An effective and efficient approach for manually improving geocoded data

Table 3 Overall and per-dataset match-type percentages and counts, before and after corrections

Match Type	Overall bef. – %: n aft. – %: n	Hospitals bef. – %: n aft. – %: n	Radiation bef. – %: n aft. – %: n	PD bef. – %: n aft. – %: n	Prostate Res. bef. – %: n aft. – %: n	Prostate Occ. bef. – %: n aft. – %: n
Un-matched	20.7: 4,618	0: 0	1: 21	18.4: 3,219	46.6: 905	100: 473
	5: 1,111	0: 0	0.1: 1	2.6: 458	28.3: 551	21.4: 101
Country	0: 0	0: 0	0: 0	0: 0	0: 0	0: 0
	1.5: 337	0: 0	0: 0	1.7: 288	2.3: 44	1.1: 5
State	0: 0	0: 0	0: 0	0: 0	0: 0	0: 0
	2: 462	0: 0	0: 0	2.2: 389	2.5: 49	5.1: 24
County	0.3: 71	0: 0	0: 0	0.3: 47	1.2: 24	0: 0
	2: 446	0: 0	0: 0	2.2: 384	2.9: 57	1.1: 5
MCD	1.7: 381	0: 0	0.3: 5	1.9: 333	2.2: 43	0: 0
	0: 2	0: 0	0: 0	0: 2	0: 0	0: 0
City	49.1: 10,959	4.8: 20	21.2: 426	55.9: 9,763	38.6: 750	0: 0
	28.2: 6,284	0: 0	3.5: 71	31.4: 5,489	25.2: 490	49.5: 234
Zip	4.6: 1,031	7.4: 31	31.7: 638	2.1: 362	0: 0	0: 0
	0.9: 200	0: 0	1.7: 34	0.9: 159	0.3: 6	0.2: 10
Street centroid	0: 0	0: 0	0: 0	0: 0	0: 0	0: 0
	10.8: 2,406	0: 0	1.5: 30	12.1: 2,106	13.4: 261	1.9: 9
Intersection	0: 0	0: 0	0: 0	0: 0	0: 0	0: 0
	9.8: 2,188	0: 0	0.1: 1	12: 2,097	4.3: 83	1.5: 7
Address range	23.2: 5,149	83.5: 349	41.3: 831	21.5: 3,747	11.4: 222	0: 0
	29.6: 6,606	13.4: 56	1.9: 39	34.5: 6,032	20.3: 395	17.8: 84
Nearest parcel	.5: 108	4.3: 18	4.5: 90	0: 0	0: 0	0: 0
	0: 7	0.7: 3	0: 0	0: 2	0.1: 2	0: 0
Exact parcel	0: 0	0: 0	0: 0	0: 0	0: 0	0: 0
	0: 7	0: 0	0: 0	0: 7	0: 0	0: 0
Building centroid	0: 0	0: 0	0: 0	0: 0	0: 0	0: 0
	10.13: 2,261	85.9: 359	91.3: 1,835	0.3: 58	0.3: 6	0.6: 3

bef. = before processing percentages and counts; aft. = after processing percentages and counts

ISSN: 1476-072X