IEEE Trans Vis Comput Graph - Forecasting Hotspots - A Predictive Analytics Approach.

Tópicos

{ model(2341) predict(2261) use(1141) }
{ spatial(1525) area(1432) region(1030) }
{ model(3404) distribut(989) bayesian(671) }
{ data(1737) use(1416) pattern(1282) }
{ extract(1171) text(1153) clinic(932) }
{ structur(1116) can(940) graph(676) }
{ chang(1828) time(1643) increas(1301) }
{ research(1085) discuss(1038) issu(1018) }
{ visual(1396) interact(850) tool(830) }
{ medic(1828) order(1363) alert(1069) }
{ patient(1821) servic(1111) care(1106) }
{ inform(2794) health(2639) internet(1427) }
{ system(1976) rule(880) can(841) }
{ measur(2081) correl(1212) valu(896) }
{ patient(2315) diseas(1263) diabet(1191) }
{ control(1307) perform(991) simul(935) }
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{ data(2317) use(1299) case(1017) }
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{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
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{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ high(1669) rate(1365) level(1280) }
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{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }

Resumo

In spatiotemporal data, analysts are searching for regions of space and time with unusually high incidences of events (hotspots). In the cases that hotspots are found, analysts would like to predict how these regions may grow in order to plan resource allocation and preventative measures. Furthermore, analysts would also like to predict where future hotspots may occur. To facilitate such forecasting, we have created a predictive visual analytics toolkit that provides analysts with linked spatiotemporal and statistical analytic views. Our system models spatiotemporal events through the combination of kernel density estimation for event distribution and seasonal trend decomposition by loess smoothing for temporal predictions. We provide analysts with estimates of error in our modeling, along with spatial and temporal alerts to indicate the occurrence of statistically significant hotspots. Spatial data is distributed based on a modeling of previous event locations, thereby maintaining a temporal coherence with past events. Such tools allow analysts to perform real-time hypothesis testing, plan intervention strategies, and allocate resources to correspond to perceived threats.

Resumo Limpo

spatiotempor data analyst search region space time unusu high incid event hotspot case hotspot found analyst like predict region may grow order plan resourc alloc prevent measur furthermor analyst also like predict futur hotspot may occur facilit forecast creat predict visual analyt toolkit provid analyst link spatiotempor statist analyt view system model spatiotempor event combin kernel densiti estim event distribut season trend decomposit loess smooth tempor predict provid analyst estim error model along spatial tempor alert indic occurr statist signific hotspot spatial data distribut base model previous event locat therebi maintain tempor coher past event tool allow analyst perform realtim hypothesi test plan intervent strategi alloc resourc correspond perceiv threat

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