The Scoping Review

What is a Scoping Review?

A scoping review is a type of literature review where the main goal is to map the key concepts of a research area, as well as examine the the types of evidence used and any gaps in the literature.

While it is similar to a systematic review, there are a few key differences. First, a scoping review takes a broad approach to reviewing the literature by trying to answer a relatively general question, while a systematic review tends to focus on a well-defined research question (Arksey, 2005). Furthermore, a scoping review attempts to represent all of the research from a specific field, whereas a systematic review will use only the best research to answer the question, and may largely ignore a large percentage of studies in favor of the ones that best fit the research question (Arksey, 2005).

Steps of a Scoping Review

1) Identifying a Research Question

Our study question is "What has been written about using remotely sensed data to do population counts on informal settlements?" The research question needs to be formulated first to guide search strategies in the second stage.

2) Identifying Relevant Studies

Our search strategy was to search three large online databases for relevant articles (Web of Science, Environment Complete, Academic Search Complete) using a selection of search terms determined with help from our faculty librarian which produced 453 articles. This is also the step where you develop inclusion/exclusion criteria.

3) Study Selection

Apply your inclusion/exclusion criteria developed in step 2. This is important due to the need for the search strategy to be broad enough to include all relevant research which in turn produces a number of irrelevant studies (Arksey, 2005.) If relevance is unclear at any stage, articles should be included in the next step. The first stage is to read article titles to determine eligibility (Archibald, 2016). Once article titles have been read, abstracts are read and inclusion/exclusion criteria is applied. If eligibility cannot be determined from the abstract, the full article is read.

4) "Chart" the data

"Charting" is a technique to sort material by key issues and themes (Arksey, 2005) and is useful for dealing with qualitative data. The goal is to extract the information necessary to answer the study question. Conducting a trial excercise followed by a group consultation can provide the team with an opportunity to reach consensus on what to chart (Daudt, 2013.) Our team chose to chart author, date, study location, data used, aim of study, and important results.

5) Report the Results

A scoping Review aims to provide a an overview of all material covered in a specific field (Arksey, 2005.) Researchers must consider the best approach to presenting the study findings to a reader. We chose to create a table of the study areas, data used, and methods used.

6) Consultation with Stakeholders

A consultation with stakeholders enhances a scoping review (Arksey, 2005) and should be considered a required component (Levac, 2010.) One option is to invite suitable stakeholders to be part of the research team, but not all stakeholders are suitable team members (Daudt, 2013)

A Scoping Review on Population counts of Informal Settlements

Identifying Informal Settlements

Informal settlements are officially defined as those that exist in urban areas without formal approval or documentation. Such settlements can contain thousands of dwellings, such as large slum complexes, or may consist of individual buildings built on otherwise legally owned land (Ioannidis et al. 2009, Snyder et al. 2013). Informal settlements are generally characterized by poor access to health services, limited access to education facilities, inadequate infrastructure, uncontrolled population densities, and they are often built in unsuitable and sometimes dangerous environments. In South Africa, around ten percent of the population live in informal settlements, while in Cape Town specifically, about twenty percent of households live in informal structures (Lehola 2012).

Surveys

The simplest way to determine dwelling and population counts is through on the ground survey of informal areas. Urban informal settlements are often underrepresented in the national censuses, but in cities such as Rio de Janeiro and in Pune surveys have been conducted in order to assess informal populations (Joshi et al. 2003, Snyder et al. 2013). If survey data for a given study area does not exist, one must either conduct one's own survey or rely on alternative methods in order to determine an informal dwelling population approximation.

Manual Counts

Sometimes a study area is small enough to facilitate a manual count of dwellings and the determination of population based on previous survey data (Chen et al. 2003, Checchi et al. 2013, Kakembo and van Niekerk 2014). While the strategy of manual counts is very simple and could potentially be conducted by non-specialist volunteers, it is not practical for larger study areas and in such cases would be very time consuming and most likely unfeasible.

Population Growth Models

Population growth models are created from historical data to forecast the changes in land cover at any given year. These models can be applied to residential land cover imagery to determine an approximation of population based on the amount of pixels residential land takes up. Many different growth models are available for a variety of objectives, constraints, and assumptions (Olena et al. 2011). The typical approach in choosing the correct model is through trial and error for the desired result (He et al. 2008). One issue with population growth models is that they do not account for changes in population due to immigration. This key assumption is inappropriate for informal settlements because the rapid growth of informal settlements is usually associated with immigration (Velijanovski et al. 2012).

Pixel Based Methods

Pixel-based methods work by assigning each pixel into different classes based on their spectral signatures. The user identifies the pixel class that is associated with residential dwellings and performs a count on the amount of pixels needed to estimate the total population. For this application, soft pixel classification is preferable to hard pixel classification (Alpin and Atkinson 2001). Pixel-based methods are best used when spectral and spatial resolution are high enough to distinguish between residential land cover and other land cover, but is still too low to distinguish individual objects.

Object-Based Image Analysis

Object-based image analysis has two main components that allow for population counts to occur in informal settlements. The first step is segmentation which involves breaking up the imagery into spatially similar segments. The second step is labeling these segments. Labeling of the segmentation only occurs in an unsupervised classification. An unsupervised classification means that the software will determine the classes based on algorithms and the user has to define these classes. A supervised classification does not need segment labeling prior to segmentation as the classes have already been determined by the user.(Knudby, 2015) One crucial aspect when trying to perform object-based image analysis is the quality of data. Throughout the studies analyzed, the importance of data quality was emphasized. The high quality data was obtained from relatively new sensors such as IKONO or QuickBird, which was high resolution (0.5 to 2.5 meters) and had at least three bands (R, B, G).(Veljanovski et al. 2012, Almeida at al. 2011.) Examples of object-based image analysis can be seen in other less developed countries including those Africa and South America. In these countries object-based image analysis was successful in determining population counts as well as land cover change in informal settlement areas. After reviewing the main object-based image analysis modules available to us, we were able to determine the most effective path to take to complete our goal.

Results

References

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