Measuring Local Individual Housing Returns from a Large Transaction Database

2010 ◽  
Author(s):  
Stéphane M. Gregoir ◽  
Tristan‐Pierre Maury
Keyword(s):  
Individual Housing ◽  
Transaction Database ◽  
Housing Returns

Measuring Local Individual Housing Returns from a Large Transaction Database

2012 ◽  
pp. 93 ◽  
Author(s):  
Stéphane Gregoir ◽  
Mathieu Hutin ◽  
Tristan-Pierre Maury ◽  
Geneviève Prandi
Keyword(s):  
Individual Housing ◽  
Transaction Database ◽  
Housing Returns

Impact of individual housing on experimental behavior of male mice

2008 ◽  
Author(s):  
S. S. Arndt ◽  
M. C. Laarakker ◽  
H. A. van Lith ◽  
F. J. van der Staay ◽  
F. Ohl
Keyword(s):  
Male Mice ◽  
Individual Housing ◽  
Experimental Behavior

Do residents care about urban dumps? Evidence from individual housing transaction data

2021 ◽  
Vol 109 ◽  
pp. 105604
Author(s):  
Yingdan Mei ◽  
Jixiang Qiu ◽  
Jialu Wu ◽  
Lina Meng
Keyword(s):  
Transaction Data ◽  
Individual Housing

Individual Housing From Rearing Modifies the Performance of Young Rats on the Elevated Plus-Maze Apparatus

1996 ◽  
Vol 60 (6) ◽  
pp. 1391-1396 ◽  
Author(s):  
NELCI LOPES DA SILVA ◽  
VANIA MARIA MORAES FERREIRA ◽  
ANTONIO DE PADUA CAROBREZ ◽  
GINA STRUFFALDI MORATO
Keyword(s):  
Elevated Plus Maze ◽  
Individual Housing ◽  
Young Rats ◽  
Plus Maze

scholarly journals An Individual Housing-Based Socioeconomic Status Measure Predicts Advance Care Planning and Nursing Home Utilization

2018 ◽  
Vol 36 (5) ◽  
pp. 362-369 ◽  
Author(s):  
Amelia Barwise ◽  
Young J. Juhn ◽  
Chung-Il Wi ◽  
Paul Novotny ◽  
Carolina Jaramillo ◽  
...  
Keyword(s):  
Decision Making ◽  
Socioeconomic Status ◽  
End Of Life ◽  
Advance Directives ◽  
End Of Life Care ◽  
Composite Index ◽  
Life Care ◽  
Individual Housing ◽  
Lower Likelihood ◽  
Multiple Variables

Background: Socioeconomic status (SES) is an important determinant of disparities in health care and may play a role in end-of-life care and decision-making. The SES is difficult to retrospectively abstract from current electronic medical records and data sets. Objective: Using a validated SES measuring tool derived from home address, the HOUsing-based SocioEconomic Status index, termed HOUSES we wanted to determine whether SES is associated with differences in end-of-life care and decision-making. Design/Setting/Participants: This cross-sectional study utilized a cohort of Olmsted County adult residents admitted to 7 intensive care units (ICUs) at Mayo Rochester between June 1, 2011, and May 31, 2014. Measurements: Multiple variables that reflect decision-making and care at end of life and during critical illness were evaluated, including presence of advance directives and discharge disposition. The SES was measured by individual housing-based SES index (HOUSES index; a composite index derived from real property as a standardized z-score) at the date of admission to the ICU which was then divided into 4 quartiles. The greater HOUSES, the higher SES, outcomes were adjusted for age, 24-hour Acute Physiology and Chronic Health Evaluation III score, sex, race/ethnicity, and insurance. Results: Among the eligible 4134 participants, the addresses of 3393 (82%) were successfully geocoded and formulated into HOUSES. The adjusted odds ratios comparing HOUSES 1 versus 2, 3, and 4 demonstrated lower likelihood of advance directives −0.77(95% CI: 0.63-0.93) and lower likelihood of discharge to home −0.60(95% CI: 1.0.5-0.72). Conclusion: Lower SES, derived from a composite index of housing attributes, was associated with lower rates of advance directives and lower likelihood of discharge to home.

Fast Algorithms for Temporal Association Rules in a Large Database

2005 ◽  
Vol 277-279 ◽  
pp. 287-292 ◽  
Author(s):  
Lu Na Byon ◽  
Jeong Hye Han
Keyword(s):  
Electronic Commerce ◽  
Association Rules ◽  
Association Rule ◽  
Temporal Association ◽  
Large Database ◽  
Running Time ◽  
Smoothing Filter ◽  
Personalized Services ◽  
Temporal Association Rule ◽  
Transaction Database

As electronic commerce progresses, temporal association rules are developed by time to offer personalized services for customer’s interests. In this article, we propose a temporal association rule and its discovering algorithm with exponential smoothing filter in a large transaction database. Through experimental results, we confirmed that this is more precise and consumes a shorter running time than existing temporal association rules.

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