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The effectiveness of an evacuation plan is a central concern of emergency management agencies. Methodologies for assessing and optimizing the space–time use efficiency of an evacuation plan have yet to be studied satisfactorily. To this end, this paper proposes an assessment metric and uses it for the optimization of evacuation plans. First, we define a space–time use efficiency metric on the basis of trajectories on road segments and intersections. The metric measures the usage of an evacuation network by supporting a trajectory-based analysis of the competing behaviors of vehicles and pedestrians in a hybrid pedestrian-vehicle simulation. Secondly, we present a two-tier hybrid multi-objective optimization algorithm to plan vehicle and pedestrian turning movement directions in an integrated road and building-interior network for the purpose of making decisions about evacuation plans. This algorithm has three objectives: (i) minimizing average evacuation time, (ii) minimizing the overall length traveled, and (iii) maximizing space–time use efficiency in the evacuation network. The stadium at the Wuhan Sports Centre in China and its adjacent road network were chosen as the study environment. A total of 23,362 evacuees and 1362 vehicles were assumed for the evacuation experiments. The analysis results suggest that intra-intersection evacuation flows can significantly influence the space–time use efficiency of a evacuation plan. The proposed space–time use efficiency evaluation approach provides a practical method of measuring the effectiveness of evacuation plans.

Local search heuristics for very large-scale vehicle routing problems VRPs have made remarkable advances in recent years. However, few local search heuristics have focused on the use of the spatial neighborhood in Voronoi diagrams to improve local searches. Based on the concept of a k-ring shaped Voronoi neighbor, we propose a Voronoi spatial neighborhood-based search heuristic and algorithm to solve very large-scale VRPs. In this algorithm, k-ring Voronoi neighbors of a customer are limited to building and updating local routings, and rearranging local routings with improper links. This algorithm was evaluated using four sets of benchmark tests for 200–8683 customers. Solutions were compared with specific examples in the literature, such as the one-depot VRP. This algorithm produced better solutions than some of the best-known benchmark VRP solutions and requires less computational time. The algorithm outperformed previous methods used to solve very large-scale, real-world distance constrained capacitated VRP.

Investigating human mobility patterns can help researchers and agencies understand the driving forces of human movement, with potential benefits for urban planning and traffic management. Recent advances in location-aware technologies have provided many new data sources (e.g., mobile phone and social media data) for studying human space-time behavioral regularity. Although existing studies have utilized these new datasets to characterize human mobility patterns from various aspects, such as predicting human mobility and monitoring urban dynamics, few studies have focused on human convergence and divergence patterns within a city. This study aims to explore human spatial convergence and divergence and their evolutions over time using large-scale mobile phone location data. Using a dataset from Shenzhen, China, we developed a method to identify spatiotemporal patterns of human convergence and divergence. Eight distinct patterns were extracted, and the spatial distributions of these patterns are discussed in the context of urban functional regions. Thus, this study investigates urban human convergence and divergence patterns and their relationships with the urban functional environment, which is helpful for urban policy development, urban planning and traffic management.

Critical transportation infrastructure has been studied extensively in recent years. This paper presents a spatiotemporal analysis of critical transportation links based on time geographic concepts. With widespread adoption of information and communication technologies (ICT) and location-aware mobile devices, large tracking datasets have become readily available. This study uses a tracking dataset of approximately 12,000 taxis in Wuhan, China over 1 week to analyze spatiotemporal origin–destination (O–D) patterns of trips that use three critical bridges connecting the three districts of Wuchang, Hankou, and Hanyang separated by the Yangtze River and the Han River. We use the space–time prism concept to identify alternative space–time paths passing through different bridges that observe the spatial and temporal constraints between each O–D pair derived from the taxi trajectory data. This case study illustrates the feasibility and benefits of using the proposed time geographic approach to analyze spatiotemporal patterns of travel demands on the critical links and their alternative paths in a transportation system.

Landmarks provide the most predominant navigation cue for pedestrian navigation. Very few navigation data models in the geographical information science and transportation communities support modeling of landmarks and use of landmark-based route instructions for pedestrian navigation services. This article proposes a landmark-based pedestrian navigation data model to fill this gap. This data model can model landmarks in several pedestrian navigation scenarios buildings, open spaces, multimodal transportation systems, and urban streets. This article implements the proposed model in the ArcGIS software environment and demonstrates two typical pedestrian navigation scenarios: 1 a multimodal pedestrian navigation environment involving bus lines, parks, and indoor spaces and 2 a subway system in a metropolitan environment. These two scenarios illustrate the feasibility of the proposed data model in real-world environments. Further improvements of this model could lead to more intuitive and user-friendly landmark-based pedestrian navigation services than the functions supported by current map-based navigation systems.

Complex spatial control problems can be computationally intensive. Timely response in urgent spatial control situations such as wildfire control poses great challenges for the efficient solving of spatial control problems. Web-based and service-oriented architectures of integrating geographic information system (GIS) clients and parallel computing resources have been suggested as an effective paradigm to solve computationally intensive spatial problems. Such real-time coupling framework is highly dependent upon interactivity and on-demand availability of dedicated parallel computing resources appropriate for the problem. We present an approach to enhancing the efficiency of solving spatial control problems while offering another coupling framework of integrating computing resources from desktop GIS and parallel computing environments to alleviate such dependency. Specifically, a model knowledge database is developed to bridge the gap between desktop GIS models and parallel computing resources. Desktop GIS...

Study of human activities in space and time has been an important research topic in transportation research. Limitations of conventional statistical methods for analysis of individual-level human activities have encouraged spatiotemporal analysis of human activity patterns in a space–time context. Based on Hagerstrand’s time geography, this study presents a space–time GIS approach that is capable of representing and analyzing spatiotemporal activity data at the individual level. Specifically, we have developed an ArcGIS extension, named Activity Pattern Analyst (APA), to facilitate exploratory analysis of activity diary data. This extension covers a set of functions such as space–time path generation, space–time path segmentation, space–time path filter, and activity distribution/density pattern exploration. It also provides a space–time path based multi-level clustering method to investigate individual-level spatiotemporal patterns. Using an activity diary dataset collected in Beijing, China, this paper presents how this Activity Pattern Analyst extension can facilitate exploratory analysis of individual activity diary data to uncover spatiotemporal patterns of individual activities.

People in modern societies are increasingly experiencing the impacts of information and communication technologies (ICT) on their daily activities and their interactions with other people. These changing activity and interaction patterns due to the use of ICT have important implications to human activity and travel patterns. There are scant studies offering a spatiotemporal analytical framework that can help researchers examine the interactions between ICT and human activities at the individual level. Time geography offers a useful approach of studying individual activities and human interactions in a space-and-time context. By extending a time-geographic analytical framework, this study uses an example of arranging a face-to-face (F2F) meeting between two people to illustrate how potential F2F meeting opportunities can be impacted under four different scenarios of phone access levels. This spatiotemporal analytical framework and the various phone access scenarios are implemented in a space–time geographic information system (GIS) to demonstrate the feasibility of this analytical framework. In addition, this time-geographic framework offers analysis functions in support of quantitative assessments of ICT’s impacts on human activities.

The concept of accessibility needs to take into account factors like individual needs, preferences, and abilities as well as transportation, land use, and finances. This article proposes a sensitive space–time accessibility indicator from a regional perspective. This indicator, representing cumulative available activity time with a regional context, is different from both traditional accessibility measures (e.g., gravity-based accessibility, cumulative accessibility, utility-based accessibility, and so on) and previous concepts of individual space–time accessibility. The major advantage of this indicator is its sensitivity to the location of the anchor point, the transportation network, the distribution of activity sites over the whole region, and certain individual needs such as minimum activity times and activity time budgets. Several examples are worked to demonstrate these sensitivities and an innovative accessibility pattern which was previously unknown. This indicator should be helpful for planning ...

Visual landmarks are important navigational aids for research into and design of applications for last mile pedestrian navigation, e.g., business card route of pedestrian navigation. The business card route is a route between a fixed origin (e.g., campus entrance) to a fixed destination (e.g., office). The changing characteristics and combinations of various sensors&rsquo, data in smartphones or navigation devices can be viewed as invisible salient landmarks for business card route of pedestrian navigation. However, the advantages of these invisible landmarks have not been fully utilized, despite the prevalence of GPS and digital maps. This paper presents an improvement to the Dempster&ndash, Shafer theory of evidence to find invisible landmarks along predesigned pedestrian routes, which can guide pedestrians by locating them without using digital maps. This approach is suitable for use as a &ldquo, business card&rdquo, route for newcomers to find their last mile destinations smoothly by following precollected sensor data along a target route. Experiments in real pedestrian navigation environments show that our proposed approach can sense the location of pedestrians automatically, both indoors and outdoors, and has smaller positioning errors than purely GPS and Wi-Fi positioning approaches in the study area. Consequently, the proposed methodology is appropriate to guide pedestrians to unfamiliar destinations, such as a room in a building or an exit from a park, with little dependency on geographical information.

Taxi drivers drive on the roads every day and become very knowledgeable of the spatiotemporal traffic patterns in a city. It therefore is reasonable to assume that the routes chosen by taxi drivers often work out better than those selected by other drivers. Since dynamic navigation assistance based on real-time traffic information faces limitations such as the spatial coverage of real-time data collection sites, performance of real-time data processing and communications, and accuracy of short-term traffic forecasts in a large urban area, experiences gained by taxi drivers can be a valuable data source for improving the quality of vehicle navigation guidance. This paper develops a vehicle navigation guidance system based on taxis drivers’ knowledge derived from floating car data collected over an extended time period. We then classify road segments based on the spatiotemporal characteristics of taxi tracking data. A case study using taxi tracking data collected in Wuhan, China is presented in this paper to demonstrate the performance of this vehicle navigation system based on taxi tracking data.

While the literature clearly acknowledges that individuals may experience different levels of segregation across their various socio-geographical spaces, most measures of segregation are intended to be used in the residential space. Using spatially aggregated data to evaluate segregation in the residential space has been the norm and thus individual's segregation experiences in other socio-geographical spaces are often de-emphasized or ignored. This paper attempts to provide a more comprehensive approach in evaluating segregation beyond the residential space. The entire activity spaces of individuals are taken into account with individuals serving as the building blocks of the analysis. The measurement principle is based upon the exposure dimension of segregation. The proposed measure reflects the exposure of individuals of a referenced group in a neighborhood to the populations of other groups that are found within the activity spaces of individuals in the referenced group. Using the travel diary data collected from the tri-county area in southeast Florida and the imputed racial-ethnic data, this paper demonstrates how the proposed segregation measurement approach goes beyond just measuring population distribution patterns in the residential space and can provide a more comprehensive evaluation of segregation by considering various socio-geographical spaces.

Air transportation in China has experienced tremendous growth and major reforms in the past three decades. While there is a large literature on airline liberalization, publications about China are limited. As China transforms from a centrally planned economy to a market economy, how closely has China followed the practice of liberalizing the airline industry seen in other countries around the world? This paper offers a review of air transportation development in China and compares China’s approach with other countries. In particular, this study examines the impact of the government-led airline consolidation of 2002 upon domestic network structure and competition.

Modern information and communication technologies (ICT) are changing human activity and travel patterns that could have significant implications to our everyday lives and the human organization of space. Time geography, which examines human activities under various constraints in a space–time context, provides a useful framework to analyze the complex spatio-temporal relationships among activities and interactions taking place in both physical and virtual spaces. However, virtual activities and interactions conducted via ICT have characteristics that cannot be properly represented and analyzed under the classical time-geographic framework. This paper extends classical time-geographic concepts to accommodate the needs of representing and analyzing all activities and interactions in a hybrid physical–virtual space. In addition, this paper presents a space–time geographic information system (GIS) design that is capable of organizing complex activity and interaction data as spatio-temporal processes in an integrated space–time environment. This space–time GIS design offers a useful analytical environment for researchers to study increasingly dynamic human activity and travel patterns in today’s society and their implications toward changing travel demand patterns from both spatial and temporal perspectives.

The increasing number of large individual-based spatiotemporal datasets in various research fields has challenged the GIS community to develop analysis tools that can efficiently help researchers explore the datasets in order to uncover useful information. Rooted in Hagerstrand's time geography, this study presents a generalized space-time path (GSTP) approach to facilitating visualization and exploration of spatiotemporal changes among individuals in a large dataset. The fundamental idea of this approach is to derive a small number of representative space-time paths (i.e. GSTPs) from the raw dataset by identifying spatial cluster centers of observed individuals at different time periods and connecting them according to their temporal sequence. A space-time GIS environment is developed to implement the GSTP concept. Different methods of handling temporal data aggregation and the creation of GSTPs are discussed in this article. Using a large individual-based migration history dataset, this study successfully develops an operational space-time GIS prototype in ESRI's ArcScene and ArcMap to provide a proof-of-concept study of this approach. This space-time GIS system demonstrates that the proposed GSTP approach can provide a useful exploratory analysis and geovisualization environment to help researchers effectively search for hidden patterns and trends in such datasets.

Great Smoky Mountains National Park (GSMNP) is filled with an abundance of ecological diversity, historical significance, and recreational opportunities for visitors to explore and experience. The wide range of potential activities available in the park also present a major challenge for park visitors to plan activities that will better meet their preferences and constraints. With the large amounts of spatial and non-spatial data associated with the diverse resources and activities in the park, it is a logical choice to use geographic information systems (GIS) for storing, managing, analyzing, and visualizing the data. Nevertheless, GIS functions alone are insufficient to facilitate activity planning for park visitors. This paper presents a GIS-based spatial decision support system (SDSS) application that integrates GIS functions and SDSS designs with easy-to-use graphic user interfaces to help visitors of GSMNP choose and plan their activities more effectively to match their personal preferences and constraints.

Mobile phone location data is a newly emerging data source of great potential to support human mobility research. However, recent studies have indicated that many users can be easily re-identified based on their unique activity patterns. Privacy protection procedures will usually change the original data and cause a loss of data utility for analysis purposes. Therefore, the need for detailed data for activity analysis while avoiding potential privacy risks presents a challenge. The aim of this study is to reveal the re-identification risks from a Chinese city’s mobile users and to examine the quantitative relationship between re-identification risk and data utility for an aggregated mobility analysis. The first step is to apply two reported attack models, the top N locations and the spatio-temporal points, to evaluate the re-identification risks in Shenzhen City, a metropolis in China. A spatial generalization approach to protecting privacy is then proposed and implemented, and spatially aggregated analysis is used to assess the loss of data utility after privacy protection. The results demonstrate that the re-identification risks in Shenzhen City are clearly different from those in regions reported in Western countries, which prove the spatial heterogeneity of re-identification risks in mobile phone location data. A uniform mathematical relationship has also been found between re-identification risk (x) and data (y) utility for both attack models: y = -ax b+c, (a, b, c>0; 0

Land use and transportation interaction is a complex, dynamic process. Many models have been used to study this interaction process during the past several decades. Empirical studies suggest that land use and transportation interaction patterns can be highly variable between geographic areas and at different spatial and temporal scales. This paper presents a temporal geographic information systems (GIS) design that offers exploratory data analysis capabilities to interactively examine land use and transportation interaction at user-specified spatial and temporal scales. A spatiotemporal interaction framework, implemented with temporal GIS databases, provides a foundation for the development of spatiotemporal analysis functions to systematically explore land use and transportation interaction.

The introduction of the Huff model is of critical significance in many fields, including urban transport, optimal location planning, economics and business analysis. Moreover, parameters calibration is a crucial procedure before using the model. Previous studies have paid much attention to calibrating the spatial interaction model for human mobility research. However, are whole sampling locations always the better solution for model calibration? We use active tracking data of over 16 million cell phones in Shenzhen, a metropolitan city in China, to evaluate the calibration accuracy of Huff model. Specifically, we choose five business areas in this city as destinations and then randomly select a fixed number of cell phone towers to calibrate the parameters in this spatial interaction model. We vary the selected number of cell phone towers by multipliers of 30 until we reach the total number of towers with flows to the five destinations. We apply the least square methods for model calibration. The distribution of the final sum of squared error between the observed flows and the estimated flows indicates that whole sampling locations are not always better for the outcomes of this spatial interaction model. Instead, fewer sampling locations with higher volume of trips could improve the calibration results. Finally, we discuss implications of this finding and suggest an approach to address the high-accuracy model calibration solution.

Time geography provides a powerful analytic framework to identify alternative space-time paths as well as space-time opportunities based on the concept of a space-time prism. Both of these are important to activity scheduling, which is a potentially popular traveler assistance service. Spatiotemporal analysis of alternative space-time paths between origin–destination pairs helps identify time-dependent critical links involving joint participants. In real activity scheduling situations, space-time opportunities can be highly dependent on time-dependent critical links. This chapter presents a time-dependent prism-based approach to identify critical links and opportunities for scheduling joint participation of multiple individuals. The first section introduces the generation of time-varying network-based prisms. In this part, a tracking dataset of 12,325 taxis in Wuhan, China, over one week was used to capture travel speed and time-dependent congestion of each link in the road network. This prism was identified based on time-dependent travel speeds. Then the chapter presents the identification of time-dependent critical links and opportunities between each origin–destination pair. Spatiotemporal analysis of alternative space-time paths between origin–destination pairs is considered in the evaluation of the use priority of links in real taxi services. This analysis supports the identification of time-dependent critical links involving joint participants. Finally, this chapter introduces a multi-objective approach to scheduling joint participation of multiple individuals by incorporating the previous two approaches. Five objectives are used here: (i) minimizing travel distance; (ii) minimizing travel time; (iii) maximizing expected participation activity time; (iv) minimizing the added travel time over alternative space-time paths; and (v) maximizing the utility index of the selected critical activity opportunity. A scenario involving joint participation among four people is designed and implemented to demonstrate the feasibility of this approach.

Much literatures have focused on the analysis of the impacts of accessibility brought by HSR, but analysis on the uneven accessibility impacts and patterns of China's high-speed rail are not complete. This paper will present an inequality analysis from the national level based on each station's time accessibility from the actual timetable in four stages, namely, the stage 1 with traditional rail service (before Aug. 2008), the stage 2 with HSR lines (between Aug. 2008 and July 2011), the stage 3 with speed-reduced HSR lines (between Aug. 2011 and Nov. 2012), and the stage 4 with some new HSR lines(between Dec. 2012 and Jan. 2013). Finally, this paper gives several suggestions to guide the allocation of new high-speed railway lines and the collaborative operations with other kinds of railway train lines. In addition, the proposed method is also useful to promote the HSR service more fairly.

Disaggregate travel data is not new to urban transportation planning studies, but it is infrequently handled in a GIS environment. With the evolution of urban travel demand models from aggregate models to disaggregate models and from a trip-based paradigm to an activity-based paradigm, there is a growing need of managing disaggregate travel data with spatial and temporal components in a GIS environment. At the data organization level, the main challenge is to efficiently store the data by minimizing redundancy while maintaining the complex relationships among the data items. The data organization should allow users to retrieve and visualize disaggregate travel data according to various possible combinations of spatial, temporal, and attribute criteria. This paper presents an implementation that employs a relational database approach and dynamic segmentation to organize the spatial, temporal, and attribute components in a sample travel diary data set. Discussions of the benefits and shortcomings associated with this approach are provided, along with suggestions for future research.

This study uses a large-scale mobile phone dataset to estimate potential demand of bicycle trips in a city. By identifying two important anchor points (night-time anchor point and day-time anchor point) from individual cellphone trajectories, this study proposes an anchor-point based trajectory segmentation method to partition cellphone trajectories into trip chain segments. By selecting trip chain segments that can potentially be served by bicycles, two indicators (inflow and outflow) are generated at the cellphone tower level to estimate the potential demand of incoming and outgoing bicycle trips at different places in the city and different times of a day. A maximum coverage location-allocation model is used to suggest locations of bike sharing stations based on the total demand generated at each cellphone tower. Two measures are introduced to further understand characteristics of the suggested bike station locations: (1) accessibility; and (2) dynamic relationships between incoming and outgoing trips. The accessibility measure quantifies how well the stations could serve bicycle users to reach other potential activity destinations. The dynamic relationships reflect the asymmetry of human travel patterns at different times of a day. The study indicates the value of mobile phone data to intelligent spatial decision support in public transportation planning.

The Airline Deregulation Act of 1978 introduced high-level competition into the US domestic industry. The resulting network changes have created a deluge of research, largely focused on the hub-and-spoke structure. Over 15 years have passed since deregulation, and the industry still appears somewhat unsettled. Recent concerns of domestic carriers include downsizing of some hubs, and discussions of further consolidation of operations. Mergers may be the answer for airlines with shrinking markets within the US transportation system. This study examines the effect on network structure created by the merger of two carriers from the perspective of hub structures, accessibility and geographic coverage.

The airline industry in the United States has experienced significant changes in the spatial configuration of networks since the Airline Deregulation Act of 1978. Attempts have been made to study the structure of the hub-and-spoke network in the airline industry, but no efficient methodology has been available. This paper describes an exploratory approach to analyzing the spatial configuration of airline networks. Flight frequencies and numbers of passengers from published schedules of six U.S. domestic airlines (American, Continental, Delta, Northwest, United, and USAir) are used in this study. The analytic components in the exploratory system include the examination of network connectivity and network autocorrelation in a dynamic mapping environment. The results of the analyses show both the overall spatial patterns of airline networks and the hierarchical hubbing structures.

In this paper, we describe an approach to integrate a Space-Time GIS data model on a high performance computing platform. The Space-Time GIS data model has been developed on a desktop computing environment. We use the Space-Time GIS data model to generate GIS module, which organizes a series of remote sensing data. We are in the process of porting the GIS module into an HPC environment, in which the GIS modules handle large dataset directly via parallel file system. Although it is an ongoing project, authors hope this effort can inspire further discussions on the integration of GIS on high performance computing platforms.

In a recent comment published in Applied Geography , Martin Kenzer (1992) raised a concern about ‘the relationship between applied geography and the swiftly growing technique side of the discipline’. As summarized in a response from Sant (1992), Kenzer's arguments are based on three propositions: that pure and applied geography are not separate fields; that applied geography is heavily driven by techniques; and that the preoccupation with techniques is detrimental both to the discipline and to the prospects of its graduates. The first proposition is fairly widely accepted and thus should not be in question (Sant 1992). Regarding the other two propositions, the rapid growth of geographical information systems (GIS) in geography appears to be the main concern behind Kenzer's arguments. In fact, he suggests that GIS might be experiencing a misdirected drive toward independence that is analogous to what applied geography attempted to do in the past (Kenzer 1992). This is the topic to be addressed here.

Since the Airline Deregulation Act of 1978, the hub-and-spoke network model has been adopted by airlines as the primary strategy for organizing their route structures. The hub location models, however, have not been able to capture completely the realities of the hub network design problem for passenger airline networks. This study examines the hub structures of six major US airlines based on three different measures of the intensity of hubbing at network nodes. Implications of the analysis results for the hub location models are discussed.

The analytical aspect of GIS has often been used to distinguish it from other spatial data handling systems. Current GIS development, however, is still short of supporting most spatial analysis and modeling applications. In addition to a general review of GIS and spatial analysis functionality, there is a need to examine the practical requirements of integrating specific modeling applications with GIS. This paper examines such requirements for the urban travel demand models. Alternative strategies of integrating these requirements into a GIS are then discussed.

This paper proposes a multi-activities scheduling algorithm based extended space-time prism under time geography framework. This algorithm aims to schedule multi-activities from different kinds of activity, which can be used as kernel function of an innovative location based service-multi-activities scheduling service. The extended space-time prism is derived of space-time prism concepts of time geography. For each available activity opportunity in this extended spacetime prism, two new shortest path trees are computed to measure the travel time between activities from different kinds of activity, one new space-time tree arrive at this available activity opportunity, and the another one departs from this available activity opportunity. A prototype 3D space-time environment is implemented to support the concept of extended space-time prism, and provide function of this proposed algorithm. An example of multi-activities scheduling scenario is shown to demonstrate the usefulness of this proposed algorithm for this location based service. Finally, several future works are also introduced in this paper.

Beijing has deployed Intelligent Transportation System (ITS) monitoring devices along selected major roads in the core urban area in order to help relieve traffic congestion and improve traffic conditions. The huge amount of traffic data from ITS originally collected for the control of traffic signals can be a useful source to assist in transportation designing, planning, managing, and research by identifying major traffic patterns from the ITS data. The importance of data visualization as one of the useful data mining methods for reflecting the potential patterns of large sets of data has long been recognized in many disciplines. This paper will discuss several comprehensible and appropriate data visualization techniques, including line chart, bi-directional bar chart, rose diagram, and data image, as exploratory data analysis tools to explore traffic volume data intuitively and to discover the implicit and valuable traffic patterns. These methods could be applied at the same time to gain better and more comprehensive insights of traffic patterns and data relationships hidden in the massive data set. The visual exploratory analysis results could help transportation managers, engineers, and planners make more efficient and effective decisions on the design of traffic operation strategies and future transportation planning scientifically.