| 000 | 02341nam a2200301 4500 | ||
|---|---|---|---|
| 999 |
_c11670 _d11670 |
||
| 003 | OSt | ||
| 005 | 20210517170256.0 | ||
| 007 | cr aa aaaaa | ||
| 008 | 210517b ||||| |||| 00| 0 eng d | ||
| 100 |
_aWilliams, Sarah _933892 |
||
| 245 | _aEvaluating sensors for the measurement of public life: A future in image processing | ||
| 260 |
_bSage, _c2019. |
||
| 300 | _aVol 46, Issue 8, 2019 (1534-1548 p.) | ||
| 520 | _aWilliam Whyte, one of the most well-known urban planners, documented hundreds of hours of street life using videos, cameras, and interviews to develop social and physical policy recommendations for cities. Since then, studies of public life have primarily depended on human observation for data collection. Our research sets out to test whether Do-it-Yourself sensor technologies can automate this data collection process. To answer this question, our team embedded sensors in moveable benches and evaluated their performance according to the Gehl Method, a popular guideline that measures public life. During three field tests, we gathered information on public life via several sensors including image capture, location tracking, weight measurement, and other environmental sensing techniques. Ultimately, we determined that analysis derived from image processing was the most effective method for measuring public life. Our research demonstrates that it is possible to use sensors to automate the measurement of public life and highlights the value and precision of using video footage for collecting these data. Since image processing algorithms have become more accessible and can be applied to Do-it-Yourself projects, future work can build on this research to develop open access image processing tools to evaluate and advocate for urban design strategies. | ||
| 650 |
_aDo-it-Yourself, _946066 |
||
| 650 |
_a image analytics _946067 |
||
| 650 |
_asensors, _946068 |
||
| 650 |
_asmart cities, _946069 |
||
| 650 |
_aurban design _946070 |
||
| 700 |
_aAhn, Chaewon _946071 |
||
| 700 |
_aGunc,Hayrettin _946072 |
||
| 700 |
_aOzgirin, Ege _946073 |
||
| 700 |
_aPearce, Michael _931951 |
||
| 700 |
_aXiong, Zhekun _946050 |
||
| 773 | 0 |
_011590 _915512 _dSage 2019. _t Environment and Planning B: Urban Analytics and City Science |
|
| 856 | _uhttps://doi.org/10.1177/2399808319852636 | ||
| 942 |
_2ddc _cART |
||