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Multi-Dimensional Data Preparation: A Process to Support Vulnerability Analysis and Climate Change Adaptation

Agriculture is the backbone of a country’s economic system, considering that it not only provides food and raw materials but also employment opportunities for a large percentage of the population. In this way, determining the degree of agricultural vulnerability represents a guide for sustainability and adaptability focused on changing future conditions. In many cases, vulnerability analysis data is restricted to use by authorized personnel only, leaving open data policies aside. Furthermore, data in its native format (raw data) by nature tend to be diverse in structure, storage formats, and access protocols. In addition, having a large amount of open data is important (though not sufficient) to obtain accurate results in data-driven analysis. These data require a strict preparation process and having guides that facilitate this process is becoming increasingly necessary. In this study, we present the step by step processing of several open data sources in order to obtain quality information for feedback on different agricultural vulnerability analysis. The data preparation process is applied to a case study corresponding to the upper Cauca river basin in Colombia. All data sources in this study are public, official and are available from different web platforms where they were collected. In the same way, a ranking with the importance of variables for each dataset was obtained through automatic methods and validated through expert knowledge. Experimental validation showed an acceptable agreement between the ranking of automatic methods and the ranking of raters. The result of this study corresponds to 16 processed data sources ready to feed data-driven systems, as well as agricultural vulnerability methodologies.


I. INTRODUCTION
Agriculture is one of the activities most affected by climatic factors. It not only provides food and raw materials but also employment opportunities for a large percentage of the population [1]. Although agriculture contributes approximately 5% to 7% of GDP in modern economies, as this percentage increases, the economic system becomes more vulnerable. The effects of variability and climate change on food production are now a reality. These phenomena have begun to affect the production of the ten main crops (barley, cassava, corn, palm oil, rapeseed, rice, sorghum, soybeans, sugarcane, and wheat), which represent key food sources for human beings [2]. These food sources represent 83% of all The associate editor coordinating the review of this manuscript and approving it for publication was Kun Mean Mean Hou . calories produced on arable land, and for this reason, understanding how much can be affected has become an urgent task for researchers around the world. In this way, determining the degree of agricultural vulnerability represents a guide for sustainability and adaptability focused on changing future conditions.
Agricultural vulnerability has become a fundamental basis for analyzing the risks of climate variability. In recent decades, several studies have focused on analyzing and measuring this type of risk [3]. Fig. 1 presents the approaches around Vulnerability Analysis and Climate Change Adaptation published since 2010. This systematic mapping was developed using the methodology proposed by Petersen [4], where the databases of Scopus, Google Scholar, and Sci-enceDirect were consulted. 80 related works were found and classified into three topics: environmental, soil and crops, and water supply. We highlight those related to agriculture and food security below. In this sense, RHoMIS (Rural Household of Multiple Indicators Survey) is a methodology composed of surveys and databases to monitor the agricultural sector through food systems [5]. Likewise, the International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) is a network of economic, water, and related crop models that simulates national and international agricultural markets [6]. Following this line of research but at a regional scale, several approaches integrate physical, agroecological and socioeconomic indicators. These indicators were grouped into the components of exposure, sensitivity, and adaptability using a composite index method [7]. Finally, Agriculture, Vulnerability, and Adaptability (AVA) [8] is a methodology for calculating the vulnerability of productive systems in the upper Cauca River basin in Colombia through multiple key indicators.
These types of approaches collect and generate valuable information in workshops organized between different stakeholders. However, to obtain acceptable results there are some limitations that increase the complexity of the entire process. The first corresponds to the type of analysis (qualitative or quantitative) developed in these studies. Using a qualitative approach had several challenges, thus, most approaches are predominantly quantitative. The second refers to the difficulty in reaching an agreement between participants. Sometimes the panels of experts become unpleasant experiences by not reaching a consensus among the stakeholders. This leads to a third limitation which lies in the time required to implement the analyzes. The enormous amount of non-trivial work represents a high time of analysis.
The above implies having sufficient and relevant data sources for such analyzes. However, data in its native format (raw data) by nature tend to be diverse in structure, storage formats, and access protocols. There are often intrinsic spatial-temporal relationships between different data sources, which may offer relevant knowledge for a given information query [9]. This need is often unsatisfied due to data inconsistencies. If an adequate cleaning process is not applied, subsequent analyzes will not be accurate enough. In other words, a great deal of information and knowledge will be lost when entering erroneous data (''garbage in, garbage out'') [10].
Additionally, in many cases, the data used in this type of vulnerability analysis is restricted to use by authorized personnel. Consequently, although there is abundant information around a domain, its access will be restricted by the organizational policies of the data owners., leaving open data policies aside. Many times, it is not easy to obtain permissions to use such data, which leads to analyze other alternatives of access to information. In this sense, an important option is the use of open data, which also allows data analysis, development of predictive models, and enhance research results. The open data is usually published on the websites for free, accessible to the public, available in formats that allow its use, reuse, and exploitation without legal restrictions and under an open license. Even so, this type of open data requires a more rigorous cleaning process [11] and ensuring it represents an important aspect for agricultural vulnerability analyzes.
Data-driven approaches require systematic and structured processes to enhance the value of data and improve analysis in decision making. In these scenarios, an adequate guide to prepare data is of great relevance. Based on the above, having a large amount of open data is important but not sufficient to obtain accurate results. These data require a strict preparation process and having guides that facilitate this process is becoming increasingly necessary. Likewise, from an agricultural and environmental perspective, decision-makers need to maintain absolute confidence in these types of vulnerability studies. The above lies in the investment of resources, which is planned around these types of actors, therefore, they need to be guided by reliable information. Therefore, decisionmakers must be clear about the entire process to obtain the data that they will use in their analyzes.
Based on the above, we present a process for preparing data to feed agricultural vulnerability analysis. This approach highlights several key aspects such as the classification of data sources in different vulnerability dimensions. Besides, the use of open data from official entities allows increasing the reliability of the processed information. Likewise, the process validation is presented through a case study in a specific area of the Colombian territory.
The remainder of the paper is organized as follows. Section II describes the process for data preparation. Here three main phases and their respective components are highlighted. Section III presents an overview of a case study where the preparation process was applied. The following sections show step by step the preparation of data sources considering the case study as follows. Section IV: phase 1 -data sources evaluation, section V: phase 2 -data sources preprocessing, and section VI: phase 3 -variables prioritization. Finally, the conclusions and future work are shown in section VII.

II. DATA PREPARATION PROCESS
This section describes the process for data preparation proposed in this study. The main aspects of this process include VOLUME 8, 2020 a modular scheme and feedback obtained from the results of each phase. This is composed of three main phases: data sources evaluation, data sources pre-processing, and variables prioritization. These phases represent the process life cycle and each implies a level of refinement through collaborative evaluations between data experts in both data and other areas of knowledge. The task flow for each of the three main phases is shown in Fig. 2. The three phases are described in the following paragraphs.

A. DATA SOURCES EVALUATION
The main objective of this phase is to determine the availability of official information around the study area. Here, data are collected from sources with three fundamental characteristics: open, official, and public. These must be classified into groups corresponding to vulnerability dimensions. Subsequently, all sources should be described considering the provider entity and the main theme of the dataset. In addition, basic metadata is extracted, such as size, number of attributes, number of instances, time window, among others. Having consolidated these metadata, a preliminary analysis of all data sources is finally developed. Each of the tasks is described below.

1) DATA SOURCES COLLECTION
The use of open data is a key aspect of this study. In this sense, searching for data sources is not only a task-oriented to the web scrapping of official organizations on their websites, but also to the results of vulnerability studies developed in the study area. Raw datasets must be collected and subsequently, as the result of this step, classified in vulnerability dimensions. These dimensions must be defined according to the topics related to each dataset. In some cases, these are already defined in previous vulnerability analyzes of the study area.

2) DATA SOURCES DESCRIPTION
After the collection task, the data sources and their providers are described. The idea in this step is to collect the information that best describes a data source and the entities where they were collected. Here it is important to describe, in general, features such as data source identifier, data source name, entity name, mission, vision, area of influence, target population, data provided, among others. At the end of this step, a document with the description of all data sources is obtained.

3) PRELIMINARY ANALYSIS
This step is intended to provide an overview of all data sources. Each dataset has specific metadata that must be consolidated in a descriptive table. The metadata considered for this preliminary analysis were the following: identifier (acronym previously assigned in the description of data sources), format (original file type of the data source), time window (the total time period of sampling), number of instances (number of records contained in the dataset), number of attributes (number of variables sampled per dataset), time scale (sampling interval like daily, monthly, annual, among others), spatial scale (relative length, area, distance, and size of the sampling area), and size of the data source (number of bytes in the stored file). When the table is consolidated, comparative charts can be obtained from the collected metadata. In this study, we propose to use the number of instances and the number of attributes to determine the amount of data available. If there is not enough information at the end of this phase, the analyst can return to the data collection step.

B. DATA SOURCES PRE-PROCESSING
This phase focuses on identifying problems in the quality of data sources and applying different cleaning steps to solve them. Pre-processing this data includes an initial stage to define the steps to follow during the cleaning process. Similarly, identify the possible problems for each data source and apply the necessary steps depending on the problem to be treated. Finally, the results of this phase are the pre-processed datasets and a series of meta-features to determine the level of cleanliness in the data sources (before and after the preprocessing). This analysis allows refining the preprocessing in a new iteration. If necessary, collect new data sources to feedback the process. The steps of this phase are described below.

1) DATA CLEANING
This module aims to describe the possible cleaning steps required by each raw data source. To pre-process a data source, the steps presented in Fig. 3 must be executed according to the type of task to be performed (classification or regression respectively) [12]. It has to be emphasized that these tasks are executed depending on the dataset characteristics. The above implies that if the data source is labeled with a 87230 VOLUME 8, 2020 FIGURE 3. Data cleaning process for classification models. Quoted from [12].
target class, the entire task flow is applied. While an unlabeled dataset only the imputation, outliers detection, removal of duplicate instances, and dimensionality reduction steps are considered. Next, the cleaning task flow is described.
(i) Check missing values. It refers to reviewing the absence of values in an attribute that indicates a problem in the data source, e.g., defective measurements, sensor failures, incomplete surveys, or data transfer problems [13]. (ii) Outliers detection. An observation can be univariate or multivariate. This is called atypical value or outlier when it seems to be inconsistent for the remaining observations, that is, it deviates noticeably from others [14], [15]. (iii) Label correction. It consists in detecting those self-contradictory instances, that is, duplicate samples which have different class labels. This implies a lack of harmony between the elements of the dataset [16]. (iv) Class balancing. This task is applied when the distribution of a data set is unequal among its classes [17]. The accuracy and quality of learning tend to decrease if a dataset is unbalanced. (v) Removal of duplicate instances. Identification of instances and redundant attributes in the data sources which can negatively affect the performance of the models [18]. (vi) Dimensionality reduction. If a data source contains a large number of features, a future predictive model will decrease its performance by overfitting. By reducing the dimensionality, the number of features decreases without affecting the model performance [19]. As mentioned earlier, the previous cleaning steps are applied to raw data sources. These tasks are selected according to the quality of each data source. Here, software tools to support this process must be defined. There are several tools with different capacities and complexity of use. From a spreadsheet in Excel to statistical environments and specialized programming languages such as R or Python.

2) META-FEATURES ANALYSIS
After applying the cleaning steps to the datasets, checking the quality of the data is of great importance. In this sense, different metrics are used to determine adequate data distribution. These metrics allow us to establish a comparison of the quality of data sources before and after being cleaned.
These should be summarized in a table for all data sets. At this point, the analyst has three types of decisions. First, reapply the cleaning steps in case the data distribution has not improved. Second, return to the data collection step in case of insufficient data sources. If neither of the above two options is used, continue with the next and last phase of the process (variables prioritization). Next, the metrics used in this module are described.
(i) Data dimensionality. This refers to the ratio between the number of attributes and instances of a dataset [20]. (ii) Missing values ratio. Represents the proportion of missing values in all dataset values (attributes x instances) [21]. (iii) Duplicate instances ratio. This refers to the proportion of duplicate instances of the total instances in a dataset [22]. (iv) Mean absolute skewness. Skewness (S) measures the symmetry of data distribution. If S > 0, the distribution curve is tilted to the left. If S < 0, the slope is to the left. Whereas if S ∼ = 0, it approaches a normal distribution. Mean Absolute Skewness represents the average skewness of all the numeric attributes of the dataset [23]. (v) Mean absolute kurtosis. Kurtosis (K ) is a way to measure the flatness of data distribution. If K > 3, the distribution curve is considered Leptokurtic (wide/fat tails).
Absolute Kurtosis represents the average kurtosis of all the numerical attributes of the dataset [23]. (vi) Mean attribute entropy. Entropy is also known as a measure of impurity in a set of elements. Impurity refers to the distribution of the decision categories in the set. This set is pure if it is composed of the same class tag and is considered impure if it contains different tags in the same proportion. Mean Attribute Entropy represents the average entropy of all the nominal attributes of the dataset [23]. In this study, the maximum likelihood was the method used to estimate entropy.

C. VARIABLES PRIORITIZATION
The focus of this last phase is to determine the most important variables or attributes in each data source. This task is established through a ranking and the method to determine the importance of variables depends on the type of data source (labeled or unlabeled). In the last step, the ranking is validated through expert knowledge around the subject of each dataset. Through this latest expert evaluation, the datasets can be pre-processed again or new datasets can be collected. The two modules of this phase are described below.

1) FEATURE SELECTION
This step determines the prioritization process to be used considering whether a dataset is labeled or not. In other words, if there is a response variable (often called a label), in each data source. If the dataset is labeled, a supervised VOLUME 8, 2020 feature selection process is applied, otherwise, an unsupervised approach is used. The two approaches to feature selection used in this study are mentioned below.
(i) Supervised approach. It is the most common practice and there are three main categories of feature selection algorithms: wrappers, filters, and embedded methods.
In this study, we propose the use of the Random Forest (RF) and Logistic Regression (LR) methods to select the subset of the most representative variables [24]. Both algorithms are included in the embedded strategies which combine the advantages of filter and wrapping methods. These methods were selected considering that they are among the most popular machine learning algorithms. In addition, these are very successful due to high predictive performance, low overfitting, and easy interpretation. This last aspect is characterized by calculating the contribution of each variable to the decision in a simple way [25]. (ii) Unsupervised approach. These types of methods represent a greater challenge to determine the importance of variables, however, this approach can also establish some advantages. Expert criteria are not necessary to tag datasets and these methods can perform better even when no prior knowledge is available [25].
In the present research work, the process of selecting attributes in the unsupervised approach is guided by the correlation between variables of a dataset. Those variables with the highest correlation coefficient are considered more important in the data source.

2) EXPERT VALIDATION
From the perspective of the application domain, it is important to corroborate the results with the knowledge of experts. This would reduce the uncertainty of the predictive models applied in the future. In this sense, the whole process described in this chapter allows the feedback of the set of prioritized variables. It should be noted that no variable is discarded considering that in the future they may be useful for other analyzes.
To better explain the data preparation process, a case study is presented in the next section. In this case study, all the steps described above are applied to the southwestern region of Colombia and are aimed at vulnerability studies in the upper Cauca river basin.

III. DESCRIPTION OF CASE STUDY
The Cauca River is considered, after the Magdalena, as the most important source of surface water in Colombia. This is located in the western part of the country and approximately 41% of the Colombian population is concentrated in this area, which crosses from south to north nine regions or also called departments (Cauca, Valle del Cauca, Quindío, Risaralda, Caldas, Antioquia, Cordoba, Sucre, and Bolivar). This basin is located at an altitude of 900 meters (in the inter-Andean valleys between the central and western mountain ranges) up to 5.400 meters above the central mountain range at its source in the department of Cauca, and it has a length of 1.360 km ending in the Magdalena River in Bolivar. This zone is subdivided into three large regions: the upper, middle, and lower basins. The upper basin (which is the subject of this study) comprises an area of 3.031.488 ha (Fig. 4) and extends from the Macizo Colombiano to the boundaries of Caldas and Antioquia departments, between the peaks of the western and central mountain ranges. The study area comprises 191 sub-basins that flow into the Cauca River, with 99 municipalities and a population of more than 7 million inhabitants [8].
Approximately 20% of the national population benefits from the environmental services of this basin. Significant changes in this agricultural and industrial corridor imply great impacts on the ecosystems of the departments that compose it. For example, the small farmers who produce coffee in Cauca especially have an economic impact. At the same time, it affects commercial agriculture in Valle del Cauca, which is specially oriented to the intensive production of sugarcane and agribusiness of sugar mills. Furthermore, in the remaining departments of the upper basin, it has special importance in agricultural, livestock, and extractive economy production, like sand for construction and mining. Since the 1980s, this region has been a framework of environmental conflicts related to the expansion of the sugar agroindustry, mining, hydroelectric generation, and drinking water supply systems, among others.
The above aspects have been crucial to progressively analyze the vulnerabilities of the agricultural sector in this region. Indeed, different approaches have analyzed agricultural vulnerability from an integral point of view. One of them is the AVA methodology [8] that has two fundamental pillars: the first is to quantify and analyze the use of the territory and the production systems for the integral planning of agricultural processes, and the second focuses on sustainability and maintenance of the upper Cauca river basin. AVA allows analyzing agricultural vulnerability from four dimensions (biophysics, economic-productive, sociocultural, and political-institutional) which are described below. 87232 VOLUME 8, 2020 (i) Biophysics. It refers to a set of structures and relationships of the environmental context. It includes physicochemical and water quality analysis of lotic and lentic ecosystems existing in the upper basin of Cauca river, geomorphology, soils, vegetation cover, water bodies, ecosystems, and agro-ecosystems services corresponding to the previous elements. (ii) Economic-Productive. Information related to the agricultural production and the cultivated area, productivity of the agricultural systems, the municipal income that depends on the agricultural activity, and the processes of optimization of the agricultural resources (results of the national agricultural census). (iii) Sociocultural. Characteristics of a household, for example, those with female heads of household and older adults. Unsatisfied basic needs of the communities, literacy levels, and the risk of a population to suffer a natural catastrophe (semi-structured surveys). (iv) Political-Institutional. Evaluation of the institutional, administrative, and fiscal management capacity of public institutions at the local, regional, and national levels. Besides, the priorities and percentage of investment of the budget of the territorial entities, in subjects related to environmental sustainability and the agricultural sector. The data preparation process will be applied to the case study described above through three phases which will be presented in sections IV (data sources evaluation), V (data sources preprocessing), and VI (variables prioritization).

IV. PHASE 1 -DATA SOURCES EVALUATION
This section will describe the first phase of the data preparation process (Fig. 5), applied to the case study of the upper Cauca river basin.

A. DATA SOURCES COLLECTION
Considering the upper Cauca river basin as the study area of this research work, different data sources with public access were required as a fundamental input. After a first search process using a secondary data collection method, 16 data sources were identified and the respective datasets were downloaded to a local repository. Finally, the four dimensions defined in the case study were complemented with some of the SAFA indicators (Sustainability Assessment of Food and Agriculture systems) proposed by the Food and Agriculture Organization of the United Nations (FAO) [26]. Fig. 6 presents these data sources classified by dimension. The main source to find information initially pointed to the Web. Additionally, complementary information was extracted from the results of different vulnerability analyzes previously developed in the study area, in this case from the AVA methodology. The web pages of the main state entities were consulted for data extraction. In some cases, the data was available directly on the websites. In other cases, these sites contained links to more specialized web portals where information could be found. Datos Abiertos Colombia [27] and Archivo Nacional de Datos (ANDA) [28] are governmental web platforms that collect public data in Colombia from different sectors, without restrictions and for specific use in research projects. This highlights the use of official open data to support this study. The Open Data Barometer [29] is an indicator that aims to measure the prevalence and impact of open data initiatives in the world. In this sense, Colombia assumes an important role in the implementation of this type of policy. 12th in the ranking proposed by this organization and in third place in Latin America, below Mexico and Uruguay. Considering the classification presented in Fig. 6, the data sources are described in the next step.

B. DATA SOURCES DESCRIPTION
Once the data collection step has been developed, we proceed to describe them. A summary description is presented below, which groups the organizations and data sources by vulnerability dimension. (ii) CORPOICA (bp_corpoica). Corporación Colombiana de Investigació n Agropecuaria by its acronym in Spanish [31], is a decentralized public entity responsible for generating scientific knowledge and technological solutions through research, innovation, technology transfer and training of researchers, for the benefit of the Colombian agricultural sector. The data was obtained from the soil analysis service of CORPOICA for the agricultural sector. It focuses on evaluating soil fertility, salinity, and parameters to develop fertilization plans, application of amendments, and land adequacy to achieve profitable production. (iii) IDEAM (bp_ideam). Instituto de Hidrología, Meteorología y Estudios Ambientales by its acronym in Spanish. This is a public institution of technical and scientific support to the National Environmental System, which generates knowledge, produces reliable, consistent and timely information on the state and dynamics of natural resources and the environment, facilitating the definition and adjustment of environmental policies and decision-making by the public and private sectors, and citizens in general. The respective data set was obtained from a request to the IDEAM about the data of the climatic stations installed in the study area [32]. (iv) AVA (bp_ava). Interinstitutional and multisectoral analysis of vulnerability and adaptation to climate change for the agricultural sector of the upper Cauca River basin impacting adaptation policies. It is a project from the Climate and Development Knowledge Network (CDKN) [8]. The available data represents the percentage of municipal areas with climatic aptitude per crop.

2) DATA SOURCES OF ECONOMIC-PRODUCTIVE DIMENSION
(i) FINAGRO (ep_finagro). Fondo para el Financiamiento del Sector Agropecuario by its acronym in Spanish [33], is an entity that promotes the development of the Colombian rural sector, with financing instruments and rural development, that stimulate investment. FINAGRO grants resources to financial entities to encourage loans to productive projects. The data was obtained from the Agronet portal and these represent the total value of disbursements and the number of credits granted by FINAGRO and Banco Agrario to different types of producers for each department by the municipality during the selected analysis period. (ii) DANE-SIPSA-P (ep_dane_sipsa_p). One of the functions of DANE (Departamento Administrativo Nacional de Estadística by its acronym in Spanish) [28] is to provide basic information for decision-making in all sectors of the economy. SIPSA (Sistema de información de precios by its acronym in Spanish) [34] presents the wholesale prices of agri-food products that are marketed in Colombia. Additionally, information on inputs and factors associated with agricultural production and the level of the food supply in cities. Data about the area sown and harvested for different crops, as well as data on production, yield, costs, prices, gross income, profits, profitability, among others. (iii) Agronet (ep_agronet). Agronet [35] is the Information and Communication Network of the Agricultural Sector of Colombia, led by the Ministry of Agriculture and Rural Development and supported by the United Nations Organization for Food and Agriculture (FAO). This platform stores information from various official sources linked to the agricultural sector in a centralized way. By having data from official sources, Agronet provides reliable and updated information, according to the frequency of the investigations. The data collected represents the evolution of the harvested area, production, and yield in a period in a selected municipality. (iv) Minagricultura (ep_minagricultura). The Ministry of Agriculture and Rural Development (Minagricultura) [36] is a Ministry of the Colombian Republic whose main objectives are the formulation, coordination, and adoption of policies, plans, programs, and projects in the agricultural, fishing and rural development sectors. The data was obtained from the Agronet portal and corresponds to the evolution of the area planted and harvested, as well as the production and yield of different crops in a period in a selected municipality. (v) Agronet-P (ep_agronet_p). The data provider was Agronet, as described previously. The data represent the prices of different agricultural products and their respective variations in the international stock market [37]. (vi) DANE-SIPSA (ep_dane_sipsa). The provider of this dataset is the same as DANE-SIPSA-P described in this section. In this case, the data was obtained from a historical series of the wholesale quotations of the main agricultural products in the food basket [34].

3) DATA SOURCES OF POLITICAL-INSTITUTIONAL DIMENSION
(i) DNP-AIB (pi_dnp_aib). Departamento Nacional de Planeación (DNP for its acronym in Spanish) [38] is an entity that defines the implementation of a strategic vision of the country in the social, economic, and environmental fields. The dataset represents the Colombian climate finance strategy and methodology for the identification of budget executions in initiatives compatible with climate change [39]. (ii) DNP-FI (pi_dnp_fi). The data provider entity was DNP and the data is related to the forest incentive certificate is a recognition of the National Government through the Ministry of Agriculture and Rural Development to the positive externalities of reforestation in its commercial component [40]. This recognition is made by the Ministry through FINAGRO [33] by delegation. (iii) DNP-LA (pi_dnp_la). DNP was the data provider and the dataset refers to the progressive access to the land ownership of the agrarian workers and the use of the nation's uncultivated lands, giving preference to the allocation of low-income farmers and establishing farmer reserve zones for the promotion of rural property [41]. (iv) DNP-PA (pi_dnp_pa). The data provider entity was DNP. The data correspond to the access of small rural producers to factors of production such as land and labor, enhancing their use and complementing investment capacity through the direct support of profitable productive initiatives with a contribution from the project, a resource called Modular Incentive [42].

4) DATA SOURCES OF SOCIOCULTURAL DIMENSION
(i) DANE-HH (sc_dane_hh). The National Administrative Department of Statistics [28] applies different censuses to obtain data from several aspects of the population in Colombia, among them, the national agricultural census. The data refers to the number of households within each dwelling and the perception of subjective poverty and displacement directed at the head of the household or his spouse 43]. (ii) DANE-H (sc_dane_h). DANE was the data provider as previously described. The data source contains the number of dwellings, the occupation status of the same, the availability of public services, and the condition of the predominant materials [44].

C. PRELIMINARY ANALYSIS
This step is intended to determine the amount of information available for each indicator in the previously established dimensions. Based on the previous consideration, the analyst can determine if more information is required to continue the process or move on to the second phase which is presented in section V. Once the data sources were identified and classified, their main characteristics were extracted following the methodology proposed in [12]. Table 1 consolidates the following metadata: data source identifier, file format, time window, number of instances, number of attributes, temporal scale, spatial scale, and data source size.
As seen in Table 1, most of the datasets are in Excel's XLS format. This facilitated the initial conversion to CSV files which was the common format to start the cleaning process. Additionally, the predominant temporal scale was annual and the spatial scale was mostly at the municipality level. As mentioned above, each of these datasets is associated with a sustainability indicator. These indicators were selected taking into account their relationship with the type of information contained in the datasets. Fig. 7 presents an overview of the amount of data (percentage represented by the number of instances and the number of attributes) available per indicator in each dimension. In this way, the radar chart establishes a starting point for data preparation that will be described in the following sections. It is worth mentioning that collecting different data sources for all the indicators is a complex task, therefore this was supported by the judgment of several experts in each of the indicators. In this case, a greater amount of data was found for the following indicators: Land (biophysical), Local Economy (Economic-Productive), Agricultural Investment (Political-Institutional), and Human Development Index (Sociocultural). However, other indicators highlight the need to return to the step of collecting data sources. The above suggests an exhaustive search process to increase the percentage of data around these indicators. For this case study, despite having conducted a deep search for information sources, only some additional data were found which did not significantly increase the amount of data.

V. PHASE 2 -DATA SOURCES PRE-PROCESSING
In this section, we present the second phase of the data preparation process applied to the study case (Fig. 8). The main objective of this phase is to establish an evaluation of the main meta-features to determine the quality of the datasets once the cleaning process is finished.

A. DATA CLEANING
In any data-oriented process, there is a risk of finding duplicate information, missing values, data at different spatial-temporal scales, noise, among others. These considerations must be taken into account to achieve a successful VOLUME 8, 2020 data integration process. Hence, this section shows the cleaning steps which were described in section II-B-1. Different inconsistencies were identified in all data sources following  the methodology proposed in [12]; therefore, the cleaning steps were applied to each of the 16 data sources which are presented in Table 2.
To illustrate a specific example, this paper only presents the case of the SIVICAP dataset corresponding to water quality, which is a very important indicator of the biophysical dimension. the SIVICAP data source collects information about physicochemical and microbiological variables obtained from water samples from different municipalities of the analyzed basin. the following are the cleaning steps for SIVICAP. Table 3 presents all the attributes of the SIVICAP data source and the respective check of missing values. This cleaning task was applied to each of the attributes along with a summary of the main measures of central tendency. Those attributes with a high percentage of missing values have a greater probability of being initially discarded (in this case we have chosen a percentage higher than 70%). In Table 3, those attributes with this characteristic were discarded. In contrast, attributes with low percentages of missing values were selected to continue with the cleaning process. The imputation method used was a direct replacement for the mean value of each attribute.

2) OUTLIERS DETECTION
There are different methods to detect outliers that include the Standard Regression (SR) and the Tukey's Method (TM) [45]. In this work, the last method was used considering its non-dependence on the distribution of data. Additionally, it ignores the mean and the standard deviation, which are influenced by extreme values. This method is based on quartiles of the data distribution: Q1 (first quartile, value ≥ 1/4 of the data), Q2 (second quartile or median, value ≥ 1/2 of the data), and Q3 (third quartile, value ≥ 3/4 of the data). The interquartile range (IQR) corresponds to Q3 -Q1 and according to TM, outliers are values more than 1.5 times the IQR of the quartiles (below Q1 -1.5IQR or above Q3 + 1.5IQR). Box plots are used to detect these outliers, where the median and the lower and upper quartiles allow visualizing the distribution of the data. To apply the Tukey's method, an R script implemented by Dhana [46] was used. In this example, we only show the detection process for the pH attribute (Fig. 9). The outliers detected were replaced by ''NA'' values and the process of replacing missing values was applied again. The other attributes of this dataset (and of all datasets) follow the same process.

3) LABEL CORRECTION, CLASS BALANCING, AND REMOVAL OF DUPLICATE INSTANCES
For this data set, it was not necessary to perform the label correction process, considering that the dataset has no contradictory instances. Additionally, class balancing was not performed since the classes were balanced acceptably. Finally, no duplicate instances were found.

4) DIMENSIONALITY REDUCTION
Two cases were considered to find the attributes that best represent the data sources: labeled and unlabeled data. In the first case, the importance of variables was determined by the respective correlation matrix. To show these correlations, in Fig. 10 we present a correlogram (or an autocorrelation plot), which is an image of correlation statistics that highlights 20 attributes with the highest correlations among themselves. In this case, the dark blue tone represents a direct autocorrelation between variables, while dark brown shows an inverse-self. For example, in Fig. 10 a high direct correlation between the variables Total Alkalinity and Total Hardness can be observed. In the same way, there is an inverse correlation between Free Residual Chlorine and the Average Water Quality Risk Index (IRCA for its acronym in Spanish).
Furthermore, Table 4 shows the eigenvalues of the correlation matrix. These values represent the explained variance of the analyzed attributes. The explained percentage VOLUME 8, 2020  and the total accumulated percentage of the variance are also presented. Based on these values we can establish the number of attributes that best explain the total variance. In this example, we could select between 8 (explain 64.13% of the total variance) and 14 (explain 91.09% of the total variance) attributes.
On the other hand, the second case corresponds to the labeled datasets. Here, the importance of variables was calculated using two methods: Logistic Regression (LR) and Random Forest (RF). A comparison of the results obtained with both methods is presented in Table 5.
Once the cleaning steps have been applied, two versions of the datasets are obtained. The former in its raw format and the last one already processed. With the next step (meta-features analysis) it is intended to corroborate whether their quality improved significantly or not, and thus continue with the last phase of the process presented in section VI.

B. META-FEATURES ANALYSIS
This step allows us to establish an overview about the quality characteristics of data sources. In other words, evaluate whether the new processed datasets present an improvement over the original ones. In this sense, Table 6 presents the consolidation of the meta-features which were previously described in section II-B-2.
From Table 6 we can establish an improvement in the quality of most of the datasets mainly influenced by the processes of imputation and removal of outliers. Skewness (values closer to zero) and kurtosis (values closer to three) demonstrated a better distribution of data after the application of the cleaning steps. For example, for the ep_agronet_p and pi_dnp_aib datasets in which the skewness and kurtosis values were very high, these decreased significantly. On the other hand, the number of missing values and duplicate instances were reduced to zero for all datasets. At this point, the set of clean data sources is obtained, and by improving their quality, in the next section, we move towards the last phase of the process.

VI. PHASE 3 -VARIABLES PRIORITIZATION
This section presents the third and final phase of the data preparation process (Fig. 11). Here, the most important attributes of each data source are identified and contrasted with the criteria of several experts to validate its relevance. This final phase and its modules applied to the case study are presented below.

A. FEATURE SELECTION
As observed in the example described in section V (data cleaning -dimensionality reduction), the importance of variables was calculated using different techniques applied according to the type of dataset: labeled (supervised approach) or unlabeled (unsupervised approach). These approaches are presented below.

1) SUPERVISED APPROACH
In the case of labeled datasets, the attributes were ranked through two automatic methods such as Logistic Regression and Random Forest as mentioned in section II-C-1. Some aspects considered to apply these techniques are mentioned below.
(i) In logistic regression, the equation to describe the model to be fitted to the SIVICAP dataset is presented in (1). Where ARL is defined as the Average Risk Level of water contamination, which is expressed as the linear combination of the 20 attributes of the dataset.   ensemble method is based on randomization to increase the diversity of decision trees (basic learners). RF uses Bagging to generate a training set for each random tree, which divides each node by gaining information.

2) UNSUPERVISED APPROACH
On the other hand, a correlation matrix was applied in unlabeled datasets to select the most important attributes (those with the highest correlation). A correlogram is shown in Fig. 12 to better visualize the degree of correlation between the variables in the AVA data source. In this example, the variables correspond to the Climate Adaptability (CA) of some crops analyzed within the AVA project. This correlogram indicates a high direct correlation between the CA of the banana crop and the CA of the coffee and cocoa crops. It also highlights a high inverse correlation between the CA of the potato crop and the CA of the two crops mentioned above (coffee and cocoa). From the previous correlation matrix, we were able to establish that the most important variables in this dataset were the CAs of banana and potato crops. The ranking of variables obtained after applying such techniques was validated by a group of experts. The description of this experiment and their respective results are presented in the next section.

B. EXPERT VALIDATION
In the final step of the process, an experiment was developed in both labeled and unlabeled datasets to validate the relevance of the results produced by the ranking of variables. In this way, four experts in domains such as agribusiness engineering, environmental engineering, and chemical engineering, had the role of raters. Each rater identified the most important variables or attributes in each data source according to their area of knowledge. Hence, different rankings were obtained from the perspective of each rater. Finally, these were compared with the rankings of the automatic methods (LR, RF in Table 5) using the Fleiss's Kappa index [47] for assessing the reliability of agreement between a fixed number of raters.
For a better visualization in this paper, the results of the ranking process are presented using two of the 16 data sources VOLUME 8, 2020   (SIVICAP, the labeled data source, and AVA, the unlabeled data source). It should be noted that the same procedure was applied for each of the 16 datasets. In the first instance, Table 7 shows the rankings by Logistic Regression, Random Forest, and expert criteria for the SIVICAP data source (labeled data), where the most important variable is represented by the ranking number 1. Furthermore, Table 8 shows the ranking obtained through the correlation matrix and the ranking determined by the experts for the AVA data source.
Subsequently, the level of agreement between automatic rankings and expert raters is evaluated to determine the performance of the data preparation process for all datasets. Hence, Table 9 presents the results of Fleiss's Kappa [47], where the following ranges were used to interpret this The results of this evaluation presented an acceptable level of agreement for all raters and most data sources. Similarly, P-values remained at the threshold of statistical significance (typically less than 0.05), which indicates that the agreement between raters is significantly better than would be expected by chance. It should be noted that for all labeled datasets, a better concordance with the logistic regression method was observed.

VII. CONCLUSIONS AND FUTURE WORK
Data processing for information generation is an important aspect of any decision-making process. Therefore, stakeholders need to reduce the uncertainty generated in complex systems. In these types of systems, environmental variables and social interaction are determinants to establish safely and accurately the investment strategies in the territories. In these areas, it is intended to improve living conditions by combining quantitative and qualitative data and reducing uncertainty in data quality. In this way, it is possible to establish decisions in public policy about risks and vulnerabilities in a region with an acceptable level of certainty. Therefore, methodologies for the analysis of agricultural vulnerability in climate change contexts constitute a valuable tool to analyze and define adaptation strategies. Similarly, vulnerability assessments can guide processes of change in both land use and crops located in areas not optimal for growth and production. Vulnerability analysis and information derived from future climate scenarios provide valuable inputs for a productive reorganization of a given territory, contributing to the territorial management plans. On the other hand, experts recommend performing these analyzes periodically, as well as considering future climate scenarios, to ensure the effectiveness of adaptation and mitigation measures, responding to changing realities and needs. However, replicating these studies requires systematic processes that guarantee reliability in the new results. Therefore, the multi-dimensional data preparation process proposed in this article is a guide for those experts in the area who need to replicate relevant analyzes from the countries or regions that constantly apply them. Indeed, the process of preparing multi-dimensional data provides quality information, free access, permanently updated, and at different climatic scales (national, regional, departmental, and municipal), productive systems (crop phenology, abiotic factors, crop management, etc.) and other specific and relevant socio-economic and environmental data for vulnerability analysis.
As mentioned in this paper and following the recommendations of the experts, a detailed vulnerability analysis adapted to local contexts must have free access information for all types of actors (researchers, agricultural organizations, farmers, policy-makers, and decision-makers, among others). Similarly, the same vulnerability studies on which this paper is based, recommend continuing to invest in systems that capture data from different indicators in a systematic way, but also in processes that guarantee updating and data quality.
From a data-driven perspective, evaluating the metafeatures of a data source, before and after preprocessing, was a key aspect of this research work. An important advantage corresponds to the reduction of time and effort in the implementation and training of a large number of predictive models, which in many cases are not applied in a real or production environment. By consolidating meta-features, we were able to establish an overview of the quality of each data source. In the same way, strategies to improve this quality can be defined by analyzing each meta-characteristic together with the expert staff.
Furthermore, prioritizing the importance of variables is a mixed task. Different statistical or machine learning models can establish which are the most relevant attributes in a data source. However, expert knowledge assumes a leading role that cannot be ignored. The expert's point of view refined the prioritization results considering the percentage of error inherent in the automatic methods. Likewise, it should be noted that the prioritization methods are not always the same for all data sources. For tagged datasets (data with target variables or classes) it is easy to calculate a score of importance with methods such as logistic regression and random forest, among others. On the other hand, for unlabeled datasets, the previous methods cannot be applied and this prioritization must be obtained through the values of the correlation matrix. Those attributes with higher correlation values were considered more relevant in the final ranking. The ranking of each of the above methods was validated through the criteria of several experts. The degree of agreement between the automatic ranking and the ranking of raters was determined by the Fleiss's Kappa measure. Having a large number of variables and raters, it is difficult to obtain a high level of agreement. However, the results of this statistical measure obtained acceptable values for most of the data sources, and at a glance, a certain coincidence can be observed between groups of variables. Likewise, for the labeled datasets, a better concordance was found for the ranking of the logistic regression method.
Finally, as future work, it is expected to implement a data fusion strategy to establish an integrated view of the available information. Although not all data can represent the same utility, this strategy will make it possible to establish common points among data sources for the subsequent application of analytical models. These models will be focused on answering different questions not only from the data analyst, but also from the agricultural producer. Which crops could have greater options to be produced in a territory? is one of the questions that try to solve these analytical models.
IVÁN DARÍO LÓPEZ received the degree in systems engineering and the master's degree in telematics engineering from the University of Cauca, Colombia, in 2011 and 2016, respectively, where he is currently pursuing the Ph.D. degree in telematics engineering. He is currently a Researcher with Telematics Engineering Group (GIT). His current research interests focus on big data analysis, data mining, and machine learning techniques for modeling and developing applications in the agricultural sector, specifically oriented to climate-smart agriculture (CSA).
APOLINAR FIGUEROA received the degree in biology from the University of Cauca, Colombia, in 1982, the master's degrees in ecology from the University of Barcelona, Spain, in 1986, and the Ph.D. degree in biological sciences from the University of Valencia, Spain, in 1999. He is currently a Full Professor and leads the Environmental Studies Group, University of Cauca. His research interests focus on environmental impact assessment and biodiversity management.
JUAN CARLOS CORRALES received the Dipl.-Ing. and master's degrees in telematics engineering from the University of Cauca, Colombia, in 1999 and 2004 respectively, and the Ph.D. degree in sciences, specialty computer science from the University of Versailles Saint-Quentinen-Yve-lines, France, in 2008. He is currently a Full Professor and leads the Telematics Engineering Group (GIT), University of Cauca. His research interests focus on machine learning and data analytics.