Sh opping 0.4 Dining and other activitie 0.3 0.2 0. 0.1 0 0.2 0.4 0.6 0.8 18 Activiness Time in a day (hour) I mme in a day (hour) (a)Spatial closeness difference. (b)Temporal closeness difference Fig.5.Distribution of activeness score computed from each AP during staying segments when people are shopping or dinning. Fig.6.Illustration of social relationships classification derived from temporal information of the places.Different from categorizing daily and spatial closeness based on one day's data. places based on their generic nature [27].our daily routine- B.Activity Feature Extraction based categorization of daily places reflects the meaning of We determine three activity features (i.e.,including active- a place to a person instead of its function,which may vary ness,visiting time slots and staying duration)that can capture from person to person to better describe the context of a place the users'mobilities and the differences between activities at for every individual.For example,the same restaurant could the daily routine-based places.Activeness (i.e.active or static) be a workplace for waiters and waitresses,but it is a leisure describes the person's status at a place,e.g.,shopping in a store place for customers.This advantage enables inferring the fine- is active while dinning in a restaurant is static.Visiting time grained social relationships and demographics. slots,including the person's one or multiple entrance/departure 2)Staying Segment Categorization based on Daily Rou- time at a daily routine-based place,captures the person's tines:Next,we determine the contextual information of a specific pattern of visiting the place,e.g.,faculties may leave place (i.e.staying segment)by categorizing it into one of office several times in one day for teaching,conference,lunch the three defined daily routine-based places.The basic idea et al.Staying duration captures the time nature of the activities is to examine common time spans of the staying segments in such as buying coffee for 10 minutes or doing hair cut for one a day with the daily routines of working and home activities, hour.We note that all the other activity features,except the respectively.Whichever staying segment results in the longest activeness,can be easily obtained by examining the temporal overlapped time with the daily routine of working or home information of the staying segments.Therefore,we discuss activities will be labeled as containing the Workplace or Home. how to derive the activeness for each staying segment in detail. The rest of staying segments are determined as containing Activeness Estimation.We devise a unique activeness the Leisure Places.Since people may move between different estimation approach to determine the activeness of the user rooms for work-related activities,after determining the Work- at a place by only utilizing the RSS of APs observed in the place,we further combine the staying segments that have at staying segment(This is the only place we apply RSS in this least level-1 closeness with the staying segments of Workplace paper).The intuition behind this approach is that the user's together to represent the whole working area.The common position changes within a place result in changing distances time spans are chosen corresponding to the majority people's to every surrounding AP and thus unstable RSS from each daily routines from the reports [25],[26]:working activities- AP.From the time series of RSS in a staying segment,we 8:00AM~4:00PM;home activities-7:00PM~6:00AM; derive a time series of RSS stability of the ih AP,denoted as leisure activities -rest free hours of a day. 3)Fine-grained Place Context Inference:Our system is de- Ai=,...,...},where j is the standard deviation signed to derive more fine-grained place contexts (e.g.restau- of RSS calculated based on a sliding time window W.Then rants or stores in the Leisure Places and universities or office we further derive the activeness score of a staying segment by buildings in the Workplace)by leveraging Geo-information, using the equation: activity features of the places and the SSID context of user ∫1,2>h associated AP.We find that the APs'BSSIDs (MAC addresses) = w+0,oiherwise. (4) in a staying segment generate fine-grained place contexts where theh is a threshold of standard deviation of RSS. through certain web-based services (e.g.,Google Map Geolo- To ensure the robustness,we only consider significant APs cation API [28],Google Place API [29]and unwired labs (80%<appearance rate)in each staying segment for deriving Location API [30]).However,the place contexts obtained from the activeness score,because the significant APs can capture the Geo-information is sometimes not unique especially in a the person's activeness in the entire staying segment.Thus, crowded business area.Therefore,to refine the place contexts the activeness score is the ratio of active period over entire from the Geo-information,we further examine the activity duration at the place.As an illustration,Figure 5 shows the features in the staying segment based on the decision rules, distribution of the activeness score of all significant APs in the made from people's general time use pattern [31]and the basic staying segments,when a user is dinning at a restaurant(i.e. knowledge of activeness at various place contexts.Moreover, sitting statically)or shopping in a store(i.e.,walking actively), if the user is associated with an AP,the semantic meaning respectively.We observe more APs of dinning have lower of the AP SSID can be utilized as assistance,if available,to activeness scores (less than 0.2)compared with shopping, identify detailed contexts (e.g.company names)of the place. indicating that the activeness score can well differentiateActiviness 0 0.2 0.4 0.6 0.8 1 Percentage 0 0.1 0.2 0.3 0.4 Shopping Dining and other activities Fig. 5. Distribution of activeness score computed from each AP during staying segments when people are shopping or dinning. information of the places. Different from categorizing daily places based on their generic nature [27], our daily routine￾based categorization of daily places reflects the meaning of a place to a person instead of its function, which may vary from person to person to better describe the context of a place for every individual. For example, the same restaurant could be a workplace for waiters and waitresses, but it is a leisure place for customers. This advantage enables inferring the fine￾grained social relationships and demographics. 2) Staying Segment Categorization based on Daily Rou￾tines: Next, we determine the contextual information of a place (i.e. staying segment) by categorizing it into one of the three defined daily routine-based places. The basic idea is to examine common time spans of the staying segments in a day with the daily routines of working and home activities, respectively. Whichever staying segment results in the longest overlapped time with the daily routine of working or home activities will be labeled as containing the Workplace or Home. The rest of staying segments are determined as containing the Leisure Places. Since people may move between different rooms for work-related activities, after determining the Work￾place, we further combine the staying segments that have at least level-1 closeness with the staying segments of Workplace together to represent the whole working area. The common time spans are chosen corresponding to the majority people’s daily routines from the reports [25], [26]: working activities - 8 : 00AM∼ 4 : 00PM; home activities - 7 : 00PM∼ 6 : 00AM; leisure activities - rest free hours of a day. 3) Fine-grained Place Context Inference: Our system is de￾signed to derive more fine-grained place contexts (e.g. restau￾rants or stores in the Leisure Places and universities or office buildings in the Workplace) by leveraging Geo-information, activity features of the places and the SSID context of user associated AP. We find that the APs’ BSSIDs (MAC addresses) in a staying segment generate fine-grained place contexts through certain web-based services (e.g., Google Map Geolo￾cation API [28], Google Place API [29] and unwired labs Location API [30]). However, the place contexts obtained from the Geo-information is sometimes not unique especially in a crowded business area. Therefore, to refine the place contexts from the Geo-information, we further examine the activity features in the staying segment based on the decision rules, made from people’s general time use pattern [31] and the basic knowledge of activeness at various place contexts. Moreover, if the user is associated with an AP, the semantic meaning of the AP SSID can be utilized as assistance, if available, to identify detailed contexts (e.g. company names) of the place. Time in a day (hour) 0 6 12 18 24 Physical Closeness 0 0.2 0.4 0.6 0.8 1 Neighbor relationship Family relationship Time in a day (hour) 0 6 12 18 24 Physical Closeness 0 0.2 0.4 0.6 0.8 1 Team member relationship Collaborator relationship (a) Spatial closeness difference. (b) Temporal closeness difference. Fig. 6. Illustration of social relationships classification derived from temporal and spatial closeness based on one day’s data. B. Activity Feature Extraction We determine three activity features (i.e., including active￾ness, visiting time slots and staying duration) that can capture the users’ mobilities and the differences between activities at the daily routine-based places. Activeness (i.e. active or static) describes the person’s status at a place, e.g., shopping in a store is active while dinning in a restaurant is static. Visiting time slots, including the person’s one or multiple entrance/departure time at a daily routine-based place, captures the person’s specific pattern of visiting the place, e.g., faculties may leave office several times in one day for teaching, conference, lunch et al. Staying duration captures the time nature of the activities such as buying coffee for 10 minutes or doing hair cut for one hour. We note that all the other activity features, except the activeness, can be easily obtained by examining the temporal information of the staying segments. Therefore, we discuss how to derive the activeness for each staying segment in detail. Activeness Estimation. We devise a unique activeness estimation approach to determine the activeness of the user at a place by only utilizing the RSS of APs observed in the staying segment (This is the only place we apply RSS in this paper). The intuition behind this approach is that the user’s position changes within a place result in changing distances to every surrounding AP and thus unstable RSS from each AP. From the time series of RSS in a staying segment, we derive a time series of RSS stability of the i th AP, denoted as Λi = {λ1,...,λ j,...,λt}, where λ j is the standard deviation of RSS calculated based on a sliding time window W. Then we further derive the activeness score of a staying segment by using the equation: ψi = ∑t−w+1 j=1 v j t −w+1 , v j =  1,λ j > λth 0,otherwise, (4) where the λth is a threshold of standard deviation of RSS. To ensure the robustness, we only consider significant APs (80% ≤ appearance rate) in each staying segment for deriving the activeness score, because the significant APs can capture the person’s activeness in the entire staying segment. Thus, the activeness score is the ratio of active period over entire duration at the place. As an illustration, Figure 5 shows the distribution of the activeness score of all significant APs in the staying segments, when a user is dinning at a restaurant (i.e. sitting statically) or shopping in a store (i.e., walking actively), respectively. We observe more APs of dinning have lower activeness scores (less than 0.2) compared with shopping, indicating that the activeness score can well differentiate