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The purpose of this paper is to generate information about the contours of police responsiveness, focussing on how quickly and precisely police make firearm arrests after a shooting incident.

Using a modified version of the Knox close pair method, a spatio-temporal clustering technique, over 11,000 shooting incidents and firearm arrests between 2004 and 2007 in Philadelphia, PA were analyzed.

Police are responding quickly and in a geographically targeted fashion to shootings. Across Philadelphia elevated patterns of firearm arrests were approximately two and a half times greater than would be expected if shootings and firearm arrests lacked a spatio-temporal association. Greater than expected patterns of firearm arrests persisted for roughly one-fourth of a mile and for about one week from the shooting incident but the strength of these associations waned over space and time. The pattern of police response varied slightly across different police divisions.

The current method uncovered spatio-temporal patterning and determined when these patterns were significantly different from what would be expected if the events were completely independent. Specific events and processes surrounding each event are not known.

Findings can help inform the knowledge about police behavior in terms of how police produce arrests.

The patterns observed here provide more micro-level detail than has been revealed in previous studies regarding police responsiveness to firearm violence while also introducing a more integrated spatially and temporally specific framework.

A wide number of technologies are currently in store to harness the challenges posed by pandemic situations. As such diseases transmit by way of person-to-person contact or by any other means, the World Health Organization had recommended location tracking and tracing of people either infected or contacted with the patients as one of the standard operating procedures and has also outlined protocols for incident management. Government agencies use different inputs such as smartphone signals and details from the respondent to prepare the travel log of patients. Each and every event of their trace such as stay points, revisit locations and meeting points is important. More trained staffs and tools are required under the traditional system of contact tracing. At the time of the spiralling patient count, the time-bound tracing of primary and secondary contacts may not be possible, and there are chances of human errors as well. In this context, the purpose of this paper is to propose an algorithm called SemTraClus-Tracer, an efficient approach for computing the movement of individuals and analysing the possibility of pandemic spread and vulnerability of the locations.

Pandemic situations push the world into existential crises. In this context, this paper proposes an algorithm called SemTraClus-Tracer, an efficient approach for computing the movement of individuals and analysing the possibility of pandemic spread and vulnerability of the locations. By exploring the daily mobility and activities of the general public, the system identifies multiple levels of contacts with respect to an infected person and extracts semantic information by considering vital factors that can induce virus spread. It grades different geographic locations according to a measure called weightage of participation so that vulnerable locations can be easily identified. This paper gives directions on the advantages of using spatio-temporal aggregate queries for extracting general characteristics of social mobility. The system also facilitates room for the generation of various information by combing through the medical reports of the patients.

It is identified that context of movement is important; hence, the existing SemTraClus algorithm is modified by accounting for four important factors such as stay point, contact presence, stay time of primary contacts and waypoint severity. The priority level can be reconfigured according to the interest of authority. This approach reduces the overwhelming task of contact tracing. Different functionalities provided by the system are also explained. As the real data set is not available, experiments are conducted with similar data and results are shown for different types of journeys in different geographical locations. The proposed method efficiently handles computational movement and activity analysis by incorporating various relevant semantics of trajectories. The incorporation of cluster-based aggregate queries in the model do away with the computational headache of processing the entire mobility data.

As the trajectory of patients is not available, the authors have used the standard data sets for experimentation, which serve the purpose.

This paper proposes a framework infrastructure that allows the emergency response team to grab multiple information based on the tracked mobility details of a patient and facilitates room for various activities for the mitigation of pandemics such as the prediction of hotspots, identification of stay locations and suggestion of possible locations of primary and secondary contacts, creation of clusters of hotspots and identification of nearby medical assistance. The system provides an efficient way of activity analysis by computing the mobility of people and identifying features of geographical locations where people travelled. While formulating the framework, the authors have reviewed many different implementation plans and protocols and arrived at the conclusion that the core strategy followed is more or less the same. For the sake of a reference model, the Indian scenario is adopted for defining the concepts.

Knowing the behavior of tourists visiting cultural destinations enables better management of tourist flows, a better understanding of areas with greater tourist density and an opportunity to decongest popular neighborhoods. The purpose of this study is to segment tourists according to their spatio-temporal behavior and identify the primary variables that characterize the resulting segments, which will help urban destinations prevent problems arising from the saturation of tourists in certain areas.

To do this, this paper analyzes the behavior of tourists visiting the southeastern Spanish city of Granada, one of the most highly visited cultural tourism destinations. The data analysis used the methodology of sequence alignment which is used to identify segments as a function of their contained elements and the order in which these appear.

The results demonstrate the existence of three segments with different behavioral patterns: the “explorer tourists” segment, the “non-traditional cultural tourists” segments and the “typical cultural tourists” segment. These segments show differences in the concentration of their visits. This study discovered that the segments that visit a greater number of destination areas are those with less cultural orientation, higher travel budgets and younger and more frequent visitors.

In the segmentation not only keep in mind the visited areas, but the order in which they were visited as well. In addition, one should consider the time that each tourist remains in each relevant zone of the destination, given that the visiting time is an important variable to assess the congestion of an area.

The purpose of this paper is to provide a method to examine the differences in behaviour during a post‐quake period.

Fieldwork and questionnaires were used to collect the households’ members’ movement behaviours after the 2004 Chuetsu Earthquake. In total, three study areas were selected in Kawaguchi town (Niigata Prefecture) in order to enhance how the visualisation process can provide support in better understanding the behaviour during evacuation and recovery process. For this purpose the Space‐Time‐Cube (STC) was used to represent and analyse residents’ movement paths over time.

Differences appear in the spatio‐temporal paths of the three study areas, implying a connection between the geographical location and movement patterns. The city centre shows disorganized Spatio‐Temporal‐Patterns (STPs) during the first week of the recovery process, eventually becoming organized after the rescuers’ arrival. Moving towards the isolated areas of the town, a progressive STP organisation can be observed, explaining the faster response after the seismic event.

Spatio‐temporal data are difficult and costly to collect, especially if a long period of time passes between the seismic event and the survey.

The STC can be used as tool to enhance the disaster management techniques and provide support in crisis situations.

The paper provides a practical approach to investigate the reactions after a seismic event and can be used in larger study areas to develop better strategies in disaster management.

The purpose of this paper is to propose and demonstrate how Tourism2vec, an adaptation of a natural language processing technique Word2vec, can serve as a tool to investigate tourism spatio-temporal behavior and quantifying tourism dynamics.

Tourism2vec, the proposed destination-tourist embedding model that learns from tourist spatio-temporal behavior is introduced, assessed and applied. Mobile positioning data from international tourists visiting Tuscany are used to construct travel itineraries, which are subsequently analyzed by applying the proposed algorithm. Locations and tourist types are then clustered according to travel patterns.

Municipalities that are similar in terms of their scores of their neural embeddings tend to have a greater number of attractions than those geographically close. Moreover, clusters of municipalities obtained from the K-means algorithm do not entirely align with the provincial administrative segmentation.

This paper aims to explore the spatio-temporal patterns of Chinese foreign direct investment (FDI) since the inception of the Belt and Road Initiative (BRI) in 2013 as an extended version of geographically weighted regression.

The panel data are used to examine spatial and temporal dynamics of the magnitude and the direction of China's outward FDI stock and its flow from 2011 to 2015 at a country level. Using the geographically and temporally weighted regression (GTWR), spatio-temporal distribution of FDI is explained through Logistic Performance Index, the size of gross domestic product (GDP), Shipping Linear Connectivity Index and Container Port Throughput.

A comparative analysis between participating and non-participating countries in the BRI shows that the size of GDP and Container Port Throughput of the participating countries have a positive effect on the increases of China's outward FDI Stock to Asia especially after 2013, while non-participating countries, such as North America, Western Europe and Western Africa, have no significant effect on it before and after the implementation of the BRI.

The findings, however, will not necessarily provide insight into the needs of China's outward FDI in certain countries to develop their economy. The findings provide the evidence to inform policy making to help identify the winners and losers of the investment, scale and direction of investment and the key drivers that shape the distributive investment patterns globally.

The study provides the empirical evidence to inform investment policy and strategic realignment by quantifying scale, direction and drivers that shape the spatio-temporal shifts of China's FDI.

The analysis also guides the Chinese government improve bilateral trade, build infrastructure and business partnerships with preferential countries participating in the BRI.

There is an urgent need to adopt a new perspective to unfold the spatial temporal complexity of FDI that incorporates space and time dependencies, and the drivers of the situated context to model their effects on FDI. The model is based on GTWR and an extended geographically weighted regression (GWR) allowing the simultaneous analysis of spatial and temporal decencies of exploratory variables.

The purpose of this paper is twofold: first is to examine the changing spatio-temporal patterns and regional trends in residential fires; and second is to investigate the likely association of fire risk with seasons, calendar events and socio-economic disadvantage.

Using spatial analytic and predictive techniques, 11 years of fire incident data supplied by the Queensland Fire and Emergency Services are mapped and analysed.

The results show significant spatial and temporal variability in the distribution of residential fires. Residential fire incidents are more likely to occur in the inner city and across more disadvantaged areas. Mapped outputs show some areas in Brisbane at a higher risk of fire than others and that the risk of fire escalates at specific times of the year, in neighbourhoods with a higher disadvantage, during major sporting events and school holidays. The residential fires showed strong seasonal periodicity. There is a continuous yet gradual increase in the number of fire incidents recorded for all five sub-regions within SEQ. Sunshine Coast experienced the highest upward trend whereas Toowoomba and West Moreton show the lowest increase.

This study provides an empirical basis to guide future operational strategies through targeting high fire risk areas at particular times. This, in turn, will help utilise finite resources in areas where and when they need and thus enable minimise emergency management costs.

This chapter analyzes spatio-temporal patterns of female homicides in Mexico during the period 1990 to 2018. It analyzes socio-demographic and geographical characteristics of female homicides from which it is possible to apply statistical methods that identify regions with high incidence rates that persist over time. It is also discussed the growing participation of civil society organizations (CSOs) and its role on establishing accountability mechanisms and the developments of public policy programs in light of the poor institutional capacity of the Mexican state to address this problem. The findings here described suggest a demographic and geographic spread of female homicides – that is, the phenomenon of violence against women has reached more significant socio-demographic segments whose incidence covers a greater territorial extension. Furthermore, it is argued that despite the strategies implemented by the federal and local government on addressing the problem, the results are far from being acceptable. As argued, this calls for a nationwide initiative, the involvement of international agencies, and the consolidation of women’s empowerment though participatory mechanisms in all aspects of public life.

Purpose — Fare validation data from transit smart card automatic fare collection (AFC) systems have properties that align with the direction of large-scale mobility surveys and the evermore demanding data needs of the transit industry. In addition to applications in transit planning and service monitoring, travel patterns and behaviour can effectively be studied by exploiting the continuous stream of observations from the same card. The paper proposes a methodology to enrich fare validation data in order to generate information that is hard to obtain with traditional travel surveys.

Methodology/approach — The methodology aims to synthesize individual-level attributes by summarizing multi-day validation records from each card. These new dimensions are then transposed to various levels of aggregation and studied simultaneously in multivariate analysis. The methodology can also be applied to synthesize other multi-day attributes and is transferable to other modes and other travel behaviour studies.

Findings — Results show that validation data can effectively be used to measure the distribution of travel patterns in time and space as well as the variation of those phenomena over time. The paper provides several examples based on millions of validation records from the metro sub-network of Montréal, along with interpretations and some practical implications.

Research limitations/implications — Limitations and bias regarding the data and the methodology as well as the strategies to handle them are discussed within the context of passive travel survey and travel behaviour studies.

Practical implications — Practitioners in transit planning, operations, marketing and modelling can benefit from studying the increasingly accessible and massive smart card datasets through a deeper understanding of multi-day travel patterns and behaviour of transit users.

Originality/value — This paper outlines a data modelling approach and simple-to-implement methodology which exploit the multi-day property of fare validation data from a smart card AFC. The concept of multi-day attributes is introduced. The analyses show that the approach is effective for extracting information on travel behaviour and its variation which would otherwise be hard to obtain through traditional travel surveys, opening up another dimension of this data source for practitioners and transport modellers alike.

The industrial property market has traditionally been under‐researched but, in recent years, studies have ranged from examining rental change at the national level to examining supply factors at the local level. These studies are valuable to the real estate community, but there still remain significant gaps. This paper aims to focus on two of these inter‐related gaps. The interaction between inflation and rental change has been largely overlooked at all levels of data aggregation. Further, the relative importance of national factors, and regional and local factors, in rental determination has also been ignored.

National and regional long‐run time series models are estimated accounting for the impact of inflation on real rents, using approaches adopted in macro‐economic consumption function analyses. The statistical validity of these models is confirmed from co‐integration tests. Local level spatio‐temporal rental changes are then examined using the hierarchical method of cluster analysis.

This paper finds that, at national and regional levels, inflation reduces real industrial rents. National regional and local factors are all found to be important in governing rental change in local markets. This implies that factors operating on all spatial scales must be considered in rental studies.

This paper combines two methodological approaches to examine the interaction between inflation and rental change, and the relative importance of national, regional and local factors in rental determination. The results suggest that factors operating on all spatial scales should be considered in rental studies.