GIS and the Social Sciences

LEARNING OBJECTIVES

▪ Using GIS for the mapping and analysis of transport networks

▪ How GIS can be used for the analysis of spatial patterns and hotspots of traffic accidents

▪ Using GIS to map and analyse public transport network accessibility

▪ GIS for the planning and impact analysis of new transport routes

Transport plays an undeniably vital role in contemporary society as among some of the most commonly used services provided by both national and local governments. Most modern cities are a result of some historic transport feature, for example river crossings and road or rail intersections, and must consider transport provision for development and planning at every level including the provision and maintenance of pavements (sidewalks) and local roads, traffic signals and signs, pedestrian crossings, car parks, cycle lanes, transport interchanges, local air quality, road safety and bus services. The provision of, and rights to, private and public transport, and easy access to goods and services, are often taken for granted, but have realised greater concern in recent times with increasing issues of congestion and pollution.

Transport systems are inherently spatial and are particularly well suited to planning, analysis and management through developing GIS and spatial analysis, a relationship that forms the content for this chapter. We should first, though, acknowledge that transport has been a core component of many previous chapters, especially when we have discussed accessibility to goods and services which often consider some kind of transport network. We first look at the fundamental elements of movement, and more specifically the reasons that trips are made and the networks that facilitate these movements. The chapter will then draw this information together in the investigation of a range of GIS applications to control and manage the many facets of transport planning in the urban or regional environment.

As GIS has become more widespread in transport planning so we have witnessed a number of useful reviews of the history and development of applications in this field which make useful complementary reading to this chapter (i.e. Goodchild, 2000; Thill, 2000; Nyerges, 2004).

The understanding of movements or flows is a concept fundamental to the field of transport planning, but is a subject that will never be static or completely controlled due to the random nature of human factors. The best representation that we can hope to achieve is by making generalisations about the volume, frequency, timing and purpose of trips. Fortunately for planners, this method is fairly consistent and reliable. For example, at the simplest level, we know that road and rail links into and out of city centres are likely to host major flow events at ‘rush hours’ on weekdays due to worker commuting patterns. Table 12.1 illustrates an attempt to classify personal trips by destination and mode. Although somewhat dated, it is still very relevant and suggests that a large proportion of personal journeys consist of local and suburban movements, usually for the purpose of everyday activities such as work, school, shopping and visiting friends and relatives. With increased access to cars and improved provision of public transport, populations have become more mobile, facilitating greater ease and frequency of trips.

The controlling factor to movement will continue to be cost and, as we will see later in this chapter, there is a function of cost termed a ‘distance decay’ factor which determines how far people are prepared to pay to travel for a particular good, service or function. As an extreme example, employees could not justify living over a certain distance to work if their travel costs exceeded income or if time taken for the trip was unfeasible. This parameter provides a guide for flow modelling on a regional or national basis but might be of less importance in an urban environment where goods, services and consumers are fairly well concentrated and connected. Despite this assertion, the distance decay factor could be argued to work at all levels depending on trip purpose; for example, a consumer is unlikely to drive or take a bus across a metropolitan landscape for a pint of milk, but would probably make this journey for work or leisure purposes.

Unfortunately for the planner, information on personal movements can be difficult to come across or costly to acquire. This could be a blessing in disguise when considering the complexity and volume of information that such a data set would generate, and the realisation that such data would require spatial aggregation before any meaningful analysis could be undertaken (although the increasing amount of data becoming available from media sources such as mobile phone or ‘twitter’ data should provide a new era of ‘big data’ analysis – see Birkin et al. (2017)). The standard format for flow data operates under geographic subdivisions such as census wards or tracts or some zoning system based on postal geography as origins and destinations.

Other flow data can be collected by various interested parties in fields such as health and retail. Health care planners are particularly concerned with the pattern of service uptake in the form of GP and hospital catchment area analysis. For the effective provision of services it is paramount that trip distances to facilities are controlled and minimised, and data to illustrate trends can be taken from patient registration information (see Chapter 9 for more details).

Retailers often use their loyalty cards to gather information on customer shopping trends. From basic address and purchase information, a retailer can study patterns of who is travelling from where (and to buy what) in order to aid their understanding of local retail environments, understand market shares and facilitate customer targeting and branch/outlet location reviews. In the UK, the ‘Nectar’ loyalty card scheme contains details on the shopping behaviour of the holders of over 18 million cards, around 12 million of which are actively used, enabling participating retailers to understand more about the trip-making behaviour of their customers (for example, understanding flows of consumers from home postcodes to specific stores). Chapter 8 explores the use of GIS in retail analysis in more detail.

Around the world many transport companies are themselves increasingly using technology in addition to paper ticketing. These smart card systems are providing new sources of data to understand how people travel around on urban transit systems. Tao et al. (2014) show how these data can supplement census data and offer new insights into commuting – by linking space and time to show movements on a 24/7 basis. Figure 12.2 shows the data for the Brisbane Rapid Transit (BRT) system using this smart card data. Tao et al. summarise the main patterns:

For BRT trips, a number of large volume pathways from the northern, southern and western outer rings formed and persisted throughout the two days, suggesting that the BRT busway serves as a backbone corridor in channelling trips from outer suburbs (more notably in the north–south direction) to the CBD and surrounding areas within the Brisbane bus network.

SIMs (introduced in Chapter 8) are worthy of a mention here due to their use of flow information to understand and simulate population movements across a spatially subdivided surface. Indeed, some of the earliest uses of these models were for transport analysis and planning (see the examples in Foot (1981)). A matrix of volumetric measurements is derived for each possible eventuality of origin – destination flow (usually between centroids of the aforementioned spatial units), and these interactions are modelled by characteristics of source zone (possibly population affluence or socio-economic condition) and destination zone (usually an attractiveness feature such as perceived popularity of a shop or service), constrained by a function of the distance between these two points (a distance decay factor). Depending on the size of spatial unit chosen for such a model, it might be useful to a transport planner in terms of understanding trends of population movement.

SIMs have been covered in detail, in a retail context, in Chapter 8 and are therefore not explored further in this chapter. Instead, we focus on GIS-based network analysis for modelling and evaluating routing through a transport system.

Transport networks are the channels that facilitate the flow of people and goods and can be represented within a GIS by points (nodes of intersection or access) and vector lines. Despite the earlier discussion of flow patterns generalised to area-based interaction, these networks form the real connections between populations, origins and destinations, and can take a variety of forms such as roads, rails, footpaths, canals, etc.

Network links and nodes can be associated with a range of attributes that determine their function. The fundamental properties when considering routes through a transport network must be length and capacity. The length of a road or rail link could be allocated in terms of distance or time taken to negotiate at a set speed. The capacity feature might be extremely difficult to ascribe figures to, but could be represented in the example of roads by some function of road width, road lane count and speed limit (factors that could, in turn, be represented by a proxy such as road classification, e.g. motorway, A-road, B-road, etc.). For the example of public services such as rail, an analyst might consider the capacity of carriages and frequency of service as indicators of capability.

There is a vast range of applications in transport networking to which GIS and spatial analysis can be adapted. These operations lend themselves most readily to functions of network management and adjustments to current layouts, offering the additional advantage of analysing implications for network changes. Applications relating specifically to networks will be discussed in the following sections, which include a range of illustrative examples relating to the main transport networks of roads, rail systems and air traffic spaces.

The planning of roads in highly populated developed countries presents a difficult balance of conflicting interests. From the perspective of most end users the objective is a straightforward package of high capacity and minimised travel time. The planner, however, must account for factors such as sustainability, economics and environmental and social interests. A study by Rapaport and Snickars (1999) integrated all of these concerns in the application of GIS to road location for minimised environmental damage, building costs and travel time in Sweden. The environmental variables considered included salt damage, interruptions to surface water flow, greenbelt incursions, visual impacts and noise disturbance (some of which are subjectively determined or have complex relationships with traffic volume). Road building costs were calculated as a function of terrain (geological conditions and slope), current land-use type and level of disturbance to existing traffic during construction. Finally, travel time indicators used a simple application of distance and time which could be derived quickly and accurately in GIS software. These factors were analysed independently to identify possible routes for a new road system, revealing that each constraint produced an alternative optimum pathway. The constraints were then combined in a weighted linear function to derive the most suitable route under consideration of all factors.

At a more localised level, a GIS system might also prove useful for the management of network repairs which could be recorded in a database as attributes to a mapped system. Information along road networks such as power lines, water mains and other utilities information could all be recorded as separate layers with attributes to advise when maintenance is due, and what effects the changes might have on other network properties. This subject was researched by Khan (2000) in the design of a GIS for street lighting and traffic signal maintenance in Leeds, UK. In an ideal management system, such an advisory scheme might enable planners to manage repairs more efficiently in a spatial sense, whereby a maintenance team could be directed to fix other problems nearby to an ongoing work site (also see Meehan, 2007; Liu and Issa, 2012).

Websites such as the UK’s ‘Fix my street’ (www.fixmystreet.com) allow local residents to use a web-based GIS interface to report transport network repairs needed within their neighbourhood, such as potholes, broken traffic lights and illegal parking. Users can view the status of other problems reported in their area or submit their own report, which is passed instantly to the relevant authority. This is an excellent example of the use of GIS interfaces to promote public participation and community engagement within local issues influencing the transport network.

We will now turn to the issue of pollution and a potential use of GIS and spatial analysis in its management. In a simple application of GIS, Mavroulidou et al. (2004) used mapping software to visualise simulated patterns of pollution vulnerability and contributory factors in an area surrounding Guildford, UK. Data including traffic volume, wind, topography and building density were combined through the overlay procedure to examine how air quality was susceptible to change. These maps identify the areas adjacent to the main ‘A3’ road and in the centre of town as most vulnerable to poor air quality, plus a hot spot to the east of town where high traffic volume and complex terrain combine to increase pollution. The paper highlights the potential for planners to use this tool in highlighting areas with adverse conditions for pollution dispersal, and to identify routes where factors favouring pollutant dilution are greater.

In another application to the same field, Guldmann and Kim (2001) used statistical regression models to explain observed variations across urban areas in concentrations of ozone and carbon monoxide. Using a cell-based raster approach, their model considered pollutant concentration as a balance between inputs and outputs, using GIS and databases to spatially relate pollution measurements, meteorological factors, land-use characteristics, socio-economic data and major highway networks. The GIS hosted model allowed the researchers to assess impacts of land-use categories or major highways on particular emission concentrations by studying samples at buffered distances from these suspected sources, and comparing figures with background pollutant levels. All of the explanatory variables relating to transport were highly significant and suggested improvements of network capacity and flow at intersections as a vital means to reduce emissions.

GIS can also be used as an important tool for network planning in the context of transporting hazardous material. In one such example, Monprapussorn et al. (2009) use a GIS to assess the risks associated with transporting hazardous materials and hazardous waste by road in Thailand. Specifically, they considered the potential impact of a truck being involved in an accident while carrying these materials, identifying a number of potential social, economic and environmental impacts, in order to determine the optimum routing for these vehicles to minimise potential risk and impact (also see Lovett et al., 1997; Verter and Kara, 2001).

Route planning is an issue that is implicitly linked to networks. Devlin et al. (2008), for example, compared the actual routes taken to transport timber across Ireland with the ‘optimal’ routes suggested by GIS. The range of data that is required to consider options and plan events is well suited to a GIS, which can facilitate a variety of spatial queries. An excellent example of this application is in GPS & GIS routing of emergency service vehicles such as ambulances. In many countries, ambulances are now fitted with GPS navigation as standard and increasingly detailed mapping products can be used to route ambulances to specific addresses or other locations not covered by a geographic address. In the UK, Ordnance Survey’s Master Map Integrated Transport Network (ITN) data set can be used in conjunction with OS Address Point to route vehicles effectively to required locations. A study by Ota et al. (2001) confirmed the benefits of this approach through an analysis of emergency service response times in the urban environment. The first test in this research was to compare the progress of two ambulance teams, one with standard street maps and another with GPS navigator, in locating and routing to randomly selected residential addresses. With results taken from a total of 29 situations, the mean distances covered were 8.7 kilometres (GPS) and 9.0 kilometres (standard maps) with a mean travel time of 12.5 (GPS) versus 14.6 minutes (standard maps). The GPS & GIS equipped teams were faster in 72% of responses. The second test involved a qualitative assessment by emergency teams who were allowed to combine GPS & GIS resources with standard methods. Most providers noted that such technology enhanced their ability to navigate to destinations in areas that were unfamiliar. Such systems can be operated from a central control, where GIS systems can be used to relay maps and information on emergency conditions to response vehicles. These systems have the potential to receive and process more detailed information on traffic conditions to ensure responses are optimised.

GIS & GPS combinations can also be used by logistics companies to manage fleet operations, ensuring that delivery routes through networks and conditions of cargos are optimised. Toll Tranzlink, a New Zealand distributor, use an application developed by Vodafone to track and control their trucks in relation to 39 bases and 400 delivery locations. From a central control unit, the management team are able to monitor live information on location and time to destination, as well as on-board conditions such as capacity and temperature which are relayed by on-screen maps and attribute information. The claimed benefits of this system include a 55% improvement in on-time undamaged deliveries, more effective use of carrying capacity due to live monitoring, and greater efficiency due to real-time information provision and instant decision making. In 2011 it was widely reported in the media that US parcel delivery company UPS had implemented a new GIS-based algorithm to determine optimum routes for their fleet of delivery vehicles in the US. Delivery routes were optimised in order to minimise the number of left-turns made by drivers. Unlike right-turns, left-turns involve waiting to cross oncoming traffic – wasting time and fuel. This approach thus saves money and reduces emissions.

Many web-based services provide real-time traffic information alongside route-planning functions available free-of-charge to users. These services make use of an extensive road network database, coupled with real-time information on delays, traffic flows, roadworks, weather conditions and current traffic speeds in order to identify the most appropriate route to a destination. For the primary route network across much of the EU, the US and parts of China, Japan, Russia and Australia, Google Maps displays real-time flow speeds, along with the opportunity to identify average flow speeds at any given date or time, making use of a large spatially referenced database of historic traffic conditions.

Satellite navigation devices (e.g. TomTom) have become an important in-car accessory and almost all smartphones also feature some form of mapping and navigation device. These applications use route-planning software to help you navigate a particular route, taking account of real-time traffic flows. Traffic flow information is delivered to these devices in the UK using the ‘Radio Data System-Traffic Message Channel’ – this is an FM channel through which traffic information is broadcast to an agreed format which can be decoded by a range of devices. In the case of a satellite navigation system, the receiving unit will decode key information such as the nature and location of the incident, provide a visual/audible alert to the user and, most importantly, attempt to recalculate the route to find an alternative quicker route to the destination (see also the discussion on evacuation planning in Chapter 10).

In relation to the road network, GIS can be used to find the optimal location of support services. We have already seen this in relation to siting fire stations (Chapter 7) and hospitals (Chapter 9). In relation to road networks this could entail finding the best location for car parks based on current network usage and trip destination modelling or perhaps park and ride schemes. A nice example of the use of location-allocation models (discussed widely in Chapter 9) in transport GIS is provided by García-Palomares et al. (2012). They built a location-allocation model to find the best location for providing ‘bike stations’ (key locations where people can hire a bike or leave it once having finished their journey), based on the movement of bike trips through the city of Madrid. Figure 12.3 shows the results of the model for various alternative numbers of bike stations.

Lovelace et al. (2016) have been able to reveal the spatial patterns in cycling accident data in West Yorkshire in the UK, which are often masked by limitations of raster format. Of particular interest is the study of changes in accident occurrence and location over time, which has enabled the identification of possible causal factors; for example, a change in road or junction layout which might have positive or negative implications for accident probability. Figure 12.4 shows hot spots of accident data in West Yorkshire which helps to plan for change (note that this study has actually been undertaken using the computing package R and its analysis and GIS-style mapping capabilities).

Rodrigues et al. (2015) used a GIS-based framework in a case study in the municipality of Barcelos, Portugal. A set of road-related variables was defined to obtain a road safety index. The indicators chosen were: severity, property damage and accident costs. In addition to the road network classification, the application of the model allowed them to analyse the spatial coverage of accidents in order to determine the centrality and dispersion of the locations with the highest incidence of road accidents.

The key inputs for this product are the Post Office Address File (PAF), the Ordnance Survey’s definitive road survey product OSCAR and a detailed timetable of services that operate on these routes. Proprietary GIS systems such as ArcMap can be used to host the models’ output information, illustrating how the public transport network operates spatially to serve access to work, education, health and other facilities. Applications have been identified as follows: regular long-term monitoring of accessibility, guidance for land-use planning and development control, definition of the sustainable public transportation network and short-term accessibility audits. The data set and GIS can be queried for specific timetables and origin–destination flows. This enables users to identify optimum routes and a duration guide for journeys to work, trips to the nearest pharmacy, etc. The system can also be updated for changes to the network and could run scenarios to examine effects of these adjustments on travel time and accessibility. Table 12.2 identifies a series of accessibility targets set by Tyne and Wear Local Transport Plan (LTP) for the period 2006–2011 identifying, for example, that in 2004, 87% of households were within a 30-minute journey by public transport to their nearest hospital – with a target of raising this to 91% by 2010.

Around the world, there are a number of accessibility packages that can be used in conjunction with GIS to produce standard access scores (e.g. see Liu and Zhu, 2004; Miller and Wu, 2000). Liu and Zhu show their outputs for Singapore: powerful 3-D maps representing accessibility surfaces in this instance (Figure 12.6).

Finally, in relation to accessibility and transport GIS, we should note an interesting set of papers that estimate accessibility to jobs in various locations for various modes of travel: car, bicycle, walking, etc. (Mavoa et al., 2012; Wang and Chen, 2015).

As noted above, the second major use of GIS in public sector transport management is to help in the design of new systems: whether they be new bus routes, bus lanes or light transit systems. Often these studies are part of much wider urban and regional planning projects. The building of a new light rail system, for example, is not simply about route planning. Planners and engineers would normally consider the demand and route planning first, but only as a component of a much larger study around economic costs and impacts. This might involve a systematic appraisal of current road and public transport use, current journey to work patterns and land-use costs and availability. There is certainly a wealth of studies in economics and regional science which have explored the impacts of new transit routes not only on traffic movements, accessibility patterns and congestion, etc., but also on job creation, house/office prices and revised commuting patterns. Some, or all, could be done within a GIS environment but the detailed land-use/transportation modelling takes us beyond the scope of this book (although the interested reader could take a look at Wilson (1998) for a general review and Vuchic (2005) in relation particularly to urban transit developments).

This desire to produce an optimal mix of lower costs and maximum accessibility has also led to the design of many sophisticated optimisation techniques, especially in the field of operational research. Such optimisation procedures might use genetic algorithms for example to find optimal station locations (for a good introduction to these approaches see Laporte et al. (2011) and García-Palomares et al. (2012)). Having helped to design new routes, GIS can also help add new infrastructure around these routes. Horner and Grubesic (2001), for example, used GIS to calculate an index of demand within the catchment area of the rail stations in Columbus, Ohio, US. Each station was a potential candidate for a park and ride scheme. They produced a type of suitability index to rank and compare potential park and ride locations. Of course, the evaluation of transformations in public transport networks does not always shine a positive light on such changes. Blair et al. (2013) used a variety of GIS access type indicators to evaluate the major revision to bus routes and times in Belfast in Northern Ireland during the mid-2000s. They showed the negative impacts on a variety of user groups, especially those in low-income areas.

As with the road network there are many studies of GIS in relation to freight movements, pollution and carbon footprint analysis. Zuo et al. (2013), for example, built a spatial flow model of the transportation of aggregates across the UK in a GIS, for both road and rail. They were then able to estimate the carbon footprint associated with travel by both modes and test a series of what-if? scenarios for trying to reduce those carbon footprints (i.e. impacts of building new high-technology quarries or increasing the amount of aggregates transported by rail).

Also, as with the examples of roads above, other examples of real-time management for flows and networks can be seen in the case of bus and train/tram services in many cities, where computer aided dispatch and passenger information systems have been introduced. Mobile technology and GPS tracking enable synchronised tracking of vehicle location, electronic displays at bus stops which indicate when the next bus will arrive and the relaying of service information to customers by mobile telephone text messaging. In many cases, intelligent bus priority traffic information systems are also used, whereby bus location and distance to the next signalised junction will be used to control traffic-light sequences for quicker public transport flow. A range of examples of these projects from around the world can be found on the ‘Innovation in Traffic Systems’ website (www.initusa.com/en/index.php).

Finally in this section we look at how GIS has been used in a more limited number of studies relating to air travel. First there are a number of studies that provide a similar decision support mechanism to the facilities management systems described for roads. Using GIS and GPS a number of so-called airport pavement systems have been created. McNerney (2000) reported that 60% of US airports use these systems to keep track of the condition of runways and taxi areas, often replacing the old vehicle-based inspection services (also see Shwartz et al., 1991; Chen et al., 2012).

In this section we explore the use of GIS and location models for the optimal locations for transport-based services such as ambulance or fire stations. These services are called ‘outreach’ services, in the sense that they come to their clients, rather than the other way round. The models are location-allocation models, introduced in Chapters 4 and 9. Figure 12.9 plots the location of ambulance and fire stations in London in 2012. It is immediately apparent that there are ‘open points’: areas that are not as well served by these services. Thus it is not difficult to estimate where the worst place to live in London is, should there be a house fire (i.e. the location which is furthest from the nearest fire station).

As emphasised throughout this chapter, flows as demand structures and networks as movement facilitators are the underlying components of transportation. In the urban environment these factors are subject to continuous change due to variations in population pressure and the erratic nature of personal journeys. The examples of GIS in this chapter have demonstrated, however, that transport systems can be understood and effectively managed by thoughtful consideration of relevant data sources and the understanding of interactions that occur in our cities and regions. Moreover, the application of suitable software and modelling environments can be used to run controlled simulations of scenario changes such as new origins, destinations and network links or changes to access policy. These opportunities should serve the transport planner well in the ongoing pursuit of more efficient, reliable and serviceable urban transport networks.

We should finish with recognition that many applications of transport planning involve large-scale research projects which might actually link many of the components we have discussed above. The final example in this section is a research project that attempts to bring together many of the elements discussed in this section through the development of a GIS-based decision support system for planning urban transport policy. Arampatzis et al. (2004) developed a tool to assist transport administrators in attempts to improve transport efficiency while monitoring environmental and energy indicators. Transport network analysis is facilitated by a road traffic simulation using traffic flow patterns (based on known network characteristics and traffic demand) for each link in the system. This information is supplemented by energy consumption and pollutant emission calculations when combined for data hosting and visualisation in a GIS environment. The main analytical use of GIS comes in the form of case studies for policy change, regarding vehicle access and parking restrictions for the centre of Athens. More specifically, the scenarios study example implementations of an area traffic restriction on the use of private cars in the Municipality of Athens, and a simulated 50% reduction of parking places in the central town. The model outputs for these scenarios include implications for other transport modes (taxis and other public transport), estimated average vehicle speeds, estimated fuel consumption and air pollutant emission levels. The tool is heralded as ‘a GIS-based decision support system that involves realistic representation of the multi-modal transportation network and efficient implementation of network equilibrium solutions for problems related to the application of urban transportation policies’ (Arampatzis et al., 2004: 475).

We looked at some of these bigger decision support systems for transport planning in relation to GIS for emergency planning in Chapter 10. We hope that this chapter has served as an introduction and overview of the literature and range of GIS-based applications in the modelling, planning and evaluation of transport systems.

This chapter is accompanied by Practical 8: Transport planning. The practical gives you the opportunity to assess the provision of public transport in relation to commuter flows (journey to work) in the city of Chicago. Specifically you consider the opportunities to introduce new cycle hire stations to promote commuting by bike. Practicals D: Network analysis and 6: Emergency planning are also related to the network approach outlined in this chapter.

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