This dataset was created as a result of an analysis examining recommended infrastructure improvements identified as part of the Austin SRTS Infrastructure Plan as part of the 2016 Mobility Bond to evenly distribute improvements between the City's ten Council Districts. These improvements include streets, trails, intersections, and sidewalks around 137 elementary and middle schools. The methodology uses: • Factors: Categories used to express community/agency values and group variables with similar characteristics (e.g. equity, safety, demand). • Variables: Characteristics of roadways, households, neighborhood areas, and other features that can be used to measure each Factor (e.g. population density, sidewalk presence). • Weights: Numbers used to indicate the relative importance of different factors based on community or agency values.

PARD’s Long Range Plan for Land, Facilities and Programs, Our Parks, Our Future (adopted November 2019) compared Austin’s park system to five peer cities: Atlanta, GA, Dallas, TX, Portland, OR, San Antonio, TX, and San Diego, CA. The peer cities were selected based on characteristics such as population, size, density, and governance type. Portland and San Diego were selected as aspirational cities known for their park systems.

Note that the table below presents each scoring area’s 1 to 100 index, where 100 is the highest possible score.

Camera traps have become an important research tool for both conservation biologists and wildlife managers. Recent advances in spatially explicit capture-recapture (SECR) methods have increasingly put camera traps at the forefront of population monitoring programs. These methods allow for benchmark analysis of species density without the need for invasive fieldwork techniques. We conducted a review of SECR studies using camera traps to summarize the current focus of these investigations, as well as provide recommendations for future studies and identify areas in need of future investigation. Our analysis shows a strong bias in species preference, with a large proportion of studies focusing on large felids, many of which provide the only baseline estimates of population density for these species. Furthermore, we found that a majority of studies produced density estimates that may not be precise enough for long-term population monitoring. We recommend simulation and power analysis be conducted before initiating any particular study design and provide examples using readily available software. Furthermore, we show that precision can be increased by including a larger study area that will subsequently increase the number of individuals photo-captured. As many current studies lack the resources or manpower to accomplish such an increase in effort, we recommend that researchers incorporate new technologies such as machine-learning, web-based data entry, and online deployment management into their study design. We also cautiously recommend the potential of citizen science to help address these study design concerns. In addition, modifications in SECR model development to include species that have only a subset of individuals available for individual identification (often called mark-resight models), can extend the process of explicit density estimation through camera trapping to species not individually identifiable.