Social media reports of urban carnivores in the San Gabriel Valley of Los Angeles county
Data files
Apr 10, 2025 version files 144.08 KB
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general_bear_conflict_neighborhood_data.csv
26.46 KB
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general_coyote_conflict_neighborhood_data.csv
26.52 KB
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README.md
13.72 KB
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specific_bear_conflict_neighborhood_data.csv
26.30 KB
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specific_coyote_conflict_neighborhood_data.csv
26.49 KB
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wildlife_sightings_neighborhood_data.csv
24.58 KB
Abstract
In this study, we analyzed 2,584 posts and comments on carnivore sightings, human-carnivore interactions, and attitudes toward carnivores via the neighborhood-based social media platform Nextdoor, focusing on 52 peri-urban neighborhoods near the Angeles National Forest in California. We focused on the two most frequently discussed species: coyote (Canis latrans) and American black bear (Ursus americanus). We then analyzed social-ecological covariates as potential predictors of Nextdoor carnivore reports, and also compared the sightings of these species to data collected on the popular biodiversity logging application, iNaturalist.
Dataset DOI: 10.5061/dryad.80gb5mm04
Description of the data and file structure
We estimated demographic and ecological characteristics of Nextdoor neighborhoods using ArcGis Pro Version 2.8.6 (ESRI 2022). We calculated demographic estimates for each neighborhood by weighing the values in constituent census blocks and block groups by the percent to which they constituted each neighborhood polygon. We used curated data on total population, population by race, median household income, and median home value for each neighborhood (US Census Bureau 2020). We used a normalized difference vegetation index (NDVI) dataset derived from imagery (60cm spatial resolution) collected by the National Agriculture Imagery Program in May 2020 (California Department of Fish and Wildlife, 2020) to calculate mean NDVI for each neighborhood. We used data regarding votes for major candidates in the 2020 presidential election as a proxy for neighborhood-level political leaning; however, these data columns are excluded from our public dataset due to access and reproduction restrictions. (Voting and Election Science Team, 2020). We calculated the distance from the center point of each neighborhood to the Angeles National Forest as a measure of distance to the nearest natural area. By dividing the number of Nextdoor users registered to each neighborhood with the estimated population of each neighborhood based on census data, we calculated a percentage of the population represented on Nextdoor for each neighborhood.
Files and variables
1. File: wildlife_sightings_neighborhood_data.csv
Variables:
● neighborhood_code: Anonymized identifier for each neighborhood.
● total_posts: The total number of social media posts related to wildlife sightings in each neighborhood (count).
● users_total: The total number of registered users in that neighborhood (count).
● users_over_population: Ratio of registered users over the total population (percentage).
● median_household_income: Median household income in each neighborhood (USD).
● ndvi_min, ndvi_max, ndvi_range, ndvi_mean, ndvi_std, ndvi_median, ndvi_pct90: Various statistical measures of the Normalized Difference Vegetation Index (NDVI) in each neighborhood, which indicate vegetation health and coverage.
● population_total: Total population of each neighborhood (count).
● count_ and percent_*: Counts and percentages of different racial and ethnic groups in each neighborhood.
● area_sqkm, area_sqmi: Area of each neighborhood in square kilometers and square miles.
● distance_to_nf: Distance to the Los Angeles National Forest (mi).
● pop_by_sqmi: Total population divided by square mile.
● black_bear, bobcat, coyote, puma, wildlife: The number of specific wildlife sightings of each species in each neighborhood (count).
2. File: specific_coyote_conflict_neighborhood_data.csv
Variables:
● neighborhood_code: Anonymized identifier for each neighborhood.
● total_posts: Total number of social media posts related to coyote conflicts in each neighborhood (count).
● users_total: Total number of registered users in each neighborhood (count).
● users_over_population: Ratio of registered users over the total population (percentage).
● median_household_income: Median household income in each neighborhood (USD).
● ndvi_min, ndvi_max, ndvi_range, ndvi_mean, ndvi_std, ndvi_median, ndvi_pct90: Various statistical measures of the NDVI in each neighborhood.
● population_total: Total population of each neighborhood (count).
● count_ and percent_*: Counts and percentages of different racial and ethnic groups in each neighborhood.
● area_sqkm, area_sqmi: Area of each neighborhood in square kilometers and square miles.
● distance_to_nf: Distance to the Los Angeles National Forest (mi).
● pop_by_sqmi: Total population divided by square mile.
● conflict_2: Number of reports of an animal following pet or person (count).
● conflict_3: Number of reports of an attacked or injured pet (count).
● conflict_4: Number of reports of an animal that** **attacked and killed pet (include instances where the pet is reported to have disappeared or been “lost”, likely to wildlife) (count).
● conflict_5: Number of reports of an animal that attacked, injured, or killed a person (count).
● other_conflict: Number of reports of other conflict (count).
● allconflict: Total number of coyote conflicts in each neighborhood (count).
● users_per_sqmi: Number of registered users per square mile.
● specificsightings: Number of specific sightings of coyotes in each neighborhood (count).
● conflictratio: Ratio of conflicts to total sightings.
● conflict_per_sqmi: Number of coyote conflicts per square mile (count).
3. File: general_coyote_conflict_neighborhood_data.csv
Variables:
● neighborhood_code: Anonymized identifier for each neighborhood.
● total_posts: Total number of social media posts related to general coyote conflicts in each neighborhood (count).
● users_total: Total number of registered users in each neighborhood (count).
● users_over_population: Ratio of registered users over the total population.
● median_household_income: Median household income in each neighborhood (USD).
● ndvi_min, ndvi_max, ndvi_range, ndvi_mean, ndvi_std, ndvi_median, ndvi_pct90: Various statistical measures of the NDVI in each neighborhood.
● population_total: Total population of each neighborhood (count).
● count_ and percent_*: Counts and percentages of different racial and ethnic groups in each neighborhood.
● area_sqkm, area_sqmi: Area of each neighborhood in square kilometers and square miles.
● distance_to_nf: Distance to the Los Angeles National Forest (mi).
● pop_by_sqmi: Population density per square mile.
● conflict_2: Number of reports of an animal following pet or person (count).
● conflict_3: Number of reports of an attacked or injured pet (count).
● conflict_4: Number of reports of an animal that attacked and killed pet (include instances where the pet is reported to have disappeared or been “lost”, likely to wildlife) (count).
● conflict_5: Number of reports of an animal that attacked, injured, or killed a person (count).
● other_conflict: Number of reports of other conflict (count).
● allconflict: Total number of coyote conflicts in each neighborhood (count).
● users_per_sqmi: Number of registered users per square mile (count).
● generalsightings: General sightings of coyotes in each neighborhood (count).
● conflictratio: Ratio of conflicts to total general sightings.
● conflict_per_sqmi: Number of coyote conflicts per square mile.
4. File: specific_bear_conflict_neighborhood_data.csv
Variables:
● conflict_2: Number of reports of an animal following pet or person (count).
● conflict_4: Number of reports of an animal that attacked and killed pet (include instances where the pet is reported to have disappeared or been “lost”, likely to wildlife) (count).
● conflict_5: Number of reports of an animal that attacked, injured, or killed a person (count).
● damaged_property_conflict: Number of reports of bear conflicts involving property damage (count).
● other_conflict: Number of reports of other conflict (count).
● trash_conflict: Number of reports of bear conflicts involving bears accessing trash (count).
● neighborhood_code: Anonymized identifier for each neighborhood.
● total_posts: Total number of social media posts related to bear conflicts in each neighborhood (count).
● users_total: Total number of registered users in each neighborhood (count).
● users_over_population: Ratio of registered users over the total population.
● median_household_income: Median household income in each neighborhood (USD).
● ndvi_min, ndvi_max, ndvi_range, ndvi_mean, ndvi_std, ndvi_median, ndvi_pct90: Various statistical measures of the NDVI in each neighborhood.
● population_total: Total population of each neighborhood (count).
● count_ and percent_*: Counts and percentages of different racial and ethnic groups in each neighborhood.
● area_sqkm, area_sqmi: Area of each neighborhood in square kilometers and square miles.
● distance_to_nf: Distance to the Los Angeles National Forest (mi).
● pop_by_sqmi: Population density per square mile.
● allconflict: Total number of bear conflicts in each neighborhood (count).
● users_per_sqmi: Number of registered users per square mile.
● specific_sightings: Specific sightings of bears in each neighborhood (count).
● conflictratio: Ratio of conflicts to total specific sightings.
● conflict_per_sqmi: Number of bear conflicts per square mile (count).
5. File: general_bear_conflict_neighborhood_data.csv
Variables:
● conflict_2: Number of reports of an animal following pet or person (count).
● conflict_4: Number of reports of an animal that attacked and killed pet (include instances where the pet is reported to have disappeared or been “lost”, likely to wildlife) (count).
● conflict_5: Number of reports of an animal that attacked, injured, or killed a person (count).
● other_conflict: Number of reports of other conflict (count).
● damaged_property_conflict: Number of reports of bear conflicts involving property damage (count).
● trash_conflict: Number of reports of bear conflicts involving bears accessing trash (count).
● neighborhood_code: Anonymized identifier for each neighborhood.
● total_posts: Total number of social media posts related to bear conflicts in each neighborhood (count).
● users_total: Total number of registered users in each neighborhood (count).
● users_over_population: Ratio of registered users over the total population (count).
● median_household_income: Median household income in each neighborhood (USD).
● ndvi_min, ndvi_max, ndvi_range, ndvi_mean, ndvi_std, ndvi_median, ndvi_pct90: Various statistical measures of the NDVI in each neighborhood.
● population_total: Total population of each neighborhood (count).
● count_ and percent_*: Counts and percentages of different racial and ethnic groups in each neighborhood.
● area_sqkm, area_sqmi: Area of each neighborhood in square kilometers and square miles.
● distance_to_nf: Distance to the Los Angeles National Forest (mi).
● pop_by_sqmi: Population density per square mile.
● allconflict: Total number of bear conflicts in each neighborhood (count).
● users_per_sqmi: Number of registered users per square mile.
● conflictratio: Ratio of conflicts to total general sightings.
● conflict_per_sqmi: Number of bear conflicts per square mile (count).
● general_sightings: general sightings of bears in each neighborhood (count).
Access information
Data was derived from the following sources:
We used curated data on total population, population by race, median household income, and median home value for each neighborhood (US Census Bureau 2020). We used a normalized difference vegetation index (NDVI) dataset derived from imagery (60cm spatial resolution) collected by the National Agriculture Imagery Program in May 2020 (California Department of Fish and Wildlife, 2020) to calculate mean NDVI for each neighborhood.
In our study we used data regarding votes for major candidates in the 2020 presidential election as a proxy for neighborhood-level political leaning; however, these data columns are excluded from our public dataset due to access and reproduction restrictions (Voting and Election Science Team, 2020).
U.S. Census Bureau (2020). American Community Survey. Retrieved from data.census.gov.
California Department of Fish and Wildlife. (2020). “NAIP 2020 NDVI, California”. Retrieved from https://data-cdfw.opendata.arcgis.com/datasets/770a6372328c4582b5801db2c9a9597e/explore
Voting and Election Science Team. (2020). “2020 Precinct-Level Election Results”, https://doi.org/10.7910/DVN/K7760H, Harvard Dataverse, V35
With the help of community members recruited through the personal network of W.S., we created three Nextdoor accounts using addresses in the cities of Arcadia, El Monte, and Covina. We gathered posts and comments for analysis through systematic searches conducted via Nextdoor’s search function while logged into each of the three accounts. We searched Nextdoor using the following keywords: “wildlife”, “bear”, “mountain lion”, “puma”, “coyote”, “bobcat”, “deer”, “raccoon”, and “opossum” between December of 2021 and January of 2022. While results relating to species other than black bear and coyote were later excluded due to limited sample sizes, the Nextdoor search algorithm returned posts related to multiple species under each keyword; thus, our two focal species were sometimes mentioned in comments related to other species within our search. The posts and comments gathered were produced by users between July of 2019 and January of 2022. We reviewed every post returned in the search and included posts which specifically mention wildlife or human-wildlife interactions in an initial data set. We then reviewed each post and all of the comments on each post, and included posts and comments in our subsequent analysis if they contained mention of wildlife sightings, attitudes towards wildlife, or wildlife conflict mitigation strategies. We excluded joke comments and comments expressing attitudes towards a conflict event but not towards the wildlife themselves (e.g., “I’m so sorry you lost your cat”), as well as general statements about the behavior or natural history of urban wildlife, and posts about species other than coyote and black bear. We entered each post or comment into a spreadsheet for further analysis, along with the date of posting and the Nextdoor neighborhood of the user.