Context

The Austin Animal Center is the largest no-kill animal shelter in the United States that provides care and shelter to over 18,000 animals each year. As part of the AAC's efforts to help and care for animals in need, the organization makes available its accumulated data and statistics as part of the city of Austin's Open Data Initiative.

Content

The data contains intakes and outcomes of animals entering the Austin Animal Center from the beginning of October 2013 to the present day. The datasets are also freely available on the Socrata Open Data Access API and are updated daily.

The following are links to the datasets hosted on Socrata's Open Data:

• Austin Animal Center Intakes
• Austin Animal Center Outcomes

The data contained in this dataset is the outcomes and intakes data as noted above, as well as a combined dataset. The merging of the outcomes and intakes data was done on a unique key that is a combination of the given Animal ID and the intake number. Several of the animals in the dataset have been taken into the shelter multiple times, which creates duplicate Animal IDs that causes problems when merging the two datasets.

Copied from the description of the Shelter Outcomes dataset, here are some definitions of the outcome types:

• Adoption
  • the animal was adopted to a home
• Barn Adoption
  • the animal was adopted to live in a barn
• Offsite Missing
  • the animal went missing for unknown reasons at an offsite partner location
• In-Foster Missing
  • the animal is missing after being placed in a foster home
• In-Kennel Missing
  • the animal is missing after being transferred to a kennel facility
• Possible Theft
  • Although not confirmed, the animal went missing as a result of theft from the facility
• Barn Transfer
  • The animal was transferred to a facility for adoption into a barn environment
• SNR
  • SNR refers to the city of Austin's Shelter-Neuter-Release program. I believe the outcome is representative of the animal being released.

Acknowledgements

The data presented here is only possible through the hard work and dedication of the Austin Animal Center in saving and caring for animal lives.

Inspiration

Following from the first dataset I posted to Kaggle, Austin Animal Shelter Outcomes, which was initially filtered for just cats as part of an analysis I was performing, I wanted to post the complete outcome and complementing intake datasets. My hope is the great users of Kaggle will find this data interesting and want to explore shelter animal statistics further and perhaps get more involved in the animal welfare community. The analysis of this data and other shelter animal provided datasets helps uncover useful insights that have the potential to save lives directly.

This dataset reports the daily reported number of the 7-day moving average rates of Deaths involving COVID-19 by vaccination status and by age group.

Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak.

Effective November 14, 2024 this page will no longer be updated. Information about COVID-19 and other respiratory viruses is available on Public Health Ontario’s interactive respiratory virus tool: https://www.publichealthontario.ca/en/Data-and-Analysis/Infectious-Disease/Respiratory-Virus-Tool

Data includes:

• Date on which the death occurred
• Age group
• 7-day moving average of the last seven days of the death rate per 100,000 for those not fully vaccinated
• 7-day moving average of the last seven days of the death rate per 100,000 for those fully vaccinated
• 7-day moving average of the last seven days of the death rate per 100,000 for those vaccinated with at least one booster

Additional notes

As of June 16, all COVID-19 datasets will be updated weekly on Thursdays by 2pm.

As of January 12, 2024, data from the date of January 1, 2024 onwards reflect updated population estimates. This update specifically impacts data for the 'not fully vaccinated' category.

On November 30, 2023 the count of COVID-19 deaths was updated to include missing historical deaths from January 15, 2020 to March 31, 2023.

CCM is a dynamic disease reporting system which allows ongoing update to data previously entered. As a result, data extracted from CCM represents a snapshot at the time of extraction and may differ from previous or subsequent results. Public Health Units continually clean up COVID-19 data, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes and current totals being different from previously reported cases and deaths. Observed trends over time should be interpreted with caution for the most recent period due to reporting and/or data entry lags.

The data does not include vaccination data for people who did not provide consent for vaccination records to be entered into the provincial COVaxON system. This includes individual records as well as records from some Indigenous communities where those communities have not consented to including vaccination information in COVaxON.

“Not fully vaccinated” category includes people with no vaccine and one dose of double-dose vaccine. “People with one dose of double-dose vaccine” category has a small and constantly changing number. The combination will stabilize the results.

Spikes, negative numbers and other data anomalies: Due to ongoing data entry and data quality assurance activities in Case and Contact Management system (CCM) file, Public Health Units continually clean up COVID-19, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes, negative numbers and current totals being different from previously reported case and death counts.

Public Health Units report cause of death in the CCM based on information available to them at the time of reporting and in accordance with definitions provided by Public Health Ontario. The medical certificate of death is the official record and the cause of death could be different.

Deaths are defined per the outcome field in CCM marked as “Fatal”. Deaths in COVID-19 cases identified as unrelated to COVID-19 are not included in the Deaths involving COVID-19 reported.

Rates for the most recent days are subject to reporting lags

All data reflects totals from 8 p.m. the previous day.

This dataset is subject to change.

This Open Data Record is comprised of datasets that document human-wildlife coexistence incidents and response actions by Parks Canada Agency from 2010 to 2023. A human-wildlife coexistence (HWC) “incident” is any potential conflict situation between people and wildlife that was assigned to Parks Canada staff to manage to help ensure the safety and wellbeing of people and wildlife. The vast majority of HWC incidents are minor and staff are able to manage them safely with low risk to people, however, a small subset of the dataset is comprised of more hazardous incidents between people and wildlife that can potentially result in injury or death of either wildlife or people. HWC incident data inform Parks Canada Agency policies, programs, and operations, and enable evaluation of HWC patterns to help Parks Canada ensure safe and enjoyable visitor experiences while conserving wildlife and integrity of ecosystems across our national system of protected heritage areas. For any single HWC incident, there may be multiple management actions taken or multiple animals involved, and therefore this Open Data Record includes separate datasets for incidents, responses, animals involved, and human activities. These four datasets include many shared fields, including a unique alphanumeric “Incident Number” that can be used to look-up records between the tables or to join the tables in a relational database. There are also thirteen derived datasets provided to summarize the total number of incidents, animal species involved, animals killed (by human causes), aggressive encounters, unnatural attractants, and response actions taken. Please note: these datasets include some incidents that Parks Canada staff were involved outside of park boundaries on surrounding lands or waters. All HWC incident data remain subject to ongoing revisions as more information comes available or for quality control purposes. All protected, personal, private or confidential information has been removed from these datasets.

Animal mortality caused by vehicle collisions is one of the main ecological impacts of roads. Amphibians are the most affected group and road fatalities have a significant impact on population dynamics and viability. Several studies on Iberian amphibians have shown the importance of country roads on amphibian road mortality, but still, little is known about the situation in northern Portugal. By being more permeable to amphibian passage, country roads represent a greater source of mortality than highways, which act as barriers. Thus, mitigation measures should be applied, but due to the extensive road network, the identification of precise locations (hotspots) and variables related to animal-vehicle collision is needed to plan these measures successfully. The aim of the study was to analyse the spatial occurrence and related factors linked to amphibian mortality on a number of country roads in northern Portugal, using spatial statistics implemented in GIS and applying a binary logistical regression. We surveyed 631 km of road corresponding to seven transects, and observed 404 individual amphibians: 74 (18.3%) alive and 330 (81.7%) road-killed. Bufo bufo represented 80% of the mortality records. Three transects showed clustered distribution of road-kills, and broadleaved forests and road ditches were the most important factors associated with hotspots of road-kill. Logistic regression models showed that habitat quality, Bufo bufo’s habitat preferences, and road ditches are positively associated with amphibians’ road mortality in northern Portugal, whereas average altitude and length of walls were negatively associated. This study is a useful tool to understand spatial occurrence of amphibian road-kills in the face of applying mitigation measures on country roads from northern Portugal. This study also considers the necessity of assessing the condition of amphibian local populations to understand their road-kills spatial patterns and the urgency to apply mitigation measures on country roads. Palabras clave: Amphibian, Mortality

Roads have multiple effects on wildlife, from animal mortality, habitat and population fragmentation, to modification of animal reproductive behavior. Amphibians in particular, due to their activity patterns, population structure, and preferred habitats, are strongly affected by traffic intensity and road density. On the other hand, road-kills studies and conservation measures have been extensively applied on highways, although amphibians die massively on country roads, where conservation measures are not applied. Many countries (e.g. Portugal) have not any national program for monitoring road-kills, a common practice in other European countries (e.g. UK; The Netherlands). This is necessary to identify hotspots of road-kills in order to implement conservation measures correctly. However, monitoring road-kills is expensive and time consuming, and depend mainly on volunteers. Therefore, cheap, easy to implement, and automatic methods for detecting road-kills over larger areas (broad monitoring) and along time (continuous monitoring) are necessary. We present here the preliminary results from a research project which aims to build a cheap and efficient system for detecting amphibians roadkills using computer-vision techniques from robotics. We propose two different solutions: 1) a Mobile Mapping System to detect automatically amphibians’ road-kills in roads, and 2) a Fixed Detection System to monitor automatically road-kills in a particular road place during a long time. The first methodology will detect and locate road-kills through the automatic classification of road surface images taken from a car with a digital camera, linked to a GPS. Road kill casualties will be detected automatically in the image through a classification algorithm developed specifically for this purpose. The second methodology will detect amphibians crossing a particular road point, and determine if they survive or not. Both Fixed and Mobile system will use similar programs. The algorithm is trained with existing data. For now, we can only present some results about the Mobile Mapping System. We are performing different tests with different cameras, namely a lineal camera, used in different industrial solutions of control quality, and an outdoor Go-pro camera, very famous on different sports like biking. Our results prove that we can detect different road-killed and live animals to an acceptable car speed and at a high spatial resolution. Both Mapping Systems will provide the capacity to detect automatically the casualties of road-kills. With these data, it will be possible to analyze the distribution of road-kills and hotspots, to identify the main migration routes, to count the total number of amphibians crossing a road, to determine how many of that individuals are effectively road-killed, and to define where conservation measures should be implemented. All these objectives will be achieved more easily at with a lower cost in funds, time, and personal resources.

Road ecology is a recent branch of Conservation biology that studies the effects of roadnetworks and motorized traffic on biodiversity and ecosystems. The most investigated impacts are road-kills and barrier effects, but most studies are descriptive approximations. Contrarily, the primary purpose of Road ecology should be to determine the mechanisms producing road-related impacts, with the aim to plan the most suitable mitigation measures. The main objective of this thesis was to produce a relevant contribution to Road ecology, suggesting how to investigate the mechanisms potentially determining road-kills, barrier effects and the effectiveness of mitigation measures, but also increasing the knowledge about little-known road-related impacts such as changes in behavior and biological invasions. The first chapter of this thesis aimed to determine the life history, temporal and spatial factors affecting road-kill probability for vertebrate species in a Mediterranean landscape (Doñana Natural Park, southwestern Spain). Abundant species were more road-killed than rare species, but also ectotherms had higher road-kill probability than endotherms (including birds). Species abundance was also the most relevant factor determining road-kill probability (both temporally and spatially) for different functional groups of species (for example resident, breeding, wintering and migrant birds in the temporal analysis and small passerines in the spatial analysis). The second chapter was the first study investigating the factors determining barrier effect along a heterogeneous roadnetwork (ie paved and unpaved roads with different traffic intensity). Our study area … Palabras clave: Population

Animal mortality on roads is one of the main concerns on wildlife conservation. Due to their habitat requirements, amphibians became one of the most commonly road-killed group and this may affect their population viability. Implementation of mitigation measures may overcome the problem. However, due to the extensive road network, their application is very expensive and required a better understanding in where they should be implemented. Mortality hotspots can be identified as clusters of road-killed records) using GIS (Geographic Information Systems). Although there are several statistical methods available, it is lacking a comparison analysis of them in order to understand their pros and contras. The aim of this study was to analyse possible differences between global, multi-scale and local spatial analysis methods in defining hotspots using amphibian road fatality data collected in northern Portugal country roads. We calculated the Nearest neighbor index, Morans I and Getis-ord General in order to compare the global clustering of points in seven sampled roads, and three were identified as clustered. We used Ripley K-function, Ripley L-function and F function to calculate the best scale for Malo's equation and Kernel density analysis in detecting hotspots and we compared their detection performance with Local Indicators of Association (LISA) (i.e Local Moran's I and Getis-ord Gi). Three different GIS software applications were used: ArcGis, Quantum GIS with R (opensource) and GeoDa (opensource). Results showed the importance of using multidistance spatial cluster analysis to define the best scale for hotspot detection with Malo´s equation and Kernel density analysis. Here we also suggest the advantages of Local Indicators of Association (LISA) for detecting clusters with the contribution of each individual observation (Local Morans I and Getis-ord Gi).

The presence of roads has a significant impact on wildlife conservation, since it is as an important cause of direct mortality of individuals as well as a factor of habitat fragmentation. The LIFE SAFE-CROSSING project (https://life.safe-crossing.eu/) aims to mitigate the impact of roads on three priority species in four European countries: the Marsican brown bear and the Wolf in Italy, the Iberian lynx in Spain and the Brown bear in Greece and Romania. Based on the best practices developed during the previous LIFE STRADE project (www.lifestrade.it) the current project started in September 2018 and will last until August 2023. The main activities of the project are: - Identification and monitoring of the high-risk road segments; - Implementation of actions to prevent animal-vehicle collisions (AVC) and to reduce habitat fragmentation; - Raising awareness of drivers and of policy makers on the risk and prevention of AVC. In order to select the road segments where to implement the prevention actions the “Kernel Density Estimation Plus (KDE+)” method was applied, using over 500 cases of road collisions with large carnivores and with other medium and large sized mammals. We also analyzed the telemetry data of radio-tagged animals in order to identify the road segments with the highest crossing rates. An accurate monitoring of hotspots of road kills has then been made to select the exact locations for the installation of the AVC prevention tools, and we also monitored the number and speed of vehicles on the target roads on 24 hours. On the basis of these activities 27 AVC Prevention System, developed in the LIFE STRADE project, will be installed and 30 km of roads will be equipped with innovative road side active reflectors. To favor habitat connectivity 80 existing underpasses will be readapted to increase their use by wildlife. The innovative “neuromarketing” technique is being experimented in order to produce road signs specifically designed for raising awareness of drivers about the road kill problem and to encourage them to adopt an adequate driving behavior. A specific information campaign is carried out with the local driving schools, and a thematic video- game will be developed to involve and attract young people. The LIFE SAFE-CROSSING project therefore is a tool to face the problems of the impact on roads on biodiversity involving associations and private bodies at international level. The main characteristic of this initiative is to act in different ways against road kills and habitat fragmentation and to provide an example for future cases of replication.