Geografie 2024, 129, 1-13

https://doi.org/10.37040/geografie.2024.005

The relationship between foot traffic and commercial land prices

Changro LeeID

Kangwon National University, Department of Real Estate, Chuncheon, South Korea

Received May 2023
Accepted February 2024

References

1. ABDELNASSER, H., YOUSSEF, M., HARRAS, K.A. (2015): Wigest: A ubiquitous wifi-based gesture recognition system. In 2015 IEEE conference on computer communications (INFOCOM), IEEE, 1472–1480. <https://doi.org/10.1109/INFOCOM.2015.7218525>
2. ANSELIN, L. (2002): Under the hood issues in the specification and interpretation of spatial regression models. Agricultural economics, 27, 3, 247–267. <https://doi.org/10.1111/j.1574-0862.2002.tb00120.x>
3. AYODEJI, O.G., KUMAR, V. (2019): Social media analytics: a tool for the success of online retail industry. International Journal of Services Operations and Informatics, 10, 1, 79–95. <https://doi.org/10.1504/IJSOI.2019.100630>
4. BARTHOLOMEW, K., EWING, R. (2010): Hedonic Price Effects of Pedestrian-and Transit-Designed Development. Journal of Planning Literature. <https://doi.org/10.1177/0885412210386540>
5. BAYODE, T., POPOOLA, A., AKOGUN, O., SIEGMUND, A., MAGIDIMISHA-CHIPUNGU, H., IPINGBEMI, O. (2022): Spatial variability of COVID-19 and its risk factors in Nigeria: A spatial regression method. Applied Geography, 138, 102621. <https://doi.org/10.1016/j.apgeog.2021.102621>
6. CAPOZZA, D.R., HELSLEY, R.W. (1989): The fundamentals of land prices and urban growth. Journal of Urban Economics, 26, 3, 295–306. <https://doi.org/10.1016/0094-1190(89)90003-X>
7. CHAO, T., DU, R., GLUCK, J., MAIDASANI, H., WILLS, K., SHNEIDERMAN, B. (2013): C-flow: Visualizing foot traffic and profit data to make informative decisions. Technical report, University of Maryland. Online: http://duruofei.com/Dev/c-flow
8. CHEN, L., LU, Y., YE, Y., XIAO, Y., YANG, L. (2022): Examining the association between the built environment and pedestrian volume using street view images. Cities, 103734. <https://doi.org/10.1016/j.cities.2022.103734>
9. CORTRIGHT, J. (2009): Walking the walk: How walkability raises home values in US cities. Durham, NC: CEOs for Cities.
10. DOBLER, G., VANI, J., DAM, T.T.L. (2021): Patterns of urban foot traffic dynamics. Computers, Environment and Urban Systems, 89, 101674. <https://doi.org/10.1016/j.compenvurbsys.2021.101674>
11. DOLEGA, L., ROWE, F., BRANAGAN, E. (2021): Going digital? The impact of social media marketing on retail website traffic, orders and sales. Journal of Retailing and Consumer Services, 60, 102501. <https://doi.org/10.1016/j.jretconser.2021.102501>
12. DUBROCA-VOISIN, M., KABALAN, B., LEURENT, F. (2019): On pedestrian traffic management in railway stations: simulation needs and model assessment. Transportation research procedia, 37, 3–10. <https://doi.org/10.1016/j.trpro.2018.12.159>
13. FENG, C., FAY, S. (2022): An empirical investigation of forward-looking retailer performance using parking lot traffic data derived from satellite imagery. Journal of Retailing. <https://doi.org/10.1016/j.jretai.2022.03.004>
14. GERIKE, R., KOSZOWSKI, C., SCHRÖTER, B., BUEHLER, R., SCHEPERS, P., WEBER, J., JONES, P. (2021): Built environment determinants of pedestrian activities and their consideration in urban street design. Sustainability, 13, 16, 9362. <https://doi.org/10.3390/su13169362>
15. GUO, L., WANG, L., LIU, J., ZHOU, W. (2016): A survey on motion detection using WiFi signals. In 2016 12th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN), IEEE, 202–206. <https://doi.org/10.1109/MSN.2016.040>
16. HABAEBI, M., ROSLI, R., ISLAM, M.R. (2017): RSSI-based human presence detection system for energy saving automation. Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 5, 4, 339–350. <https://doi.org/10.11591/ijeei.v5i4.356>
17. HOSFORD, K., CLOUTIER, M.S., WINTERS, M. (2020): Observational study of pedestrian and cyclist interactions at intersections in Vancouver, BC and Montréal, QC. Transportation research record, 2674, 6, 410–419. <https://doi.org/10.1177/0361198120919407>
18. LAM, B.Y., CHAU, K.W. (2012): Explaining the variations in the pedestrian flow values of shopping centres. Facilities, 30, 3/4, 164–176. <https://doi.org/10.1108/02632771211202860>
19. LEE, K.M., JUNG, C.M. (2014): The effect of time period pedestrian volume on store location: focused on the Suwon’s retail stores and restaurants. Journal of the Architectural Institute of Korea, 30, 8, 47–55. <https://doi.org/10.5659/JAIK_PD.2014.30.8.47>
20. MA, Y., ZHOU, G., WANG, S. (2019): WiFi sensing with channel state information: A survey. ACM Computing Surveys (CSUR), 52, 3, 1–36. <https://doi.org/10.1145/3310194>
21. NING, H., YE, X., CHEN, Z., LIU, T., CAO, T. (2022): Sidewalk extraction using aerial and street view images. Environment and Planning B: Urban Analytics and City Science, 49, 1, 7–22. <https://doi.org/10.1177/2399808321995817>
22. PATRA, M., SALA, E., RAVISHANKAR, K.V.R. (2017): Evaluation of pedestrian flow characteristics across different facilities inside a railway station. Transportation research procedia, 25, 4763–4770. <https://doi.org/10.1016/j.trpro.2017.05.488>
23. PERDIKAKI, O., KESAVAN, S., SWAMINATHAN, J. M. (2012): Effect of traffic on sales and conversion rates of retail stores. Manufacturing & Service Operations Management, 14, 1, 145–162. <https://doi.org/10.1287/msom.1110.0356>
24. PHAM, L.T.N. (2023): Real estate prices in Hanoi pedestrian streets. In E3S Web of Conferences, EDP Sciences, 403, 01003. <https://doi.org/10.1051/e3sconf/202340301003>
25. PINEDA-RÍOS, W., GIRALDO, R., PORCU, E. (2019): Functional SAR models: With application to spatial econometrics. Spatial statistics, 29, 145–159. <https://doi.org/10.1016/j.spasta.2018.12.002>
26. PIVO, G., FISHER, J.D. (2011): The walkability premium in commercial real estate investments. Real estate economics, 39, 2, 185–219. <https://doi.org/10.1111/j.1540-6229.2010.00296.x>
27. QIAN, F., ZHANG, Q., ZHANG, X. (2023): The Effects of Agglomeration on Customer Traffic & Commercial Real Estate Values: Evidence from Grocery Store Openings.
28. RAUTERKUS, S.Y., MILLER, N. (2011): Residential land values and walkability. Journal of Sustainable Real Estate, 3, 1, 23–43. <https://doi.org/10.1080/10835547.2011.12091815>
29. SEVTSUK, A., KALVO, R. (2022): Predicting pedestrian flow along city streets: A comparison of route choice estimation approaches in downtown San Francisco. International Journal of Sustainable Transportation, 16, 3, 222–236. <https://doi.org/10.1080/15568318.2020.1858377>
30. SHIN, H.S., WOO, A. (2024): Analyzing the effects of walkable environments on nearby commercial property values based on deep learning approaches. Cities, 144, 104628. <https://doi.org/10.1016/j.cities.2023.104628>
31. SOHN, D.W., MOUDON, A.V., LEE, J. (2012): The economic value of walkable neighborhoods. Urban Design International, 17, 115–128. <https://doi.org/10.1057/udi.2012.1>
32. SUN, H., CHEN, J., FAN, M. (2021): Effect of Live Chat on Traffic‐to‐Sales Conversion: Evidence from an Online Marketplace. Production and Operations Management, 30, 5, 1201–1219. <https://doi.org/10.1111/poms.13320>
33. SUN, H., FAN, M., TAN, Y. (2020): An empirical analysis of seller advertising strategies in an online marketplace. Information Systems Research, 31, 1, 37–56. <https://doi.org/10.1287/isre.2019.0874>
34. TAN, Q., LING, X., CHEN, M., LU, H., WANG, P., LIU, W. (2019): Statistical analysis and prediction of regional bus passenger flows. International Journal of Modern Physics B, 33, 11, 1950094. <https://doi.org/10.1142/S0217979219501017>
35. TRASBERG, T., SOUNDARARAJ, B., CHESHIRE, J. (2021): Using Wi-Fi probe requests from mobile phones to quantify the impact of pedestrian flows on retail turnover. Computers, Environment and Urban Systems, 87, 101601. <https://doi.org/10.1016/j.compenvurbsys.2021.101601>
36. WANG, X., LIONO, J., MCINTOSH, W., SALIM, F. D. (2017): Predicting the city foot traffic with pedestrian sensor data. In Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, 1–10. <https://doi.org/10.1145/3144457.3152355>
37. WANG, Y., ZHANG, W., ZHANG, F., YIN, L., ZHANG, J., TIAN, C., JIANG, W. (2020): Analysis of subway passenger flow based on smart card data. In 2020 6th International Conference on Big Data Computing and Communications (BIGCOM), IEEE, 198–202. <https://doi.org/10.1109/BigCom51056.2020.00034>
38. WARD, M.D., GLEDITSCH, K.S. (2018): Spatial regression models, 155. Sage Publications. <https://doi.org/10.4135/9781071802588>
39. WASHINGTON, E. (2013): Role of walkability in driving home values. Leadership and Management in Engineering, 13, 3, 123–130. <https://doi.org/10.1061/(ASCE)LM.1943-5630.0000222>
front cover

ISSN 1212-0014 (Print) ISSN 2571-421X (Online)

Archive