david pritchard. bibliography.

Notes on Fotheringham and Wegener, Spatial Models and GIS [16]

Chapter 2 [15]

The potential sensitivity of the results of the modelling exercise to the definition of the spatial units used is commonly referred to as the modifiable areal unit problem (MAUP) or the zone definition problem (ZDP). Reviews of the general problem are provided elsewhere [36,17] and there have been investigations, although rather inconclusive, of the problem in other types of spatial modelling. [p. 27]

Chapter 3 [14]

• Contemporary spatial theories have led to the emergence of two major schools of analytical thought: the macroscopic school based upon probability arguments and entropy-maximising formulations [53] and the microscopic one corresponding to a behavioural or utility-theoretic approach (for an overview see [3]). [p. 34]

• Maximum likelihood and simulated annealing methods are used
• GIS is mostly used for display; "proprietary GIS are generally so inflexible that their functions cannot be easily dissembled from the packages" [p. 40]

Chapter 4 [47]

• They took aggregated land use data and rasterized it (with some probabilistic spread) to generate microscale land use data [52]
• They then overlaid a vector transport network for transport/land use analysis
• They used this to model carpooling behaviour, using disaggregated synthetic data to define work trips, and a behavioural model to define how pooling took place.
• They also analysed the rail networks to determine the accessibility of different parts of Europe, using disaggregated raster population data and the rail transport network data [46]. Very interesting.
• Some other work [51]
• They view probabilistic synthetic microdata as the solution to the MAUP problem.

Chapter 5 [32]

• GIS offers urban modelers a data structure and a set of data manipulation tools through which they can explore the spatial processes inherent in urban activities. What are spatial processes, and how can they be measured?

  Spatial processes are those processes by which activities at one location affect or are affected by activities at another location. Haining [23] pp. 24-26 and Krieger [27] have identified four types of spatial process that arise in urban activities:

  1. Spatial diffusion and dispersal occur when one or more attributes spread gradually over space, usually affecting nearby agents first. This type of `spreading-out' dynamic is common to many urban processes, including, for example, metropolitan job and population growth, neighborhood succession and transportation congestion. The mechanisms underlying diffusion and dispersal tend to be complex and vary by activity.

  2. A second type of spatial process involves exchange and transfer. This process dominates many types of human activity, especially economic activity. Location may affect both production and consumption decisions. Prices may be spatially differentiated, reflecting either differential transportation costs, or location-dependent input costs. The spatial proximity of competitors may enhance competition; a lack of nearby competitors may reduce it.

  3. A third type of spatial process can be termed interaction. Spatial interaction involves movement and/or communication over space. The nature of the interaction is determined by the characteristics of the various sets of origins and destinations, as well as by the spatial separation between the two sets. Through spatial interaction, activities at one location may be influenced by activities at other distant locations.

  4. Lastly, spatial segmentation or percolation may occur when a homogeneous spatial entity separates into two or more parts of a qualitatively different character. A variety of factors—agglomeration, division of labor, multiple equilibria, racial prejudice—can give rise to spatial segmentation. Spatial segmentation can also arise as a consequence of random or stochastic processes [27].

  These four processes are not necessarily mutually exclusive: they may occur simultaneously or even in opposition to each other.

  To model spatial processes one must collect spatial data, or at least collect data that includes spatial attributes. Traditional data collection and representation methods, unfortunately, tend to obscure or distort the spatial nature of attributes. The practice of collecting socio-economic data at an aggregated zonal level, for example, treats all events within a particular zone as spatially homogeneous. This tends to minimize intrazonal differences while exaggerating interzonal ones. The representation of data in two-dimensional tables or matrices—another common practice in urban modeling—often requires the casting aside of spatial characteristics. Once lost, such characteristics are difficult to reintroduce.

  Advances in GIS data representation provide a technical basis for overcoming some of these difficulties, but to take full advantage of the richness of spatial information represented in GIS, geographers and modelers must rethink how they extract spatial measurements from maps and other sources of spatial information. This section discusses how to use existing GIS analysis functions to operationalize four types of spatial measurement [...]:

  1. Distance is certainly the most common spatial measurement. Generally regarded as a measurement of spatial separation, the concept of distance can also be used to represent the spatial relationships between two or more locations [18]. Distance can be measured in three ways: (a) as Euclidean, or straight-line distance, (b) as route distance along a transportation network such as freeways or local roads, or (c) as minimum-path distance among multiple routes. Euclidean, route and minimum-path distance are commonly used in used in urban activity and transportation models. Manual measurements of distance—even simple Euclidean distance—are cumbersome for even small numbers of objects. For large numbers of objects, the time and cost associated with manual distance measurements is usually prohibitive. GIS automates such measurements. Similarly, although the precise algorithms for finding the minimum distance between two nodes along a network have been available for a long time, GIS allows such measurements to be made in a more efficient and flexible way [specifically, from pre-coded street files such as the US Census Bureau's TIGER files rather than from a specially developed and coded transportation network]. [...]

  2. While distance is commonly incorporated into almost all spatial models, the use of adjacency is not so widespread. Adjacency can be narrowly defined as spatial objects being right next to each other. A broader definition would regard all objects within a small distance as adjacent. Establishing whether two or more features are adjacent is essential for the identification of spatial autocorrelation [11]. Determinations as to whether two or more objects are adjacent are usually made on a case-by-case or ad hoc basis. Measuring all possible adjacency conditions, and then including such measurements in models becomes progressively more difficult as map objects become more numerous or more spatially complex. Only with the assistance of GIS can such measurements—in terms of both the broad and narrow definitions—be acquired for complex spatial layouts. Adjacency analysis can be operationalized by writing GIS routines to (a) automatically select each spatial object, (b) create buffer zones of user-specified or variable width around each object, (c) overlay the buffer zones with an attribute layer of interest, and (d) manipulate the attribute data tables resulted from the overlay. Some existing GIS packages have the capacity to handle all four of these operations.

  3. A third proposed spatial construct involves the measurement of a spatial intervening-opportunities gradient. The concept of intervening opportunities was first proposed by Stouffer [48] as a way to model flows from an origin to a destination. Yet the concept of intervening opportunities goes far beyond traffic flows. Patterns of urban development, for instance, are largely a function of the availability of usable sites. The likelihood that a specific site will be developed will vary according to its own availability for development, but also according to the availability of other sites located at different distances from various activity centers or generators of demand for development. Using GIS, one can easily construct an intervening-opportunities gradient that measures usable land supplies at varying distances. Such procedures involve (a) generating a series of rings or buffers of specified width at increasing distances from a location of interest, (b) overlaying the resulting rings on a second layer of interest, (c) determining what number, share or area of the second layer falls within each ring, and (d) adding up the various areas, shares or numbers to calculate a gradient. Although conceptually simple, these procedures are operationally cumbersome. GIS greatly streamlines this process.

  4. A final spatial construct involves the use of GIS to disaggregate space into characteristic entities such as polygons or grid cells—disaggregate spatial units. Without the support of GIS, traditional transportation and urban models typically divide a metropolitan area into zones, with all data aggregated at the same zonal level. Since objects within each zone are treated as identical, all intrazonal processes are ignored. More importantly, information regarding individual locations or activities is lost, making finer-grained modeling or simulation impossible. GIS renders such zonal aggregation unnecessary. In particular, with multiple layers of data, the overlay functions in GIS can identify the intersections between different layers and thus disaggregate large regions into smaller units, each carrying its own unique features. In this way, through the use of disaggregate spatial units, fine-grained spatial or attribute information need not be lost or averaged through aggregation [47].

  [pp. 64-66]

• They did a very interesting study [31] of the effects of transport accessibility on home prices. They estimated the value added to a home by proximity to a BART (San Francisco Bay Area Rapid Transit) station and to a freeway interchange, as well as the reduction in value directly adjacent to BART and the freeway, using hedonic price models.

  Hedonic price models are used to decompose the transaction price of a composite bundle of goods or services (such as housing) into its component or implicit prices [45]. Hedonic models are estimated statistically, usually using some form of linear or non-linear regression. When applied to housing markets, the dependent variable is usually the observed transaction price. Typical explanatory variables include (1) the physical attributes of the property (for example, lot size), (2) supply and demand conditions in the property market (for example, vacancy rates), (3) financial characteristics of the buyer and/or seller, and (4) locational or spatial attributes of the property. [p. 67]

  In Alameda County, for every meter closer a home was to the nearest BART station (measured along the street network), its 1990 sales price rose by US$2.29. For every meter it was closer to a freeway interchange, the price declined by US$2.80. In Contra Costa county, the respective price changes were +US$1.96 and -US$3.41. Proximity to the BART line/freeway (within 300m) had no statistical effect.

• The authors also investigated a land use and development model for regions surrounding BART stations [30]. Their model did a reasonable job of explaining a range of factors, but found that distance from the BART station had no effect.

• Finally, the authors worked on the California Urban Futures project [29], using GIS to make land-use development decisions under a range of policy scenarios.

Chapter 9 [25]

• The current limitations of GIS in providing a dynamic representation of spatial phenomena at a micro level is only one obstacle to this [spatial dynamics modelling] development. The major challenge is to formulate a conceptual framework and to integrate theories of individual actors' behaviour, micro-level interaction and space/time constraints in order to model spatial micro-level dynamics. Another difficulty is to generate and analyse empirical data of micro processes in order to specify and calibrate dynamic spatial micro models. [p. 144]

• The conceptual ideas behind the microanalytic modelling approach were originally developed by Guy Orcutt in the 1950s [37]. During the following years he and three other scholars—Greenberger, Korbel and Rivlin—tried to put these ideas into practice by developing an operational model. Four years later, in 1961, they issued a book in which the results of their efforts were presented [39]. From there on many microanalytic models have been designed and executed. [p. 145]

• The central feature of the microanalytic approach is the identification and representation of individual actors in the socio-economic system and the way in which their behaviour changes over time. The modelled decision-making units might be nidividuals, households, firms, banks, corporations and so on. The shift of focus, from sectors of the economy to individual decision-making units is the basis of all microsimulation work. Knowledge about individual behaviour, other actors and decision-making units is integrated into the model, and the consequences of many individuals' behaviour or responses to external influences are explored [28]. Major advantages of microsimulation models are the possibility of incorporating individual behaviour and micro processes in the models and to apply theories of individual behaviour. Moreover, the hetereogeneity of information can be fully represented in the model and maintained during simulation. The output will consequently contain a great variety of information about general and specific conditions at the micro level, information that can be easily aggregated to the levels suitable for answering research and applied questions. This facilitates a detailed analysis of micro processes or sequences of individuals' actions and provides opportunities for a more thorough understanding of the mechanisms behind the macro processes and of the consequences at aggregate or disaggregate levels.

  There are limitations to the approach. Irrespective of the sophistication of the method, an appropriate data set is required to provide valid projections. Attributes of the decision units relevant to the problem in question are needed. Since the objective of many microsimulation analyses is to study social processes, the requirements of the data set go beyond a single cross-section. It should rather be based on frequent observations at several points in time in order to facilitate the estimation of transition probabilities based on dynamically tested behavioural hypotheses. This kind of data is rarely available which has compelled researchers to turn to alternatives such as surveys/samples and synthetic data. In most countries the population census is the only source of micro data. The methodology of synthetic sampling is aimed at linking different series of separate tabulations by means of the conditional probability analysis. The obtained set of probabilities is used in the next stage to create a sampe of micro entities which is the foundation for microsimulation modelling (see [8] for a through presentation of the principles).

  Another problem related to microsimulation is connected to the immense need of computation capacity and, in turn, hardware and staff resources [28]. These types of model tend to become rather voluminous and engage numbers of researchers with different specialities. This is due not only to the ambitious objectives of the model but also to the interdisciplinary character of the modelling approach. However, it should be stressed that microsimulation models and traditional models have quite different `size functions' in terms of data storage and computation. Microsimulation models may have a larger initial size threshold but, as more variables are incorporated, they grow additively, whereas aggregate models increase multiplicitavely.

  Microanalytic models are usually divided into two main categories: static and dynamic. Static models contain no temporal dimension. They cannot, for example, be used for analysing changes in demography or the indirect impact of policy changes outside the directly targeted behaviour or for long-term distributional effects. [...] At the policy level they are designed to answer questions such as: what are the (short-term equilibrium) effects on income distribution induced by changes within the welfare system? The major advantage of dynamic micro models is their capability to represent interdependent processes by ageing the micro units, i.e. by not having to rely implicitly on equilibrium processes without empirical justification.

  Microsimulation models are also divided into deterministic and stochastic models. The outcomes of deterministic models will be the same in every simulation, as the defined parameters provide fully determined relationships. In some cases, where there is a rich set of information on the micro units and where there are strong structural constraints, i.e. tax liabilities or subsidies, it may be advisable to use a deterministic modelling approach. However, in most cases the major problem is lack of knowledge. One possible way to cope with uncertainty is to treat it as random noise. In a stochastic model the outcome of different simulations will vary and make it possible to determine the impact of systematic parameter changes compared with the level of variation due to random processes. The random component can be justified in two ways:

  1. Random numbers represent information shortage and are used to trigger outcomes of partly unknown processes: a realistic heterogeneity in the course of events and in the combination of attribute values is thereby maintained.
  2. With stochastic models, unlike determinstic ones, it is possible to simulate many courses of events on the basis of given conditions.

  [pp. 144-146]

• Examples of models (see also [35,9]:
  • KGB [4]: static labour market model
  • DYNASIM [38] by Orcutt et al.: dynamic demographic and economic model.. Includes simple macroeconomic model for input.
  • CORSIM [7]: carrying on from DYNASIM, including individual and family demographic processes, including wealth accumulation.
  • HIPS [10]: population synthesis and dynamic simulation of demands on health system
  • HUDS [26]: dynamic disaggregate disequilibrium model of metropolitan housing system, with a zonal spatial representation (200 residential zones, 20 workplace zones, 70 000 households) and seventeen separate submodels.
  • IRPUD [49,52] by Wegener et al.: dynamic microsimulation of transportation and land use.
  • DYNAMOD [1]: another dynamic demographic simulator, coupled with a macroeconomic simulator. Uses unusual `survival function' for individuals' ageing.

• They discuss some time-geographic concepts, an important theoretical basis for current spatial microsimulation. Hägerstrand's work [19,20,21,22] is particularly relevant: he noted (obvious, self-evident) space/time constraints on individuals, e.g. people can only be in one place at a time, activities take time and need space, space is limited, mobility in space takes time, and so on. Time-geographic approaches have had difficulty modelling and developing a theory of human actions which goes beyond the effects of delimiting constraints [pp. 151-152]. They discuss one time-geographic model, HÖMSKE by Holm et al.

• They discuss in detail the TOPSIM I and upcoming TOPSIM II projects, as well as the future TOPSIM III project [24]. The first project replicated a macro-based demographic model using a micro formulation; the second will add new capabilities that can only be obtained through a micro representation, while the third will be a highly interdependent time-geographical microsimulation model. They identify "mobility and labour market clearing as the most crucial origins of errors in traditional regional modelling" [pp. 153-158].

• They make similar arguments about the poor suitability of GIS for spatial dynamic modelling; while GIS is great at doing high spatial resolution, it's awful for high temporal resolution, and interactions with external software via translation processes become painful.

Chapter 10 [13]

• An uninteresting discussion of microsimulation from a computer science (artificial intelligence) point of view. It's too bad I wasted time reading this. At least I didn't read the frickin' fractals chapter.
• MIRIAD team in LAFORIA (University of Paris 6) did some work on multi-agent systems [12]. The PARIS team of CNRS did some studies in dynamic geography, looking at settlement systems and urban dynamics [5]. A group at the Université Libre de Bruxelles studied public transport systems [40]. In the USA, the Santa Fe Institute for Non-Linear Studies has done interesting work in multi-agent systems.
• Lots of simple description of reactive multi-agent systems. The only interesting part is the perception of the agents, which restricts their spatial awareness to a small radius.
• He describes a cartographic generalisation project called SIGMA within LIFIA in collaboration with the COGIT team and the LAMA group. They implemented cartographic symbols as agents, and used agent interaction to organise the symbols, drawing analogy to a "social organisation" of the agents [2].

Chapter 12 [34]

• an interesting example of a dynamic simulation: TGSS (Time Geographic Simulation System) from the PARIS team of CNRS. They used a Houdini-esque block diagram to represent data flow, including differential operators etc. to provide feedback. It's a sort of model invention suite, where you can test hypotheses of how a model might be formed against actual data [p. 217-220].

Chapter 13 [42]

• They discuss the POLIS land-use model developed for the San Francisco Bay Area [41,6].
• History: most operational urban models derive from Lowry [33], which was the basis for ITLUP [43,44].
• Good overview: [50].
• Uses optimisation of an object function based on random-utility theory
• Zonal assumption (zones = municipalities); travel times are based on centroids of zones (!!)
• All sorts of crap related to ancient computers: 640k limit, etc. etc.
• Not much useful info for modern work.

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