Using ambient WiFi signals to find occupied and vacant houses in local communities

  • Shin’ichi KonomiEmail author
  • Tomoyo Sasao
  • Simo Hosio
  • Kaoru Sezaki
Original Research


In many countries, the population is either declining or rapidly concentrating in big cities, which causes problems in the form of vacant houses. It is often challenging to keep track of the locations and the conditions of vacant houses, and for example in Japan, costly manual field studies are employed to map the occupancy situation. In this paper, we discuss a technique to infer the locations of occupied and vacant houses based on ambient WiFi signals. Our technique collects Received Signal Strength Indicator (RSSI) data based on opportunistic smartphone sensing, constructs hybrid networks of WiFi access points, and analyzes their geospatial patterns based on statistical shape modeling. In situ experiments in two residential neighborhoods show that the proposed technique can successfully detect occupied houses and substantially outperform a simple triangulation-based method in one of the neighborhoods. We also argue that the proposed technique can significantly reduce the cost of field surveys to find vacant houses as the number of potential houses to be inspected decreases.


Ambient WiFi signals Vacant houses Civic computing Localization 



We thank Ryohei Suzuki, the members of the Urban Housing Policy Division of Kashiwa City, and the members of the local communities for providing valuable feedback at different stages of this project.


This work was supported by JSPS KAKENHI Grant numbers JP17KTT0154, JP17K00117, JST CREST Grant number JPMJCR1411, and MLIT Pioneering Countermeasure Models for Vacant Houses Project, Japan, and Academy of Finland grant 286386-CPDSS.


  1. Accordino J, Johnson GT (2002) Addressing the vacant and abandoned property problem. J Urban Affairs 22(3):301–315. CrossRefGoogle Scholar
  2. ArchiSnapper (2013) ArchiSnapper. Accessed 11 Jun 2016
  3. Baddeley A, Turner R, Rubak E (2014) Spatstat analysing spatial point patterns. Accessed 30 Mar 2018
  4. Bahl P, Padmanabhan VN (2000) RADAR: an in-building RF-based user location and tracking system. In: Proceedings of the 19th annual joint conference of the IEEE and communications societies (INFOCOM 2000), IEEE, pp 775–784.
  5. Burke J, Estrin D, Hansen M, Parker A, Ramanathan N, Reddy S, Srivastava MB (2006) Participatory sensing. In: Workshop on world-sensor-web (WSW06): mobile device centric sensor networks and applications, pp 117–134. Accessed 1 Oct 2017
  6. Chi G, Liu Y, Wu H (2014) “Ghost Cities” analysis based on positioning data in China. arXiv:1510.08505 (cs.SI)
  7. Garvin E, Branas C, Keddem S, Sellman J, Cannuscio C (2013) More than just an eyesore: local insights and solutions on vacant land and urban health. J Urban Health 90(3):412426. CrossRefGoogle Scholar
  8. Ji M, Kim J, Cho Y, Lee Y, Park S (2013) A novel Wi-Fi AP localization method using Monte Carlo path-loss model fitting simulation. In: Proceedings of the 24th IEEE international symposium on personal, indoor and mobile radio communications (PIMRC 2013), IEEE, pp 3487–3491.
  9. Konomi S, Shoji K, Ohno W (2013) Rapid development of civic computing services: opportunities and challenges. In: Streitz N, Stephanidis C (eds) Distributed, ambient, and pervasive interactions, proceedings of the 1st international conference on distributed, ambient, and pervasive interactions (DAPI 2013), held as part of HCI international 2013, LNCS 8028, Springer, pp 309–315.
  10. Konomi S, Sasao T, Hosio S, Sezaki K (2017) Exploring the use of ambient WiFi signals to find vacant houses. In: Braun A, Wichert R, Mana A (eds) Ambient intelligence, proceedings of the 13th European conference on ambient intelligence (AmI 2017), LNCS 10217, Springer, pp. 130–135.
  11. Koo J, Cha H (2012) Unsupervised locating of WiFi access points using smartphones. IEEE Trans Syst Man Cybern Part C Appl Rev 42(6):1341–1353. CrossRefGoogle Scholar
  12. Lane ND, Eisenman SB, Musolesi M, Miluzzo E, Campbell AT (2008) Urban sensing systems: opportunistic or participatory? In: Proceedings of the 9th workshop on mobile computing systems and applications (HotMobile’08), ACM, pp 11–16.
  13. LaMarca A, Chawathe Y, Consolvo S, Hightower J, Smith I, Scott J, Sohn T, Howard J, Hughes J, Potter F, Tabert J, Powledge P, Borriello G, Schilit B (2005) Place Lab: device positioning using radio beacons in the wild. In: Gellerson HW, Want R, Schmidt A (eds) Proceedings of the 3rd international conference on pervasive computing (PERVASIVE 2005), Springer, pp 116–133.
  14. McCoy C (2009) Mapping site for abandoned properties: visualizing the most current data on the web. ArcUser. Accessed 12 Jun 2016
  15. Nomura Research Institute (2016) News release, June 7, 2016. Accessed 1 Oct 2017 (in Japanese)
  16. Ripley BD (1976) The second-order analysis of stationary point processes. J Appl Probab 13:255–266. MathSciNetCrossRefzbMATHGoogle Scholar
  17. Sasao T, Konomi S, Suzuki R (2016) Supporting community-centric use and management of vacant houses: a crowdsourcing-based approach. In: Adjunct proceedings of the 2016 ACM conference on pervasive and ubiquitous computing (UbiComp’16)—international workshop on mobile and situated crowdsourcing (WMSC16), ACM, pp1454–1459.
  18. Sasao T, Konomi S, Kostakos V, Kuribayashi K, Goncalves J (2017) Community reminder: participatory contextual reminder environments for local communities. Int J Hum Comput Stud 102:41–53. CrossRefGoogle Scholar
  19. Streitz N (2017) Reconciling humans and technology: the role of ambient intelligence. In: Braun A, Wichert R, Mana A (eds) Proceedings of the 2017 European conference on ambient intelligence (AmI 2017), Springer, pp 1–16.
  20. Streitz N (2018) Beyond ’smart-only’ cities: redefining the ’smart-everything’ paradigm. J Amb Intell Hum Comput. (corresponding page numbers can potentially be added by the editors of the special issue.)
  21. The Institute for Tokyo Municipal Research (2016) Reports on the policies on vacant houses by local governments. Accessed 11 Jun 2016 (in Japanese)
  22. U.S. Department of Housing and Urban Development Office of Policy Development and Research (2014) Vacant and abandoned properties: turning liabilities into assets. In: Evidence matters. Accessed 12 Jun 2016
  23. Vacant Voices (2014) Vacant voices home. Accessed 11 Jun 2016
  24. Oksanen J (2015) Multivariate analysis of ecological communities in R: vegan tutorial. Accessed 1 Oct 2017
  25. WiGLE (2017) all the networks. found by everyone. Accessed 1 Oct 2017
  26. Wu D, Liu Q, Zhang Y, McCann J, Regan A, Venkatasubramanian N (2014) CrowdWiFi: efficient crowdsensing of roadside WiFi networks. In: Proceedings of the 15th International middleware conference (Middleware’14), ACM, pp 229–240.
  27. Yin L, Silverman RM (2015) Housing abandonment and demolition: exploring the use of micro-level and multi-year models. Int J Geo Inf 4(3):11841200. Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Arts and ScienceKyushu UniversityFukuokaJapan
  2. 2.Center for Community RevitalizationTokushima UniversityTokushimaJapan
  3. 3.Center for Ubiquitous ComputingThe University of OuluOuluFinland
  4. 4.Center for Spatial Information ScienceThe University of TokyoKashiwaJapan

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