j40-cejst-2/data/data-pipeline/data_pipeline/tests/sources/ejscreen/data/output.csv at f70f30d610df2479b8a06cc064343acadfc490c2

mirror of https://github.com/DOI-DO/j40-cejst-2.git synced 2025-02-25 11:04:19 -08:00

Add tests for all non-census sources (#1899 )

* Refactor CDC life-expectancy (1554)

* Update to new tract list (#1554)

* Adjust for tests (#1848)

* Add tests for cdc_places (#1848)

* Add EJScreen tests (#1848)

* Add tests for HUD housing (#1848)

* Add tests for GeoCorr (#1848)

* Add persistent poverty tests (#1848)

* Update for sources without zips, for new validation (#1848)

* Update tests for new multi-CSV but (#1848)

Lucas updated the CDC life expectancy data to handle a bug where two
states are missing from the US Overall download. Since virtually none of
our other ETL classes download multiple CSVs directly like this, it
required a pretty invasive new mocking strategy.

* Add basic tests for nature deprived (#1848)

* Add wildfire tests (#1848)

* Add flood risk tests (#1848)

* Add DOT travel tests (#1848)

* Add historic redlining tests (#1848)

* Add tests for ME and WI (#1848)

* Update now that validation exists (#1848)

* Adjust for validation (#1848)

* Add health insurance back to cdc places (#1848)

Ooops

* Update tests with new field (#1848)

* Test for blank tract removal (#1848)

* Add tracts for clipping behavior

* Test clipping and zfill behavior (#1848)

* Fix bad test assumption (#1848)

* Simplify class, add test for tract padding (#1848)

* Fix percentage inversion, update tests (#1848)

Looking through the transformations, I noticed that we were subtracting
a percentage that is usually between 0-100 from 1 instead of 100, and so
were endind up with some surprising results. Confirmed with lucasmbrown-usds

* Add note about first street data (#1848)

2022-09-19 15:17:00 -04:00

3.4 KiB

Raw Blame History

1	GEOID10_TRACT	Total population	Air toxics cancer risk	Respiratory hazard index	Diesel particulate matter exposure	PM2.5 in the air	Ozone	Traffic proximity and volume	Proximity to Risk Management Plan (RMP) facilities	Proximity to hazardous waste sites	Proximity to NPL sites	Wastewater discharge	Percent of households in linguistic isolation	Poverty (Less than 200% of federal poverty line)	Individuals over 64 years old	Individuals under 5 years old	Percent pre-1960s housing (lead paint indicator)	Leaky underground storage tanks
2	06027000800	3054	20.0000000000	0.2000000000	0.0162608457	5.9332945205	59.8143830065	134.3731709435	0.0161739005	0.0231458734	0.0088169702	0.0000000476	0.0943661972	0.4021269525	0.2445972495	0.0422396857	0.3691340106	0.0271801764
3	06061021322	20899	30.0000000000	0.5000000000	0.1849562857	12.1102756164	52.7832287582	12.5173455346	0.4515663958	0.2027045525	0.0687928975	0.2667203153	0.0343563903	0.1859250743	0.1406287382	0.0683764773	0.0334588644	0.0258826940
4	06069000802	3049	20.0000000000	0.2000000000	0.0375346206	7.4113546849	47.0434058824	15.7944927934	0.0811927061	0.1674220356	0.0396183204		0.0324607330	0.2453201970	0.1534929485	0.0787143326	0.3485254692	0.0102735941
5	15001021010	8606	10.0000000000	0.1000000000	0.0067389217			0.1074143214	0.0478749209	0.0931096253	0.0027318608		0.0109090909	0.5159562078	0.1992795724	0.0366023704	0.0112496943	0.0259838494
6	15001021101	3054	10.0000000000	0.1000000000	0.0033713587			1.7167679255	0.2484740667	0.2746856427	0.0025910486		0.0194426442	0.4755657593	0.2976424361	0.0301244270	0.0168539326	0.0375389154
7	15001021402	3778	10.0000000000	0.1000000000	0.0131608945			635.9981128640	0.0225482603	0.6278707343	0.0033357209		0.0407569141	0.1877496671	0.2469560614	0.0751720487	0.1743524953	0.5088713177
8	15001021800	5998	10.0000000000	0.1000000000	0.0049503455			0.0743045071	0.0402733327	0.0410968274	0.0038298946		0.0359848485	0.2698678267	0.2352450817	0.0586862287	0.1676168757	0.1071290552
9	15003010201	4936	10.0000000000	0.1000000000	0.0171119880			1493.8870892160	0.0548137804	0.4080845621	0.0694550700		0.0340041638	0.2999166319	0.1318881686	0.0964343598	0.2131062951	0.0995447326
10	15007040603	2984	10.0000000000	0.1000000000	0.0225796264			255.5966484444	0.1042895043	0.5200441984	0.0065810172		0.0311909263	0.2676292814	0.2533512064	0.0563002681	0.0935077519	0.1610354485
11	15007040604	3529	10.0000000000	0.1000000000	0.0297040750			464.0468169721	0.1282189641	0.3810520320	0.0064334940		0.0353833193	0.3687102371	0.1790875602	0.0943610088	0.1981538462	0.2277699060
12	15007040700	9552	10.0000000000	0.1000000000	0.0120486502			829.6297843840	0.2776903565	0.5315584393	0.0062317499		0.0328151986	0.2079176730	0.1920016750	0.0808207705	0.1049120679	0.8605507426
13	15009030100	1405	10.0000000000	0.1000000000	0.0026846006				0.0398066625	0.0329594792	0.0046765532		0.0000000000	0.2911208151	0.2434163701	0.0882562278	0.2135678392	0.0973247551
14	15009030201	2340	10.0000000000	0.1000000000	0.0063521816			7.0868595222	0.1292001112	0.0908033666	0.0053511202		0.0000000000	0.2677266867	0.2367521368	0.0641025641	0.0928229665	0.0098923140
15	15009030402	8562	10.0000000000	0.1000000000	0.0153866969			233.6880574427	0.6633705951	0.5914191729	0.0055146115		0.0122641509	0.1792805419	0.1810324690	0.0463676711	0.0760149726	0.4432670413
16	15009030800	7879	10.0000000000	0.1000000000	0.0169064550			575.9991000531	1.0347888110	0.5999348163	0.0061499864	0.0008675195	0.0013422819	0.1386100877	0.1303464907	0.0753902780	0.1220556745	0.0263640121

3.4 KiB Raw Blame History

3.4 KiB

Raw Blame History