Austin Watershed Reach Index and Problem Scores


Files Size Format Created Updated License Source
2 174kB csv zip 3 months ago John Snow Labs Standard License John Snow Labs Data City of Austin

Data Files

File Description Size Last changed Download
austin-watershed-reach-index-and-problem-scores-csv 17kB csv (17kB) , json (90kB)
austin-watershed-reach-index-and-problem-scores_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 21kB zip (21kB)


This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
Watershed_ID 1 integer A Unique watershed Identification Number
Watershed_Name 2 string Official Watershed Name
Integrity_Score_ID 3 integer Primary Integer Key for this dataset
Watershed_Reach 4 string 1 is most downstream reach and ascends for subsequent upstream reaches in the same watershed.
Year_of_Observation 5 date (%Y-%m-%d) The City of Austin Fiscal year the data points are associated with. FY 2014 started on 01-OCT-2013 and ended 30-SEP-2014.
Index_Phase 6 integer For streams only - one of two phases. Lakes all collected in same phase.
Index_Source_Type 7 string EII - Environmental Integrity Index or ALI - Austin Lakes Index
Overall_Score 8 integer Overall Index Score. 100 = best condition. Average of other index scores. Problem scores not included.
Aquatic_Life 9 integer Aquatic Life Index score. 100 = best condition. (Bugs, diatoms abundance, diversity, pollution tolerance and other metrics)
Contact_Recreation 10 integer Contact Recreation Index score (for creeks only). 100 = best condition. (bacteria)
Eutrophication 11 integer Eutrophication Index score (for lakes only). 100 = best condition. In general, lower chlorophyll-a abundance and lower proportion of blue-green algae lead to a higher score to represent a superior trophic condition.
Habitat 12 integer Habitat Index score. 100 = best condition. Instream cover and substrate niches.
Non_Contact_Recreation 13 integer Non Contact Recreation Index score (creeks only). 100 = best condition. Aesthetics, odor, safety.
Sediment 14 integer Sediment Index score. 100 = best condition. Metals, pcbs, pesticides average.
Vegetation 15 integer Vegetation Index score (for lakes only). 100 = best condition
Water_Quality 16 integer Water Quality Index score. 100 = best condition. (nutrients, temp, tss)
Animal_Waste_Problem 17 integer Animal Problem score (creeks only). 100 = worst condition. Pet waste.
Construction_Runoff_Problem 18 integer Construction TSS Problem score (creeks only). 100 = worst condition. Erosion and Sedimentation controls failure.
Fertilizer_Problem 19 integer Fertilizer problem score (creeks only). 100 = worst condition. Nitrate.
Litter_Problem 20 integer Litter problem score (creeks only). 100 = worst condition. Trash
Riparian_Vegetation_Problem 21 integer Riparian Vegetation problem score (creeks only). 100 = worst condition. Not enough riparian cover.
Sediment_Problem 22 integer Sediment problem score (creeks only). 100 = worst condition. Worst of the problems set score.
Sewage_Problem 23 integer Sewage problem score (creeks only). 100 = worst condition. Water Quality problem caused by sewage.
Stability_Problem 24 integer Stability problem score (creeks only). 100 = worst condition. Stream bank failures.
Water_Quality_Problem 25 integer Water Quality problem score (creeks only). 100 = worst condition. Water quality worst case.
Created_Date 26 date (%Y-%m-%d) Date when Record was Created
Modified_Date 27 date (%Y-%m-%d) Date when Record was Modified

Import into your tool

Data-cli or just data is the program to get and post your data with the datahub.
Use data with the almost like you use git with the github. Here are installation instructions.

data get
tree JohnSnowLabs/austin-watershed-reach-index-and-problem-scores
# Get a list of dataset's resources
curl -L -s | grep path

# Get resources

curl -L

curl -L

If you are using R here's how to get the data you want quickly loaded:

install.packages("jsonlite", repos="")

json_file <- ''
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# get list of all resources:

# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
    path_to_file = json_data$resources$path[i]
    data <- read.csv(url(path_to_file))

Note: You might need to run the script with root permissions if you are running on Linux machine

Install the Frictionless Data data package library and the pandas itself:

pip install datapackage
pip install pandas

Now you can use the datapackage in the Pandas:

import datapackage
import pandas as pd

data_url = ''

# to load Data Package into storage
package = datapackage.Package(data_url)

# to load only tabular data
resources = package.resources
for resource in resources:
    if resource.tabular:
        data = pd.read_csv(resource.descriptor['path'])
        print (data)

For Python, first install the `datapackage` library (all the datasets on DataHub are Data Packages):

pip install datapackage

To get Data Package into your Python environment, run following code:

from datapackage import Package

package = Package('')

# print list of all resources:

# print processed tabular data (if exists any)
for resource in package.resources:
    if resource.descriptor['datahub']['type'] == 'derived/csv':

If you are using JavaScript, please, follow instructions below:

Install data.js module using npm:

  $ npm install data.js

Once the package is installed, use the following code snippet:

const {Dataset} = require('data.js')

const path = ''

// We're using self-invoking function here as we want to use async-await syntax:
;(async () => {
  const dataset = await Dataset.load(path)
  // get list of all resources:
  for (const id in dataset.resources) {
  // get all tabular data(if exists any)
  for (const id in dataset.resources) {
    if (dataset.resources[id]._descriptor.format === "csv") {
      const file = dataset.resources[id]
      // Get a raw stream
      const stream = await
      // entire file as a buffer (be careful with large files!)
      const buffer = await file.buffer
      // print data