Public space lighting (PSL) is indispensable after the natural dark. However, little is known about how much PSL people actually need to feel sufficiently safe in different real-world urban settings. The present study attempts to answer this question by employing a novel real-time interactive approach, according to which, observers use a specially-designed mobile phone application to assess and report the perceived attributes of street lighting and the feeling of safety (FoS) it generates. To validate the proposed approach, a systematic survey was conducted in three cities in Israel—Tel Aviv-Yafo and Haifa, which lie on the Mediterranean coast, and Be’er Sheba, which lies inland. Additionally, instrumental PSL measurements were performed at the same locations. As the study reveals, the necessary level of illumination required by urban residents to feel safe differs by city and is significantly higher in Be’er Sheba, other factors held equal, in compare to Haifa and Tel Aviv-Yafo. This difference may be attributed to stronger daylight that the residents of the desert city of Be’er Sheba are accustomed to, and, therefore, may prefer stronger nighttime illumination. The difference could also be related to the relatively low socio-economic status and somewhat higher crime rates in the latter city. Findings also show a significant and positive association between FoS and instrumentally measured PSL levels, although this association exhibits diminishing returns. To the best of our knowledge, the present study is the first to use an interactive location- and time-based mobile phone technology, which can potentially provide more accurate and reliable assessments, compared to traditional “pen and paper” survey techniques.

Large metropolitan areas often exhibit multiple morbidity hotspots. However, the identification of specific health hazards, associated with the observed morbidity patterns, is not always straightforward. In this study, we suggest an empirical approach to the identification of specific health hazards, which have the highest probability of association with the observed morbidity patterns. The morbidity effect of a particular health hazard is expected to weaken with distance. To account for this effect, we estimate distance decay gradients for alternative locations and then rank these locations based on the strength of association between the observed morbidity and wind-direction weighted proximities to these locations. To validate this approach, we use both theoretical examples and a case study of the Greater Haifa Metropolitan Area (GHMA) in Israel, which is characterized by multiple health hazards. In our theoretical examples, the proposed approach helped to identify correctly the predefined locations of health hazards, while in the real-world case study, the main health hazard was identified as a spot in the industrial zone, which hosts several petrochemical facilities. The proposed approach does not require extensive input information and can be used as a preliminary risk assessment tool in a wide range of environmental settings, helping to identify potential environmental risk factors behind the observed population morbidity patterns.