Urban Ecology Over Time by Kaggle | Technology and IT

About this Dataset

Urban Ecology Over Time

Urban Ecology over time

Exploring Cameras Traps, Scans, and Surveys

About this dataset

This dataset offers a comprehensive overview of the ecology and behaviour of urban coyotes (Canis latrans) during a period that profoundly affected everyday life. It includes important camera trap data, scat analyses, and survey information. Here we have the opportunity to gain valuable insights into the remarkable adaptations of coyotes in an urban setting, both before and after the pandemic.

The gathered data is multi-faceted, providing us with essential information on food sources (Percent_mass & Percent_freq), bone, fur, insect presence in scat samples; type of seeds, trash and feathers; scales; subjects pertaining to surveys; specific questions (1-54); pre/post responses; species observed by camera traps; formatted date/time variables (date & hour); family classifications (class-order-species); scientific names & common name as well as origins.

With this data set in hand there are limitless possibilities for research projects into populations that thrive even in cities – research projects that could offer valuable advice for wildlife preservation alongside human habitation!

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How to use the dataset

This dataset provides a unique and valuable insight into the ecology of urban coyotes during the pandemic. Our data includes camera trap data, scat analysis data, and survey results collected over a period of five months in 2020.

The camera trap data contains information on the species observed in an urban environment, including their directions and distances traveled. The scat analysis files provide details on what composition of food sources was found in each sample as well as the frequency and mass percentages associated with each food source. Surveys were conducted to gain more comprehensive understanding of coyote behavior both before and after the pandemic began. As such, our dataset contains columns such as Origin, Family and Species to identify which type or family of animal or plant was found in each sample; Date/Time/Hour for when observations were made; Camera for which camera took each image; Food_Source for what kind of food sources were found; Percent_mass/freq for respective percentages associated with measured frequencies or masses; Bones/Fur/Insects etc to identify whether bodies or hair from prey animals were present inside a sample; Pre(X)/Post(X) to indicate whether these same questions were answered before (Pre)or after (Post)the start of COVID-19 lockdown measures etc respectively

This dataset can be used by a variety of audiences from policy makers making decisions about managing wildlife amidst unprecedented global changes due to climate change & COVID-19 lockdowns affecting ecosystems across many scales simultaneously & understanding their responses quickly enough so that effective policies may be put into place consequently researchers who are looking to understand the diet transition patterns that occur amidst various naturalistic pressures & see how they affect animals’ behaviors & relation within ecosystems - ecotourism groups looking at accurately assessing animal populations within cities over time so that they can better understand movements based upon seasonality & help plan guided tours accordingly – educators who highlight scale specific disturbances caused at different levels in order to teach students conceptually rather than technically etc

Overall this dataset offers an affordable way accessible way for people belonging from different walks life (including but not limited detect people working towards policy making , research , teaching ) use this joint collection interesting insights on how wildlife behave differently under peculiar circumstances like those created due covid - 19

Research Ideas

Using the survey data and camera trap data, a study can be conducted to compare coyote activity during the pandemic versus before it. This could provide insight into how coyotes adapted their behavior in response to changes in urban environments due to COVID restrictions.

By combining scat analysis with camera trap and survey data, researchers can assess dietary shifts that occurred during the pandemic and identify areas where coyotes are most likely to find food.

By creatinga map of where coyote sightings were recorded throughout the duration of the pandemic, researchers can gain insight into coyote dispersal patterns across an urban environment over time, giving clues as to how they move within city limits and adapts to changes in their habitat due to human activities such as construction or increased presence of people outdoors

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: scat_data_percent.csv

Column name	Description
Food_source	The type of food source found in the scat sample. (String)
Percent_mass	The percentage of the sample that the food source makes up. (Float)

File: species_observed.csv

Column name	Description
Species	The scientific name of the species. (String)
Family	The scientific name of the family the species belongs to. (String)
Order	The scientific name of the order the species belongs to. (String)
Class	The scientific name of the class the species belongs to. (String)
Common name	The common name of the species. (String)
Origin	The origin of the species. (String)

File: camera_trap_data.csv

Column name	Description
Species	The scientific name of the species. (String)
Camera	The camera used to capture the data. (String)
Date	The date the data was captured. (Date)
Time	The time the data was captured. (Time)
Hour	The hour the data was captured. (Integer)

File: scat_data_freq.csv

Column name	Description
Food_source	The type of food source found in the scat sample. (String)
Percent_freq	The percentage frequency of the food source found in the scat sample. (Float)

File: scat_data_raw.csv

Column name	Description
Bone	The presence of bone fragments in the scat sample. (Boolean)
Fur	The presence of fur fragments in the scat sample. (Boolean)
Insect	The presence of insect fragments in the scat sample. (Boolean)
Snail	The presence of snail fragments in the scat sample. (Boolean)
Seeds	The presence of seed fragments in the scat sample. (Boolean)
Trash	The presence of trash items such as plastic items or aluminum cans in the scat sample. (Boolean)
Feathers	The presence of feathers in the scat sample. (Boolean)
Scales	The presence of scales in the scat sample. (Boolean)

File: survey_data.csv

Column name	Description
Subject	The subject of the survey. (String)
Question 1	The first question on the survey. (String)
Pre1	The response to the first question before the pandemic. (String)
Post1	The response to the first question after the pandemic. (String)
Question 3	The third question on the survey. (String)
Pre3	The response to the third question before the pandemic. (String)
Post3	The response to the third question after the pandemic. (String)
Question 5	The fifth question on the survey. (String)
Pre5	The response to the fifth question before the pandemic. (String)
Post5	The response to the fifth question after the pandemic. (String)
Question 7	The seventh question on the survey. (String)
Pre7	The response to the seventh question before the pandemic. (String)
Post7	The response to the seventh question after the pandemic. (String)
Question 8	The eighth question on the survey. (String)
Pre8	The response to the eighth question before the pandemic. (String)
Post8	The response to the eighth question after the pandemic. (String)
Question 10	The tenth question on the survey. (String)
Pre10	The response to the tenth question before the pandemic. (String)
Post10	The response to the tenth question after the pandemic. (String)
Question 11	The eleventh question on the survey. (String)
Pre11	The response to the eleventh question before the pandemic. (String)
Post11	The response to the eleventh question after the pandemic. (String)
Question 13	The thirteenth question on the survey. (String)
Pre13	The response to the thirteenth question before the pandemic. (String)
Post13	The response to the thirteenth question after the pandemic. (String)
Question 14	The fourteenth question on the survey. (String

Acknowledgements

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

Tables

Camera Trap Data

@kaggle.thedevastator_urban_coyote_activity_and_diet_data_during_covid.camera_trap_data

19.68 KB
1983 rows
5 columns


CREATE TABLE camera_trap_data (
  "camera" VARCHAR,
  "species" VARCHAR,
  "date" VARCHAR,
  "time" VARCHAR,
  "hour" BIGINT
);

Scat Data Freq

@kaggle.thedevastator_urban_coyote_activity_and_diet_data_during_covid.scat_data_freq

2.25 KB
9 rows
2 columns


CREATE TABLE scat_data_freq (
  "food_source" VARCHAR,
  "percent_freq" BIGINT
);

Scat Data Percent

@kaggle.thedevastator_urban_coyote_activity_and_diet_data_during_covid.scat_data_percent

2.83 KB
200 rows
2 columns


CREATE TABLE scat_data_percent (
  "food_source" VARCHAR,
  "percent_mass" DOUBLE
);

Scat Data Raw

@kaggle.thedevastator_urban_coyote_activity_and_diet_data_during_covid.scat_data_raw

6.49 KB
25 rows
8 columns


CREATE TABLE scat_data_raw (
  "bone" DOUBLE,
  "fur" DOUBLE,
  "insect" DOUBLE,
  "snail" DOUBLE,
  "seeds" DOUBLE,
  "trash" DOUBLE,
  "feathers" DOUBLE,
  "scales" DOUBLE
);

Species Observed

@kaggle.thedevastator_urban_coyote_activity_and_diet_data_during_covid.species_observed

6.47 KB
40 rows
6 columns


CREATE TABLE species_observed (
  "species" VARCHAR,
  "family" VARCHAR,
  "order" VARCHAR,
  "class" VARCHAR,
  "common_name" VARCHAR,
  "origin" VARCHAR
);

Survey Data

@kaggle.thedevastator_urban_coyote_activity_and_diet_data_during_covid.survey_data

64.97 KB
22 rows
86 columns


CREATE TABLE survey_data (
  "subject" BIGINT,
  "question_id_1" VARCHAR,
  "question_1" VARCHAR,
  "pre1" VARCHAR,
  "post1" VARCHAR,
  "question_id_3" VARCHAR,
  "question_3" VARCHAR,
  "pre3" VARCHAR,
  "post3" VARCHAR,
  "question_id_5" VARCHAR,
  "question_5" VARCHAR,
  "pre5" VARCHAR,
  "post5" VARCHAR,
  "question_id_7" VARCHAR,
  "question_7" VARCHAR,
  "pre7" VARCHAR,
  "post7" VARCHAR,
  "question_id_8" VARCHAR,
  "question_8" VARCHAR,
  "pre8" VARCHAR,
  "post8" VARCHAR,
  "question_id_10" VARCHAR,
  "question_10" VARCHAR,
  "pre10" VARCHAR,
  "post10" VARCHAR,
  "question_id_11" VARCHAR,
  "question_11" VARCHAR,
  "pre11" VARCHAR,
  "post11" VARCHAR,
  "question_id_13" VARCHAR,
  "question_13" VARCHAR,
  "pre13" VARCHAR,
  "post13" VARCHAR,
  "question_id_14" VARCHAR,
  "question_14" VARCHAR,
  "pre14" VARCHAR,
  "post14" VARCHAR,
  "question_id_15" VARCHAR,
  "question_id_16" VARCHAR,
  "question_16" VARCHAR,
  "pre16" VARCHAR,
  "post16" VARCHAR,
  "question_id_26" VARCHAR,
  "question_26" VARCHAR,
  "pre26" VARCHAR,
  "post26" VARCHAR,
  "question_id_29" VARCHAR,
  "question_29" VARCHAR,
  "pre29" VARCHAR,
  "post29" VARCHAR,
  "question_id_30" VARCHAR,
  "question_30" VARCHAR,
  "pre30" VARCHAR,
  "post30" VARCHAR,
  "question_id_34" VARCHAR,
  "question_34" VARCHAR,
  "pre34" VARCHAR,
  "post34" VARCHAR,
  "question_id_40" VARCHAR,
  "question_40" VARCHAR,
  "pre40" VARCHAR,
  "post40" VARCHAR,
  "question_id_42" VARCHAR,
  "question_42" VARCHAR,
  "pre42" VARCHAR,
  "post42" VARCHAR,
  "question_id_43" VARCHAR,
  "question_43" VARCHAR,
  "pre43" VARCHAR,
  "post43" VARCHAR,
  "question_id_45" VARCHAR,
  "question_45" VARCHAR,
  "n_45pre" VARCHAR,
  "n_45post" VARCHAR,
  "question_id_49" VARCHAR,
  "question_49" VARCHAR,
  "n_49pre" VARCHAR,
  "n_49post" VARCHAR,
  "question_id_52" VARCHAR,
  "question_52" VARCHAR,
  "n_52pre" VARCHAR,
  "n_52post" VARCHAR,
  "question_id_54" VARCHAR,
  "question_54" VARCHAR,
  "pre" VARCHAR,
  "post" VARCHAR
);