20180629
Walkability Measures for Florida
vector digital data
This research is commissioned by the Florida Department of Health in hopes of quantifying environmental factors in Florida for the purpose of assisting local planners and designers with information potentially useful for increasing walkability in their communities. The specific tasks involve writing a report of findings from assessing existing walkability formulae in the academic literature, examining the data available in Florida, and devising a new formula for the state. A second task is to develop a statewide online map using the devised formula via an online map available to the general public.It is commonly recognized that physical activity plays an important role in human health. Walking is the first thing an infant wants to do and the last thing an elder wants to give up (Butcher 1999). Walking is one method to increase physical activity that is generally considered accessible to most without special training or equipment. Walking is “the forgotten transportation” (Cochoy et al 2015) as automobiles and other modes of transit have encroached upon humans’ simplest mobility. One out of two adults lives with a chronic disease that contributes to disability, premature death, and health care costs. Physical activity is recognized as one of the most important steps that people can take to improve their health. The Surgeon General has issued a Call to Action that addresses goals to make walking a national priority (United States, U.S. Department of Health and Human Services, Surgeon General 2015).While the scope of this project is limited to assessing walkability and developing tangible statewide maps, it is hoped by the Department that the maps produced here would be of value to local planners for increasing walk motivation in their communities. The Department would like for local officials to be able to use this information to design environmental changes such as lighting, sidewalks, or greenways to increase walking motivation. However, this project initially serves as a gathering of baseline statewide data considered to be related to walkability. This research could be extended in the future to include more detailed information through localized Geographic Information Systems (GIS) data and perceptions gathered from local walkers. The combination of these datasets holds promise for fully understanding the environmental details and how areas are viewed by the community. Combining quantifiable GIS data with audits of the pedestrian experience help ensure a more realistic view of neighborhoods.
TThe academic fields of transportation, urban design and public health each identify differing explanations as to why people walk and suggest different characteristics to affect one’s choice to walk. Reviewing academic journals and charting the data used for studies revealed a finite set of data inputs in spite of the diverging theories on walking motivation. Examination into the data available for Florida revealed several commonly used quantifiable data inputs that are readily available. Road compactness, population estimates, proximity to destinations, and presence of parks and trails are available statewide at favorable resolutions. Much data affecting urban design and pedestrian aesthetics such as sidewalk data, lighting, and cleanliness are not available at a state level. Further, elements such as visual design, human scale, unblocked vision, and perceived safety-- also not available at a state level-- are nebulous as these characteristics could be considered subjective measures. This dataset seeks to accommodate both transportation and recreation walking motivations. The final results are presented visually as a composite based upon multiple criteria at a 1-kilometer grid cell scale. Multiple attributes are used to convey information about the various input data so that users can understand the positive and negative factors in an area instead of a single metric. An area’s score can be assessed from multiple perspectives, thus revealing the reason(s) why an area might have received a particular score. While much local and subjective data cannot be included in this study, it is hoped that the results of this research could be helpful to local planners and designers looking to increase walking motivation in their communities. An interactive map using this data is located at: http://hermes.freac.fsu.edu/che/walk/ A report on this creation of this dataset is located at: https://hermes.freac.fsu.edu/che/walk/FDOH_Walkability_Measures_for_Florida.pdf
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31.008478
24.527481
None
Walkability
ISO 19115 Topic Categories
health
planningCadastre
None
Attribution 4.0 International (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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To cite this data:
FREAC (2018). Florida Walkability Measures, Florida Resources and Environmental Analysis Center [GIS shapefile]. Project funded
by the Florida Department of Health through a grant from the Center for Disease Control.
Florida Resources and Environmental Analysis Center (FREAC)
Stephen Hodge
Principle Investigator
shodge@fsu.edu
Florida Resources and Environmental Analysis Center (FREAC)
Georgianna Strode
GIS Researcher
gstrode@fsu.edu
This project was commissioned by the Florida Department of Health, Division of Community Health Promotion, Bureau
of Chronic Disease Prevention. The walkability measures data is a partial fulfillment of Contract #COHR9, June 2018.
The data was developed by Georgianna Strode, Beverly Renard, and Philip Griffith of the Florida Resources and Environmental Analysis Center (FREAC), Florida State University, with advisement from Christopher Coutts of the Urban and Regional Planning Department, Florida State University.
Microsoft Windows 7 Version 6.1 (Build 7601) Service Pack 1; Esri ArcGIS 10.6.0.8321
Vector
GT-polygon composed of chains
98189
NAD83(2011) Florida GDL Albers
coordinate pair
0.000000006714873101998366
0.000000006714873101998366
meter
D WGS 1984
WGS 1984
6378137.0
298.257223563
Walkability_Data
FID
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
objectid
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
dest_count
Count of number of destinations located within the 1-km grid. Data is from NAVTEQ April 2018. Destinations that are not likely to be walked to were not counted. See full report for more information.
fin_total
Count of number of financial institutions located within the 1-km grid. Data is from NAVTEQ April 2018. See full report for more information.
ent_total
Count of number of destinations with entertainment value located within the 1-km grid. Data is from NAVTEQ April 2018. See full report for more information.
edu_total
Count of number of destinations that are educational in nature located within the 1-km grid. Data is from NAVTEQ April 2018. See full report for more information.
com_total
Count of number of destinations that serve a communication purpose located within the 1-km grid. Data is from NAVTEQ April 2018. See full report for more information.
shop_total
Count of number of shopping destinations located within the 1-km grid. Data is from NAVTEQ April 2018. See full report for more information.
rest_total
Count of number of restaurants located within the 1-km grid. Data is from NAVTEQ April 2018. See full report for more information.
park_perc
Percentage of area recognized as parks or trails. Note that the full area of park is counted even if all of it may not be walkable.
pop_count
Count of number of persons residing within the 1-km grid. Data is dasymetrically calculated using 2010 census data, group quarters data, and Florida Department of Revenue property tax information according to method by Strode,Mesev,Maantay 2018. See full report for more information.
z_pop
Z-score of the pop_count (count of total population)attribute. Z-scores are expressed in terms of standard deviations from their means. Z-scores have a distribution with a mean of 0 and a standard deviation of 1. The standard score is simply the score, minus the mean score, divided by the standard deviation.
z_inter
Z-score of the inter_count (count of road intersections)attribute. Z-scores are expressed in terms of standard deviations from their means. Z-scores have a distribution with a mean of 0 and a standard deviation of 1. The standard score is simply the score, minus the mean score, divided by the standard deviation.
z_dest
Z-score of the dest_count (count of destinations)attribute. Z-scores are expressed in terms of standard deviations from their means. Z-scores have a distribution with a mean of 0 and a standard deviation of 1. The standard score is simply the score, minus the mean score, divided by the standard deviation.
z_park
Z-score of the park_perc (percent occupied by parks/trails)attribute. Z-scores are expressed in terms of standard deviations from their means. Z-scores have a distribution with a mean of 0 and a standard deviation of 1. The standard score is simply the score, minus the mean score, divided by the standard deviation.
walk_index
The walk index was calculated by summing the z-scores of intersections, destinations, parks / trail, and population. This index is categorized into the "walk_desc" field.
shape
Feature geometry.
Esri
Coordinates defining the features.
inter_coun
Count of number of road intersections located within the 1-km grid. Data is from NAVTEQ April 2018. See full report for more information.
walk_desc
The categories were determined by classifying the field "walk_index" using a 5-class Jenks Natural Breaks classification system. The Description field was assigned descriptions of "High," "Above Average," "Average," "Below Average," and "Low."
st_area_sh
st_length_
20181207
FGDC Content Standard for Digital Geospatial Metadata
FGDC-STD-001-1998
local time