Concept of advanced decision-tree tool for selecting optimal participatory mapping method Jiri Panek1, Jan Geletic2, Vit Vozenilek3 1
Department of Development Studies, Palacky University in Olomouc,
[email protected] 2 Department of Geography, Palacky University in Olomouc,
[email protected] 3 Department of Geoinformatics, Palacky University in Olomouc, vit.vozenilek@upol.
Abstract Using geographic information systems in developing countries has been coined as an oxymoron for several reasons, mainly because of the historical burden of maps being used as a tool of control and technological dominance, largely by Western powers. Participatory approaches in mapping and GIS allow combining social responsibility and ethics with research and visualisation of local spatial knowledge. The main question of this paper is how should one choose the right method for participatory mapping or participatory GIS? The paper presents the introduction to the concept of an advanced decision-tree tool for selecting an optimal participatory mapping method based on the expert system First the paper brings short introduction to the participatory approaches in mapping and GIS. In next part paper outlines the different methodologies used with participatory mapping and participatory GIS. Third, the paper shares the best practices of selecting the ideal method for a specific community. Fourth, it suggests how advanced decision-tree and statistical tools can be used in the community mapping planning phase. Keywords: Participatory mapping, Participatory GIS, decision-tree, local spatial knowledge, expert system. 1
INTRODUCTION
Participatory mapping as an independent approach historically comes not only from Participatory Rural Appraisal, but also from all other visual methods of Participatory Rural Appraisal, such as matrix scoring, seasonal diagramming, Venn diagramming, ground mapping and it has been most widespread (Chambers 2006). This popular usage is, according to Chambers (2006), mainly due to the versatility and power of community mapping, the relative ease
with which it can be facilitated and the fun, fulfilment and pride which people derive from it. As the results of the survey conducted by authors suggest, the facilitation is crucial for all participatory methods. Creating their own community maps has an empowering effect on the participating members of the community. It gives people the opportunity to think spatially about their environment and the process of creating a community map, it triggers feelings of being literally put on the map, belonging to the community and a sense of ownership of the empowering process. The sense of ownership sparks empowerment and actuates the momentum for sustainable development — driven and run by the community as it comes from within the community (Vlok and Pánek 2012). Maps have the burden of being tools of colonial powers, because as much as guns and warships, maps have been weapons of imperialism for centuries (Harley 1988). Also carrying this burden, geographic information systems (GIS) were introduced to the world of development discourse. Many critics (Abbot et al. 1998; Dunn 2007) of GIS were concerned that geospatial technology was being employed with the explicit goal of expanding political and economic control over those already disadvantaged by local, regional, and global divisions of power. They were therefore afraid that GIS was still only a tool of control and technological surveillance (Pickles 1995). There was growing concern among academia at that time, that GIS may become a tool of techno-neocolonialism. With the rise of GIS in general, development workers started exploring the practice of using georeferenced mapping within their practices as well. Participatory GIS (PGIS) emerged from participatory approaches to planning and spatial information and communication management (Dunn 2007). As a term, PGIS came to express the adoption of GIS in order to empower indigenous and local communities in their daily lives. It represents the vision of GIS practitioners who have developed an interest in the socio-political contributions and implications of the technology and its ability to empower less privileged groups in society (Abbot et al. 1998; Pickles 1995). With the process of democratisation of cartography (Rød, Ormeling, and Van Elzakker 2001) and democratisation of the GIS (Butler 2006) people started using maps and GIS in more participatory ways than ever before. Nevertheless, the most important question still stays unanswered: How should one choose the right method for participatory mapping or participatory GIS? The decision about the optimal participatory mapping method depends mainly on human decision-making skills. These can be acquired by years of experience, but in the fast-growing and quickly developing sector of GIS this may not be good enough. The decision-tree models (Magerman 1995), sometimes called decision-
tree classifiers (Safavian and Landgrebe 1991) are one of the possible approaches for multistage decision making, where the basic idea is to break up the complex decision into a union of several simpler decisions, in the hope that the final solution obtained through this method will be the desired one. Decisiontrees are used successfully in many diverse areas such as remote sensing, expert systems and speech recognition (Safavian and Landgrebe 1991). This paper aims to answer the main question “How should one choose the right method for participatory mapping or participatory GIS?” and the authors are testing if the advanced decision-tree tool is the answer to this question. Much of the work in this paper depends on the ability to replace the human decisionmaking process with automatic or semi-automatic algorithms. 2
WHY SHOULD PUBLIC BY INVOLVED IN MAPPING?
The use of participatory approaches in GIS and in mapping emerged from the critics of GIS in the 1990s. Community mapping is part of a more socially aware type of GIS which gives greater privilege and legitimacy to local and indigenous spatial knowledge. Variously labelled as Participatory GIS, Public Participation GIS, Bottom-Up GIS, Community-integrated GIS or Grassroots mapping these newer approaches are context- and issue-driven rather than technology-led and seek to emphasize community involvement in the production and/or use of geographical information (Dunn 2007). These approaches seek the human story behind the data and allow classical development workers to start using GIS within their work. Recent years have witnessed a burgeoning of applications of GIS which grant legitimacy to local spatial knowledge and by incorporating various forms of community participation these newer framings of geographical information systems are a response to the critiques of GIS which were prevalent in the 1990s. The main argument of “democratisation of GIS” is used to justify the legitimacy of PGIS and similar approaches inside the GIS context (Abbot et al. 1998). Critical to this inclusion of participation within the GIS is the need for PGIS scholars and practitioners to be more explicit about who “the public” is and what “participation” means, if appropriate goals are to be achieved (Schlossberg and Shuford 2005). In this paper the authors understand the “public” as a group of users, and a community, who are actively participating in creating and using spatial and attributive data, while that process of creating data is referred to as “participation”. Chambers (2006) as well as Amsden and VanWynsberghe (2005) or Corbett et al. (2006) mention how important it is for communities to be
the driving power in the mapping process and how much empowerment and involvement one can derive from it. One needs to be aware that the empowerment does not end with the map. The community's capacity to use the map for its own benefit is crucial part of the empowerment and development of the community. The Aarhus convention (1998) defines the public as one or more natural or legal persons, and, in accordance with national legislation or practice, their associations, organizations and groups. The scale of participation, or what participation means is not defined by the convention, but one can find a vast amount of participation models and ladders such as Connor (2007) and Sherry (1969). 3
ADVANCED DECISION-TREE TOOL FOR SELECTING OPTIMAL PARTICIPATORY MAPPING METHOD
Currently the human-decision making process is under the consideration of development workers, who solve this problem by either following certain methodologies provided by donors and development agencies or by following their own instincts. The development workers are accomplishing two critical tasks: identifying the features (community settings) which are relevant to each decision (selecting the optimal participatory mapping method), and deciding which choice (method) to select, based on the values of the relevant features (community assets). By assigning a classification to the possible choices, decision-trees provide a ranking system which not only specifies the order of preference for the possible choices, but also gives a measure of the relative likelihood that each choice is the one which should be selected (Magerman 1995). The following chapter describes what is needed to create such a decision-tree. 3.1
Data analysis
There is basically no common statistical analysis method for combining qualitative and quantitative data and this already comes from the substance of the data itself. Nevertheless, one can quantify the qualitative data by using specific questions and standardisation, which leads to the possible use of this data in the following analysis together with quantitative inputs. It is important to keep the analysis as complex as possible so the results can express a variety of variables, which should lead to an objective evaluation of the current state of the art as well as the potential development of the specific variables. In order to satisfy these needs it is possible to use multidimensional statistical scaling, which includes a large number of different algorithms and
methods such as Principal Component Analysis (PCA), Factor analysis, Cluster analysis and Discriminant analysis (Hebák, Hustopecký, and Malá 2005). The aim of the generalisation is to find the causal relationship between a dependent variable and independent variables, where we assume the effect on the observed variable. Depending on the complexity of the model we move from correlation analysis via Cluster and PCA analysis toward the sample analysis (Ševčík). The main method used in the model is Principal Component Analysis, which should help to select the most important variables while ensuring a reasonable level of validity of the decision-tree algorithm. Users will be asked to fill in information about the community and the tool described below will suggest the optimal method for the specific community. These inputs will be compared with results gathered during the survey. 3.2
Participatory mapping methods considered within the ARAMANI tool
The advanced decision-tree tool for selecting an optimal participatory mapping method is the complex algorithm which should be able to help development workers in the field to decide which participatory method is optimal for a certain community. The tool was named ARAMANI (Ambaye Ramani), meaning “Whose map?” in Kiswahili. The name is derived from the “Who and whose questions” arguments of Chambers (2006). Development workers can choose from various methods of participatory mapping and visualisation of spatial data. These methods of mapping can be selected based on their technical complexity or by advanced decision-making algorithms – such as ARAMANI. In the first testing phase, ARAMANI will work with the methods described by Corbett et al. (2006), Warren (2010) or Corbett, White and Rambaldi (2010). They are as follows: •
Ephemeral mapping – This is a straightforward mapping method that involves community members drawing maps on the ground from memory using any available materials, such as plants, rocks or household tools. The final product survives for a short time only.
•
Sketch mapping – Maps are free-hand drawings and are drawn on large pieces of paper and from memory. They represent the land from a bird’seye view and involve drawing key community-identified features. Outcomes do not rely on exact measurements and do not use a consistent scale or geo-referencing. One important factor is to show the
relational size and position of features as known and perceived by the map makers. •
Transect mapping – Transect maps or diagrams are spatial crosssections of a territory (terrestrial, coastal or marine) described by members of a community. Once completed, transect maps depict geographic features (e.g. infrastructure, local markets, schools), land-use types and vegetation zones; problems and opportunities observed or perceived along a transect line. Activities involve walking and mapping transects with the aim to cover as many of the agro-ecological, production and social groups along the defined route as possible.
•
Scale mapping – Scale maps present accurate geo-referenced data. Local knowledge can be gathered through conversations surrounding a scale map, which is then drawn directly upon the map (or onto acetate overlays, transparent paper or plastic sheets placed on top of the map (Pánek and Vlok 2013)) by knowledge holders. The location of features is determined by looking at their position relative to natural landmarks (e.g. rivers, mountains, lakes).
•
Photomapping – Similarly to Scale mapping this method uses an existing map as a geo-referenced background. In this case it is an aerial photograph of the study area (Vlok and Pánek 2012). While aerial maps have recently become easier to access and are often freely available, there are still places where they may not be available or where it will be difficult to obtain permission to use them. While aerial maps can be engaging, offering communities the opportunity to view large areas from a perspective that they may have never experienced before, this data may be difficult for some community members to relate to and these maps may not always depict information that is important to community members.
•
Participatory 3D modelling – Participatory 3D Modelling (P3DM) is a mapping method based on extracting topographic information (i.e. contour lines) from scale maps, and then constructing a physical model that is used to locate specific spatial knowledge. P3DM is increasingly gaining recognition as a method which goes beyond map making and enhances spatial learning, intergenerational knowledge transfer and community involvement in dealing with spatial issues related to territory. The sheer size and weight of the resulting models ensures that these remain with the communities that manufacture them.
•
GPS mapping – A GPS receiver is carried to a position in the field and used to capture an exact location on the earth using a known coordinate
system such as latitude and longitude. Data are stored in digital format and can later be displayed on a map (such as a topographic base map). GPS devices are useful for accurately displaying specific points within a community, which are often more readily recognised by official agencies. •
Grassroots mapping – This method includes balloon and kite aerial photography capturing as a low-cost and easy to learn means of collecting aerial imagery for mapping, and introduces a novel opensource online tool for orthorectifying and compositing images onto maps.
•
Multimedia mapping – This approach is much closer to the traditional oral sharing of information, including spatial information. Maps are usually in the digital form embedded with audio and video information from the communities.
•
(P)GIS – GIS technology has long been regarded as complicated and costly and a technology that is primarily used by experts. Since the 1990s, the PGIS movement has sought to integrate local knowledge and qualitative data into GIS for community use. Within this category one can find Participatory GIS, Public Participation GIS, Bottom-Up GIS, Community GIS, etc.
Each of these methods can be described with certain variables called method settings that will affect the ARAMANI tool. These variables are described in section 3.3. 3.3
Data collection
In order to process the analysis about the optimal method, authors decided to ask various stakeholders (Academics in GIS, Academics in Cartography, Academics in Community Development, Fieldworkers in Community Development, GIS professionals, Cartography professionals and Local government representatives) to fill in the questionnaire about how should one choose the optimal Participatory mapping method? (Surveymonkey.com 2013). In the time of writing this paper, there were 152 participants in the survey labelling themselves mainly (60%) as GIS professionals or (30%) as GIS academics. Participants were asked to rate what do they think is important while working with specific participatory methods – these methods are described in section 3.2.
Each participant rated following “needs” by their importance respective to the method as “lowest”, “low”, “middle”, “high”, “highest” and “N/A”. The “needs” evaluated by the questionnaire were: • • • • • • • • • • • • • • • • • • • • • • • • •
Time available and needed for the mapping activity Money available Number of people you are working with Size and terrain of the mapping area Local restrictions and regulations Physical material available (non-electrical equipment) Electrical equipment (PC, laptop) available Internet connection available GIS data available GPS available Topographic maps available Facilitator’s geo-skills and experience Sustainability of maps (ability to record local knowledge for long time period) The output is readable/recognizable by authorities outside the community The output will be readable/recognizable by next generations Ability to transport the map to another place Ownership and the understanding of the community on the mapping process Historical experience with mapping within the community Colonial history of the community Land disputes within the community Land disputes between the community and the external actors (government, mining companies, etc.) Local power struggles within the community Knowledge of the local language (by research team/facilitators) Security and accessibility of the area Reason of the mapping activity
The summarised results of the questionnaire are presented in Table 1. For each question three highest and lowest answers are highlighted. One can see that “Time available and needed for the mapping activity”, “Facilitator’s geo-skills and experience” and “Reason of the mapping activity” are most important factors while selecting the optimal mapping method – no matter which method you are about to select. On the other hand “Internet connection available”, “Colonial history of the community” and “Electrical equipment (PC, laptop) available” are among the least important factors while selecting the optimal mapping method. One needs to be aware that both “Internet connection available” and “Electrical equipment (PC, laptop) available” become crucial while working with “Multimedia Mapping” and/or “PGIS” method.
Tab. 1: Aggregated results of the mapping methods survey.
3.4
ARAMANI tool
The ARAMANI tool (Figure 1) will incorporate both qualitative and quantitative input data. The qualitative input criteria will include information about historical experience with mapping, colonial history of the community, information about land disputes and local power struggles and the level of knowledge of the local language within the research team. The quantitative input criteria will scale from information about the size of the community, time and money available, to access to electricity, the internet and spatial data in digital form.
The ARAMANI tool will work with both qualitative and quantitative data, which will be standardised, and these data will be processed according to the specific methods' settings. The methods' settings will be derived from the participatory mapping methods mentioned above. As a result of this tool the user will get the most suitable methods for community mapping according to the variables selected. Each variable will also have its own weight according to the analysis of selected case studies. Principal Component Analysis incorporated as part of ARAMANI will advise users which variables are crucial for the right decision about the optimal method and which variables can be omitted. In the first phase the survey was conducted to monitor which factors are more or less significant for selected methods. This information will be considered as foundation of so-called expert system, which is a computer system that emulates the decision-making ability of a human expert and is designed to solve complex problems by reasoning about knowledge, like an expert (Jackson 1998).
Users will be asked to define their community, mapping situation and assets and based on the expert system and decision-tree ARAMANI will compare their answers with answers gathered in the survey and suggest the optimal participatory mapping method. A variety of related factors need to be considered in regard to how the tree is constructed, depending on the type of test and the nature of the data used to construct the decision tree (Brodley and Utgoff 1995). ARAMANI can be defined as a Hybrid Decision-Tree algorithm and the authors believe that this is the optimal classification algorithm, where the type of classification depends on the data set to be classified. If different classification algorithms are allowed within the framework of a single hybrid tree, the data set can be partitioned in such a fashion that the “best” classifier can be applied to different subsets of the data (Brodley and Utgoff 1995). Each model needs to be parameterised and trained. The results gained from ARAMANI will be compared in the testing phase and trained with the methods and settings used in selected case studies. Based on this comparison the value of validity will be calculated.
4
DISCUSSION
The commercialisation of local spatial knowledge is an emerging problem in communities all around the world. Users creating spatial and attributive data through the use of commercial tools such as Google Map Maker, are extending corporate databases, but are not able to freely use the very same data when they need to. Open-source and participatory mapping projects such as OpenStreetMap are designed to support community empowerment and to ensure that high quality data are available to everyone regardless of their origin, social status and position within the power structure (Pánek 2013). Participatory mapping is mapping by those (can) participate, thus it is not giving everybody an equal voice, and hence it cannot be universal. The crucial question remains to be answered; how should one choose the right method for participatory mapping or participatory GIS? The ARAMANI tool is designed to suggest the optimal participatory mapping method based on previous best practices from case studies, and needs classification of the specific community. The ARAMANI tool is here only to advise development workers on the method. Nevertheless, there is still a level of uncertainty in the process and, more specifically, each community has its own specific needs, which should be addressed accordingly.
Users, in this case development workers in the field, can always decide about the methodology based on their previous experience, but no community is at the same stage of development, with the same needs as another. There is a growing need for a universal tool that will both advise as well as assist in the process of selecting the optimal participatory mapping method. With the ever-growing number of case studies and methodologies development workers cannot keep track of all the latest methods and uses of GIS. ARAMANI is designed to advise and assist in this process. Using GIS as a tool for community building and community empowerment is an emerging practice. Local communities need to be aware of the possibilities and implications of using one of the presented participatory approaches to digital mapping. 5
CONCLUSIONS
This paper presented a blueprint for an advanced decision-tree tool for selecting an optimal participatory mapping method called ARAMANI, which is still under construction, and therefore it is crucial to discuss specific elements of this tool at the international level, in order to create a tool that will actually work for various communities all around the world. It is believed that GIS can be a positive and effective tool within current development discourse. Spatial information is no longer only combined with statistical data as it was with the first examples of spatial analysis for mapping the origins of cholera in Paris (De Châteauneuf 1834) and London (Snow 1855). Spatial information can nowadays be combined with feelings towards the environment as well as indigenous spatial knowledge. The authors of this article would like to invite academics as well as development practitioners to critically discuss elements of the ARAMANI tool to make it more useful and user-friendly. 6
ACKNOWLEDGEMENT
The paper has been completed within the project CZ.1.07/2.2.00/28.0078 “InDOG” which is co-financed from European Social Fund and State financial resources of the Czech Republic. Authors would like to thank reviewers for their valuable insight and advices.
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