Open mateoclarke opened 9 years ago
Then, step 2 is figuring out how to include the data from this layer onto the landing page map
Incorporating our park data into a format similar to that of the Accessibility Score website we saw in the Census demo would be one way to include the demographic data to our landing page.
http://website-mnhomefinder.rhcloud.com/accessibilityscore/evaluation/explore.action
Yeah, I really liked those radar (or spider) charts too. Digging into the source of their project, it looks like they used Chart.js. Looks like a pretty easy library to plug in, or we could try with d3 if we need something more custom. (Here's a d3 radar chart example)
@kyoder, I've started digging into the census tract data you uploaded. Thanks for merging all this Census Data into the tract objects.
Can you share which of these properties are most important and what their names mean? And in what units they are measured?
And it looks like you already did some calculations to come up with some scores for density, poverty, parks, youth, seniors, and health. What did you do to arrive at these numbers and how should we interpret them? Is it a ranking of by all 202 census tracts?
Finally, if we want to follow the Accessibility Score app model, we would need to get our scores on a 0-100 scale.
Below is a sample of data from one Census Tract...
properties | values |
---|---|
OBJECTID | 175 |
STATEFP | 48 |
COUNTYFP | 453 |
TRACTCE | 002212 |
GEOID | 48453002212 |
NAME | 22.12 |
totalCivil | 563 |
uninsuredN | 90 |
totalHousi | 161 |
occupiedHo | 161 |
percentOcc | 100 |
vacantHous | 0 |
percentVac | 0 |
ownerOccup | 144 |
percentOwn | 89.4 |
renterOccu | 17 |
percentRen | 10.6 |
totalHouse | 161 |
totalFamil | 145 |
medIncHous | 50313 |
medIncFam | 55703 |
moe_total | 157 |
est__inHou | 181 |
est_under3 | 30 |
est_3_4 | 10 |
est_5 | 31 |
est_6_8 | 3 |
est_9_11 | 38 |
est_12_14 | 21 |
est_15_17 | 48 |
estgroup | 0 |
Shape_Leng | 0.21945908268 |
Shape_Area | 0.0028237841 |
popTotal | 563 |
popDensity | 48.4335228399 |
tractArea | 7439.47536388 |
uninsuredP | 16 |
poverty | 0 |
ageTotal | 563 |
age65total | 28 |
over65 | 0.04973357016 |
un18Total | 181 |
under18 | 0.3214920071 |
densitySCR | 0 |
healthSCR | 4 |
povertySCR | 0 |
seniorSCR | 3 |
youthSCR | 9 |
parkSCR | 16 |
We probably need to revisit the logic. I'm sorry the field names are so messy.
Important fields:
STATEFP = state FIP code COUNTYFP = county FIP code TRACTCE = census tract id GEOID = unique census tract id that is a combination of STATEFP, COUNTYFP, and TRACTCE NAME = name of census Tract
popTotal = population total popDensity = population density (calculated from popTotal/tractArea) used for score tractArea = area of the census tract calculated in square miles uninsuredP = percent uninsured used for score poverty = percent below poverty used for score ageTotal = total from the census table, Sex and Age age65total = number from the census table, Sex and Age, that are over 65 over65 = percent over 65 (calculated age65total/ageTotal) used for score un18Total = total number under 18 from census table, Population under 18 under18 = percent under 18 (calculated un18Total/popTotal) *used for score densitySCR = population density score healthSCR = health insurance coverage score povertySCR = below poverty score seniorSCR = population over 65 score youthSCR = population under 18 score parkSCR = SUM of all other score fields
See this Park Score breakdown for more details on the classifications and source data https://docs.google.com/spreadsheets/d/1YzCqHEBSD9saystcNoUkGIMcaqOKt2oZsJSVtIVS_Y8/edit?usp=sharing
Discussed tonight at open austin wtih @wilsaj and john
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