	9	72.00
161	49573	9	312.50
161	49622	9	1729.45
161	49622	10	1212.76
161	49622	11	4512.50
161	49681	9	595.12
161	49700	10	208.33
161	49700	11	3444.50
161	50027	9	1633.33
161	50027	10	451.67
162	49441	9	48.00
162	49477	9	396.75
162	49477	10	203.79
162	49477	11	253.12
162	49603	9	655.39
162	49603	10	451.67
162	49631	9	483.82
162	49631	10	226.04
162	49631	11	1867.57
162	49651	9	3362.00
162	49691	9	1252.67
162	49967	9	1949.33
163	49477	9	1180.08
163	49477	10	471.67
163	49477	11	253.12
163	49603	9	724.14
163	49603	10	543.54
163	49631	9	303.12
163	49631	10	373.38
163	49631	11	1867.57
163	49651	9	3362.00
163	49691	9	1239.38
163	49967	9	1949.33
164	49572	9	1750.80
164	49572	10	1607.92
164	49572	11	3444.50
164	49608	9	2241.33
164	49624	9	1510.05
164	49624	10	2450.00
164	49689	9	3008.33
164	49689	10	1633.33
164	49983	9	3444.50
165	49572	9	1662.00
165	49572	10	1921.14
165	49608	9	2241.33
165	49624	9	1451.57
165	49624	10	2450.00
165	49689	9	3008.33
165	49689	10	1633.33
165	49983	9	2450.00
166	49482	9	42.25
166	49482	10	923.75
166	49482	11	401.04
166	49523	9	2707.50
166	49523	10	775.57
166	49523	11	396.75
166	49607	9	1844.40
166	49607	10	1306.67
166	49607	11	2503.50
166	49694	9	1470.00
166	49694	10	1073.67
166	49694	11	1500.06
166	49700	9	688.50
166	49700	10	451.67
167	49482	9	42.25
167	49482	10	923.75
167	49482	11	594.17
167	49523	9	2494.30
167	49523	10	775.57
167	49523	11	396.75
167	49607	9	1844.40
167	49607	10	1306.67
167	49607	11	2503.50
167	49694	9	1470.00
167	49694	10	1073.67
167	49694	11	1681.00
167	49700	9	688.50
167	49700	10	451.67
168	49435	9	1722.25
168	49435	10	1059.14
168	49435	11	1577.78
168	49465	9	2241.33
168	49593	9	868.75
168	49593	10	800.88
168	49593	11	2250.38
168	49654	9	2017.20
168	49654	10	2241.33
168	49685	9	1470.00
168	49973	9	4512.50
169	49435	9	3008.33
169	49435	10	932.92
169	49435	11	1770.70
169	49465	9	2241.33
169	49578	9	1633.33
169	49593	9	376.56
169	49593	10	869.50
169	49593	11	2167.04
169	49654	9	2017.20
169	49654	10	2241.33
169	49685	9	1633.33
169	49973	9	2450.00
170	49542	9	56.33
170	49542	10	144.64
170	49542	11	564.06
170	49553	9	56.33
170	49553	10	1538.49
170	49599	9	253.12
170	49618	9	168.75
170	49618	10	501.67
170	49618	11	1234.90
171	49542	9	56.33
171	49542	10	120.54
171	49542	11	126.56
171	49553	10	1308.73
171	49618	11	42.25
172	49610	10	312.50
172	49615	9	1500.06
172	49615	10	400.99
172	49615	11	1234.90
172	49703	9	280.40
172	49863	10	2450.00
172	49863	11	1835.27
173	49602	10	3362.00
173	49615	9	1500.06
173	49615	10	408.60
173	49615	11	1197.44
173	49703	9	217.56
173	49863	10	2450.00
173	49863	11	1835.27
174	49437	9	3008.33
174	49464	9	1416.90
174	49464	10	133.93
174	49464	11	1798.40
174	49550	10	188.39
174	49550	11	522.40
174	49565	9	1805.00
174	49565	10	901.67
174	49565	11	168.75
174	49602	9	1510.05
174	49602	10	1514.20
174	49602	11	1777.58
174	49616	9	892.58
174	49616	10	941.85
174	49616	11	3444.50
174	49690	10	1584.67
175	49437	9	3008.33
175	49464	9	1022.54
175	49464	10	237.39
175	49464	11	1922.80
175	49550	9	168.75
175	49550	10	104.17
175	49550	11	564.06
175	49565	9	1805.00
175	49565	10	901.67
175	49565	11	168.75
175	49602	9	1510.05
175	49602	10	1375.77
175	49602	11	1800.40
175	49616	9	564.58
175	49616	10	476.79
175	49616	11	3444.50
175	49690	10	1584.67
176	49550	1	253.12
176	49552	10	1023.87
176	49552	11	1219.78
176	49595	9	357.07
176	49595	10	277.72
176	49630	9	1220.08
176	49682	9	2241.33
176	49682	10	2241.33
176	49853	9	312.50
176	49853	10	282.50
177	49552	10	1161.78
177	49552	11	1219.78
177	49595	9	297.56
177	49595	10	277.72
177	49630	9	1842.58
177	49682	9	2241.33
177	49682	10	2638.92
177	49853	9	312.50
177	49853	10	238.05
178	49448	9	2241.33
178	49462	9	1608.70
178	49462	10	1182.06
178	49462	11	226.77
178	49613	9	2017.20
178	49613	10	1679.69
178	49613	11	1999.78
178	49647	9	4512.50
178	49687	9	2092.67
178	49687	10	2653.00
178	49687	11	322.58
179	49448	9	2241.33
179	49462	9	1485.20
179	49462	10	1272.17
179	49462	11	226.77
179	49613	9	2017.20
179	49613	10	1679.69
179	49613	11	1999.78
179	49647	9	4512.50
179	49687	9	56.33
179	49687	10	2653.00
179	49687	11	322.58
179	50014	9	208.33
180	49573	9	253.12
180	49622	9	1510.05
180	49622	10	664.24
180	49622	11	2450.00
180	49681	9	1128.12
180	50027	9	3008.33
180	50027	10	1454.70
181	49573	9	1128.12
181	49622	9	1510.05
181	49622	10	290.96
181	49622	11	3362.00
181	49681	9	595.12
181	50015	9	312.50
181	50027	9	3008.33
181	50027	10	2091.87
182	49477	9	48.00
182	49477	10	34.29
182	49603	9	879.14
182	49603	10	633.74
182	49631	9	317.75
182	49631	10	1004.88
182	49631	11	2061.44
182	49651	9	3362.00
182	49691	9	1416.90
182	49691	11	2664.33
182	49698	9	3444.50
182	49967	9	3362.00
183	49477	9	396.75
183	49477	10	152.04
183	49603	9	911.82
183	49603	10	840.50
183	49631	9	711.82
183	49631	10	384.38
183	49631	11	2061.44
183	49691	9	1793.95
183	49691	10	3444.50
183	49967	9	2653.00
184	49572	9	168.75
184	49572	10	790.54
184	49624	9	1736.57
184	49689	9	3008.33
184	49689	10	1633.33
184	49967	9	2450.00
185	49572	10	595.04
185	49624	9	1770.70
185	49624	10	3362.00
185	49689	9	3008.33
185	49689	10	1633.33
186	49600	9	1750.80
186	49600	10	627.57
186	49655	9	2289.90
186	49655	10	739.17
186	49655	11	968.94
186	49974	11	208.33
187	49600	9	1750.80
187	49600	10	789.29
187	49655	9	2707.50
187	49655	10	739.17
187	49655	11	968.94
187	49974	11	208.33
188	49435	10	721.88
188	49435	11	1236.82
188	49465	9	1681.00
188	49578	9	3008.33
188	49593	9	1633.33
188	49593	10	653.54
188	49593	11	1654.24
188	49654	9	1795.08
188	49654	10	253.12
188	49685	9	2241.33
189	49435	10	753.54
189	49435	11	1134.75
189	49465	9	2241.33
189	49578	9	3008.33
189	49593	9	2320.92
189	49593	10	971.04
189	49593	11	2000.57
189	49654	9	2241.33
189	49685	9	2638.92
190	49482	10	769.33
190	49482	11	806.17
190	49523	9	2707.50
190	49523	10	636.53
190	49523	11	1128.12
190	49607	9	2092.67
190	49607	10	2075.37
190	49694	9	2258.57
190	49694	10	1257.11
190	49694	11	630.12
190	49700	9	1507.06
190	49700	10	824.93
191	49482	10	769.33
191	49482	11	806.17
191	49523	9	2707.50
191	49523	10	775.57
191	49523	11	1128.12
191	49607	9	2092.67
191	49607	10	2320.92
191	49694	9	1863.20
191	49694	10	1387.90
191	49700	9	1507.06
191	49700	10	824.93
192	49600	9	1413.38
192	49600	10	627.57
192	49607	10	3362.00
192	49655	9	2707.50
192	49655	10	641.74
192	49655	11	4512.50
192	49974	9	3362.00
193	49481	9	3008.33
193	49600	9	1685.80
193	49600	10	627.57
193	49655	9	2707.50
193	49655	10	739.17
193	49655	11	2450.00
193	49974	9	3362.00
194	49437	9	3008.33
194	49464	9	1172.39
194	49464	10	317.75
194	49464	11	1389.34
194	49550	10	401.04
194	49550	11	1537.56
194	49565	9	2578.57
194	49565	10	932.92
194	49602	9	1681.00
194	49602	10	1552.32
194	49602	11	4512.50
194	49616	10	1072.62
194	49616	11	1830.12
194	49690	10	1982.20
195	49437	9	3008.33
195	49464	9	1413.38
195	49464	10	596.32
195	49464	11	1989.36
195	49550	10	693.49
195	49550	11	1537.56
195	49565	9	1805.00
195	49565	10	627.57
195	49602	9	1681.00
195	49602	10	1750.80
195	49602	11	2664.33
195	49616	9	84.50
195	49616	10	1510.05
195	49616	11	1830.12
195	49690	10	1982.20
196	49552	10	509.58
196	49552	11	2092.67
196	49595	10	672.34
196	49630	9	168.75
196	49682	9	2653.00
196	49682	10	2653.00
196	49853	9	312.50
196	49853	10	238.05
197	49552	10	509.58
197	49552	11	2092.67
197	49595	9	885.06
197	49595	10	632.43
197	49630	9	168.75
197	49682	9	2241.33
197	49682	10	2653.00
197	49853	9	312.50
197	49853	10	238.05
198	49462	10	33.80
198	49613	9	2017.20
198	49613	10	1679.69
198	49613	11	2061.44
198	49647	9	4512.50
198	49687	11	1993.00
198	50014	9	208.33
199	49523	9	3362.00
199	49613	9	2017.20
199	49613	10	1812.46
199	49613	11	2061.44
199	49647	9	4512.50

This table above, shows that for the same tree observed by the same person there can be huge variation in observations from week to week. This is a main drawback from collecting data from Citizen Scientists.

d_filtered %>%
  group_by(common_name, month) %>%
  filter(month >= 9) %>%
  summarize(var_intensity = var(intensity,na.rm = TRUE),
            mean_intensity = mean(intensity, na.rm = TRUE)) %>%
  ggplot(aes(x = month, y = mean_intensity)) +
  geom_line() + facet_wrap(~ common_name) +
  ylab("Average Tree Coverage")

## Warning: Removed 1 rows containing missing values (geom_path).

The above set of graphs depicts the average tree coverage (intensity) from month to month for each species of tree. We would expect the tree coverage to decrease almost linearly, however we can see that’s not the case. For some, the tree coverage was reported to increase in the colder months.

Results

##Making Icons
icons <- awesomeIcons(
  icon = 'ios-close',
  iconColor = 'white',
  library = 'ion',
  markerColor = trees$color)

##Interactive Leaflet Map
leaflet(trees) %>% 
  addTiles() %>%
  addAwesomeMarkers(~lon, ~lat,
                    icon=icons,
                    label = paste("Tag:", trees$tag, "|",
                    "Species:", trees$common_name))

This is an interactive leaflet map to show the different trees around campus. Shown for each marker is the common name and the tag number.

d_filtered %>%
  filter(month >= 9, !is.na(leaf_drop)) %>%
  ggplot(aes(x = month, fill = leaf_drop)) +
  geom_bar(position = "fill") +
  facet_wrap(~ common_name)

The graph pictured above shows the proportion of observations for each tree each month to see the percentage of observations that have been declared as leaves dropped. The decision was made that any tree with an intensity less than 50% has been declared as their leaves being dropped.

ggplot(d_filtered, aes(x = common_name, fill = common_name)) +
  geom_bar(position = "dodge") +
  ylab("Count of Observations") +
  xlab("Species") +
  coord_flip()

The bar chart above shows the raw count of observations made for each tree.

d_filtered %>% 
  filter(month >= 8) %>%
  ggplot(aes(x=date,
             y=intensity,
             group=phenophase_description,
             col=phenophase_description))+
  geom_point()+
  facet_wrap(~common_name)+
  ylim(0,100)+
  geom_smooth(span=3)

This graph was generated with help from Professor Wilson. This graph shows the trend in when leaves start to become colored and we have a smoothing line to indicate the speed at which they change colors.

Conclusions

In the data, we are able to see that in Citizen Science, there can be a lot of variability in observations. It’s interesting to see how data is collected from other individuals and how it contributes to a bigger picture. We can see from the graphs that although most leaves typically drop around October, some trees don’t drop their leaves until much later in the year. Citizen Science is very useful in terms of getting young scientists involved and helping to contribute data to solving a bigger problem. One of the main drawbacks of using citizen science data is the large variation in observations as shown in the table above. Even looking at one particular tag, we can see the huge amount of variation in observations between the different participants. This has implications in our interpretation of these results. We have to keep in mind that because there is such wide variation in amount of observations made and the actual data from the observations that we can not imply causation.

Future Work

For work in the future, it would be neat to collect this data again next year and see how characteristics of the trees changed from year to year. It would also be neat to compare variation metrics between the different years of students. I think another next step would be to incorporate state level ecological data and see if trees in Western New York are different than those in other places of New York due to the differences in climate.

References

Professor Wilson | National Phenology Network | Stackoverflow (code) | Rstudio documentation

GEO 511: Spatial Data Science Project

Contributing to the National Phenology Network

David Torgerson