We’ve been asked to create measures of communities that are “walkable” for several projects. While there is no standard definition of what makes a community “walkable”, and the definition of “walkability” can differ from person to person, we thought an indicator that explores the total length of available sidewalks relative to the total length of streets in a community could be a good place to start. In this blog post, we describe how we used open data from SPC and Allegheny County to create a new measure for how “walkable” a community is. We wanted to create a ratio of the length of a community’s sidewalks to the length of a community’s streets as a measure of pedestrian infrastructure. A ratio of 1 would mean that a community has an equal number of linear feet of sidewalks and streets. A ratio of about 2 would mean that a community has two linear feet of sidewalk for every linear foot of street. In other words, every street has a sidewalk on either side of it. In creating a measure of the ratio of streets to sidewalks, we had to do a little bit of data cleanup. Much of this was by trial and error, ground-truthing the data based on our personal experiences walking in different neighborhoods. Since street data was not shared as open data by many counties in our region either on PASDA or through the SPC open data portal, we limited our analysis of “walkability” to Allegheny County. In looking at the sidewalk data table and map, we noticed that trails were included. While nice to have in the data, we wanted to exclude these two features from the ratio. We did this to avoid a situation where a community that had few sidewalks but was in the same blockgroup as a park with trails would get “credit” for being more “walkable” than it actually is according to our definition. We did this by removing all segments where “Trail” was in the “Type_Name” field. We also used a similar tabular selection method to remove crosswalks from the sidewalk data “Type_Name”=”Crosswalk.” We kept the steps in the dataset along with the sidewalks. In the street data obtained from Allegheny County’s GIS department, we felt like we should try to exclude limited-access highway segments from the analysis, since pedestrians are prohibited from using them, and their presence would have reduced the sidewalk/street ratio in communities where they are located. We did this by excluding street segments whose values in the “FCC” field (designating type of street) equaled “A11” or “A63.” We also removed trails from this dataset by excluding those classified as “H10.” Since documentation was sparse, we looked to see how these features were classified in the data to determine which codes to exclude. After running the data initially, we also realized that excluding alleyways from the calculations also could improve the accuracy of our results. Some of the communities with substantial pedestrian infrastructure have alleyways, and including them would make them appear to be less-”walkable” in our indicator. We removed these from the dataset by removing records with a value of “Aly” or “Way” in the “St_Type” field. We also excluded streets where the word “Alley” appeared in the street name, or “St_Name” field. The full methodology used for this dataset is captured in our blog post, and we have also included the sidewalk and street data used to create the ratio here as well.

This dataset contains spatial boundaries for Bonus Plot Ratio Plans relating to the City of Perth Planning Scheme No.2The Maximum Bonus Plot Ratio Plan shows the total maximum bonus plot ratio that can be granted on a specific lot. This is either 20% or 50%.Bonus plot ratio may be granted under a single category or a combination of Special Residential, Residential, Heritage and Public Facilities.Definition under Schedule 4 “means the maximum percentage increase in the maximum plot ratio which is specified for a lot or part of a lot by the Maximum Bonus Plot Ratio Plan”;Please see https://perth.wa.gov.au/develop/planning-framework/planning-schemes and https://perth.wa.gov.au/develop/planning-framework/planning-policies-and-precinct-plans for more information regarding the City of Perth Planning Schemes.

Mean monthly solar radiation was modelled across Australia using topography from the 1 arcsecond resolution SRTM-derived DEM-S and climatic and land surface data. The SRAD model (Wilson and Gallant, 2000) was used to derive: •\tIncoming short-wave radiation on a sloping surface •\tShort-wave radiation ratio (shortwave on sloping surface / shortwave on horizontal surface) •\tIncoming long-wave radiation •\tOutgoing long-wave radiation •\tNet long-wave radiation •\tNet radiation •\tSky view factor All radiation values are in MJ/m2/day except for short-wave radiation ratio which has no units. The sky view factor is the fraction of the sky visible from a grid cell relative to a horizontal plane.

The radiation values are determined for the middle day of each month (14th or 15th) using long-term average atmospheric conditions (such as cloudiness and atmospheric transmittance) and surface conditions (albedo and vegetation cover). They include the effect of terrain slope, aspect and shadowing (for sun positions at 5 minute intervals from sunrise to sunset), direct and diffuse radiation and sky view.

The monthly data in this collection are available at 3 arcsecond resolution as single (mosaicked) grids for Australia in TIFF format.

The 3 arcsecond resolution versions of these radiation surfaces have been produced from the 1 arcsecond resolution surfaces, by aggregating the cells in a 3x3 window and taking the mean value.

The 1 arcsecond tiled data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:9631 . The 1 arcsecond mosaic data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:18731 Lineage: Source data 1. 1 arcsecond SRTM-derived Smoothed Digital Elevation Model (DEM-S; ANZCW0703014016) 2. Aspect derived from the 1 arcsecond SRTM DEM-S 3. Slope derived from the 1 arcsecond SRTM DEM-S 4. Monthly cloud cover fraction (Jovanovic et al., 2011) 5. Monthly albedo derived from AVHRR (Donohue et al., 2010) 6. Monthly minimum and maximum air temperature (Bureau of Meteorology) 7. Monthly vapour pressure (Bureau of Meteorology) 8. Monthly fractional cover (Donohue et al., 2010) 9. Monthly black-sky and white-sky albedo from MODIS (MCD43A3, B3) (Paget and King, 2008; NASA LP DAAC, 2013) 10. Measurements of daily sunshine hours, 9 am and 3pm cloud cover, and daily solar radiation from meteorological stations around Australia (Bureau of Meteorology)

Solar radiation model Solar radiation was calculated using the SRAD model (Wilson and Gallant, 2000), which accounts for: \tAnnual variations in sun-earth distance \tSolar geometry based on latitude and time of year \tThe orientation of the land surface relative to the sun \tShadowing by surrounding topography \tClear-sky and cloud transmittance \tSunshine fraction (cloud-free fraction of the day) in morning and afternoon \tSurface albedo \tThe effects of surface temperature on outgoing long-wave radiation, which is modulated by incoming radiation and moderated by vegetation cover \tAtmospheric emissivity based on vapour pressure

All input parameters were long-term averages for each month, i.e., monthly climatologies of cloud cover, air temperature, vapour pressure, fractional cover, AVHRR albedo and MODIS albedo.

Circumsolar coefficient was fixed both spatially and temporally at 0.25, while clear sky atmospheric transmissivity and cloud transmittance were varied. Transmittance measures the fraction of radiation passing through a material (air or clouds in this case), while transmissivity measures that fraction for a specified amount of material. SRAD uses a transmittance parameter for cloud, representing an average of all cloud types during cloudy periods, and a transmissivity parameter for clear sky so that the transmittance can vary with the position of the sun in the sky and hence the thickness of atmosphere that radiation passes through on its way to the ground. The clear sky transmissivity τ and cloud transmittance β were calibrated using observed daily radiation and sunshine hours.

References Donohue R. J., McVicar T. R. and Roderick M. L. (2010a). Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate. Journal of Hydrology, 386, 186-197, doi:10.1016/j.jhydrol.2010.03.020.

Donohue, R. J., T. R. McVicar, L. Lingtao, and M. L. Roderick (2010b). A data resource for analysing dynamics in Australian ecohydrological conditions, Austral Ecol, 35, 593–594, doi: 10.1111/j.1442-9993.2010.02144.x.

Erbs, D. G., S. A. Klein, and J. A. Duffie (1982), Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation, Solar Energy, 28(4), 293-302.

Jovanovic, B., Collins, D., Braganza, K., Jakob, D. and Jones, D.A. (2011). A high-quality monthly total cloud amount dataset for Australia. Climatic Change, 108, 485-517.

NASA Land Processes Distributed Active Archive Center (LP DAAC) (2013). MCD43A3, B3. USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota

Paget, M.J. and King, E.A. (2008). MODIS Land data sets for the Australian region. CSIRO Marine and Atmospheric Research Internal Report No. 004. https://remote-sensing.nci.org.au/u39/public/html/modis/lpdaac-mosaics-cmar

Wilson, J.P. and Gallant, J.C. (2000) Secondary topographic attributes, chapter 4 in Wilson, J.P. and Gallant, J.C. Terrain Analysis: Principles and Applications, John Wiley and Sons, New York.

Treated effluent from wastewater treatment plants (WWTPs) contains contaminants not fully removed during the treatment process and that may pose environmental health risks when discharged to surface waters. This data release presents inputs for and results from a wastewater reuse model that used data compiled from several sources to calculate the following estimates for each non-tidal, non-coastline, initialized National Hydrography Dataset Version 2.1 (NHDPlus V2) stream segment in the Potomac River watershed: (1) accumulated wastewater as a percent of total streamflow (ACCWW%); and (2) predicted environmental concentrations (PECs, in micrograms per liter) of 69 municipal effluent-derived contaminants. ACCWW% values were calculated for mean-monthly and mean-annual streamflow conditions for both municipal (model results table: Table1_PotomacACCWW_municipal.csv) and industrial-plus-municipal effluent discharges (model results table: Table2_PotomacACCWW_municipal_plus_industrial.csv). PECs were calculated for mean-monthly and mean-annual streamflow conditions for municipal effluent discharges (model results tables: Table3_PotomacPECs.zip, containing comma separated value files of results for mean-monthly and mean-annual conditions). Model estimates at a stream reach of interest represent the combined total upstream wastewater discharges as well as direct discharges into the segment. Model input data included: (1) National Pollutant Discharge Elimination System-permitted facility outfall locations and 2016 average daily effluent discharges linked to a NHDPlus V2 stream Common Identifier (COMID) and facility-specific information on treatment levels and population served per capita (model input table: Table4_PotomacWWTPs.csv); (2) NHDPlus V2 stream geometry and hydrologic attributes (hydrosequence, startflag, terminalfl, divergence, fromnode, tonode, and Enhanced Runoff Method mean-monthly and mean-annual gage-adjusted streamflow and velocity, 1971-2000) (model input table: Table5_PotomacNHDPlusV2.1_flowlines_hydrology.csv); and (3) contaminant-specific data on consumption, fate, and transport compiled from literature sources or estimated from physicochemical properties (see: supplementary table in Larger Work Citation). In Table 4, where information on population served by the facility was missing, this value was estimated by standardizing to 100 gallons per capita per day. Information on population served was only acquired and estimated for municipal facilities. Where treatment level information was missing, the treatment level was assumed to be primary. Ninety-two percent of WWTPs have an assumed treatment as none was reported. R (version 4.0.4) and Python (version 2.7.16) scripts were used to summarize wastewater inputs from outfall locations by COMID and route and accumulate each wastewater and predicted contaminant loads while accounting for in-stream dilution and attenuation of contaminants. Any users of these data should review the entire metadata record and the associated manuscript (see Larger Work Citation). See 'Distribution liability' statements for more information.