Developments in Multidimensional Spatial Data Models


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In: Encyclopedia of Geographic Information Science. Edited by: Karen K. Subject: Geographic Information Systems. Fisher, P. Uncertainty and error. Kemp Ed.

Fisher, Peter. Kemp, Karen K. SAGE Knowledge. Have you created a personal profile? Login or create a profile so that you can create alerts and save clips, playlists, and searches. Please log in from an authenticated institution or log into your member profile to access the email feature. SOVAT is also equipped with an advanced linkage module capable of integrating an array of health-related databases inpatient and outpatient hospitalizations; cancer, birth and death registries and socio-economic data sets.

Finally, a front end application fetches outcomes of the integration module and visualizes the outcomes to end users. The current version of SOVAT is a desktop application that runs either on a stand-alone PC or on a client-server environment SOVAT interface runs on the desktop and the database engine runs on the server, hence the terms "front-end applications" and "back-end engine".

Through an easy-to-use point-and-click interface, even a novice user is able to conduct complex queries quickly and effortlessly using SOVAT. SOVAT DSS has been designed to present health information in a way that will facilitate planning efforts among community stakeholders with diverse interests. The two main components that make up SOVAT interface are the navigation and visualization component [ 7 ], as displayed in Figure Navigation components on the left side display cube dimensions and their members as a hierarchical tree.

These trees provide simplicity for users to browse and select dimension members to be included in their queries. A search engine is provided as a complementary feature to help users find a member in a dimension with large members. The visualization component on the right side consists of spreadsheets, charts, and maps. SOVAT provides a spreadsheet to display query results, translates the spreadsheet data into charts, and visualizes the results onto maps. Several advantages of having different approaches include the ability to easily recognize certain trends using charts and the ability to recognize geographic patterns using the maps.

The map in SOVAT is a real vector GIS not a bitmap image that can be used to query an area by clicking the map, drill down to lower geographic levels, drill up to upper levels, and querying neighbourhood areas. SOVAT is also equipped with the ability to export query results to be used in different applications. In addition to these functionalities, the queries can also be saved to be used at a later time.

The Indonesian data sets are comprised of demographic data, health indicators, and spatial data maps. The data sets come in different levels of detail and were collected using different collection methods. Indonesia is divided into provinces. Provinces consist of regencies kabupaten and cities kota which together are called counties in this paper. One level below counties is sub-district kecamatan , while the lowest administrative level — that is the one below sub-district — is called village desa.

The definition of village applies to both rural and urban areas. Political changes in Indonesia since the last decade have affected the administrative division, with the tendency of a growing number of provinces and regencies. The latest data from the Ministry of Internal Affairs show that Indonesia currently has 33 provinces and counties [ 8 ]. Data sets from the Indonesian Census and the Indonesian village statistics were used in the case studies. Similar to many countries, a census in Indonesia is conducted every decade.

Indonesia conducts a series of population, agricultural, and economic censuses. The population census takes place in the years ending with "0"; the agricultural is conducted in the years ending with "3"; while the economic census is held in the years ending in "6" [ 9 ]. Of these censuses, the population census is the most comprehensive and is aimed at gathering characteristics of the Indonesian population such as gender, age, marital status, education level, and occupation.


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The Population Census is the latest census and the first census conducted using complete enumeration. Since the Census was aimed at providing users with small area statistics, statistics of villages can be established from the data collected. In addition to the censuses, the Indonesian Bureau of Statistics also conducts an intercensal population survey SUPAS in between the two censuses [ 10 ].

The survey is designed to collect the population statistic that is comparable to the population census. Another approach to data collection, rather than to collect data on each household and individual, is to collect statistics on villages [ 11 , 12 ]. Village statistics provide information that otherwise is not available. Among the objectives of village-level data collection are:. The information in village-level collection includes: the number of the population and households, the housing and environmental data, the education and health-related data, socio-cultural information, recreation and sport facilities, transportation, and communication.

While demographic data are mostly from the Bureau of Statistics BPS , more specific health indicators are available from the Ministry of Health [ 13 ]. These data include: a general mortality rate, an infant mortality rate, life expectancy, top diagnoses for in-patients and out-patients, and morbidity of infectious diseases Although these data are not used for this project, it is a potential source to use in future works.

Components of gis wikipedia

SOVAT uses spatial data in polygon format that consists of administrative-boundary maps. The map is rendered using certain color schemes to display the results of OLAP queries. For example, in Figure 2 , the darker the color, the higher are the results of performed queries. In addition to the polygon data, SOVAT can also have additional layers using lines and point data, for example to represent rivers, streets, cities, or industrial places. The additional layers can be used to perform other spatial analysis such as buffering.

The digital map of Indonesia is provided by the BPS. The existing spatial data come from four different levels: from province level down to village level. However, due to the low accuracy of the village-level spatial data, we chose to use one level higher than the village — that is the sub-district kecamatan level. Geographic location was used as the primary linkage variable that connects statistical and spatial data sets. The linking process is done using an administrative code that is uniquely defined for every administrative unit.

Standardization is the key for the linking process. Most developed countries have a uniform identification for every geographic entity that can be used by the government and private sector. This code is used by the US Census Bureau and other government agencies that generate statistical data sets. Unfortunately, there is no such uniform identification standard for geographic entities in Indonesia. The lack of a uniform code leads to the use of geographic names such as the name of counties and villages as the key identifiers of the geographic entity.

Geographic names are very susceptible to typographical errors and inconsistent spelling. As a result, the same geographic entities can be written differently in different reports even if the reports come from the same government institution Bureau of Statistics. Several solutions were tried for this problem. Some of the data were corrected using a pattern-matching approach, while the remainder that could not be recognized using pattern matching were manually corrected. The need of a uniform code is more important in light of the rapid changes of administrative boundaries in the past decade.

The problem with spatial data becomes more complex since the updating process of spatial data is not as fast as the process of administrative changing. While the administrative code is easily updated with more recent changes, the map is still outdated. For example, the administrative boundaries are changed up to , however the latest version of the map we use is from Since there is no official new map released, there is no other option rather than to translate the data into an older map.

As shown in Figure 11 , the geographic unit provides a linkage between spatial data and other numerical data sets. Multidimensional database design was conducted in order to develop the database capable of supporting multidimensional analysis. The "measures" in this model is a statistical number about the geographic unit such as the number of population in a sub-district.

The "dimension" is an independent variable that allows us to view the information from different angles. For example, we can view the number of incidences based on the disease type, urban-rural designation area, or time. Therefore, certain social-ecological systems are over-sampled and others under-sampled. The sample size required was calculated using prevalence of poverty according to a Head Count Ratio.

Towards a web coverage service for efficient multidimensional information retrieval

Prevalence of poverty according to Head Count Ratio below lower poverty line constructed on the basis of cost of basic food needed is Here nine districts 3 from Khulna Division and 6 districts from Barisal Division were considered as one study area and the mean was weighted accordingly. Weighted mean of prevalence of poverty below the lower poverty line is A further 10 percent was added to the sample size to take into account non-responses, 6.

The formula used to determine the sample size is:. Unions belonging to each of the seven SES were labelled from 1 to n west to east.

Working Paper: 95

Table 2 shows the number of Unions in each of the SES. Three Unions in each SES were systematically selected.


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  5. The number of Unions per SES was limited to three because of time and cost constraints. If any Union selected had less than three Mouzas with households the segment size then we excluded that Union and considered the nearest Union based on the criteria mentioned above. To select Mouzas from each Union, first any Mouza with fewer than households identified using the Mouza list from the Bangladesh Population Census Tables 25 , was removed.

    Then the remaining Mouzas were assigned number from 1 to n. R programme was used to randomly selected three Mouzas from each of the selected three Unions. Due to time and money constraints a segment was considered, not an entire village. A segment of households was decided based on previous studies in Bangladesh R programme was used to randomly select a segment. The count of the households began from the north corner of the Mouza and moved southwards. Thus 63 segments, randomly selected from the 63 Mouzas, were mapped and then listed by trained enumerators. The listing form included information on name of main earner and household head, age, marital status, primary, secondary and tertiary occupation of main earner, monthly total household income, and household construction material information.

    The household listing facilitated systematic random sampling of households. No further stratification took place. Households were selected where there was the presence of both a male aged 18 to 54 and a female respondent aged 15— The target respondent for the survey was the main earner, not necessarily the household head, although the two categories often overlap.

    The main earner male or female completed the structured questionnaire. Information on global satisfaction of life, anthropometry height and weight and blood pressure was collected from both a male and female member of the selected household. If the main earner was not available at the time of interview then the enumerator made an additional two attempts to catch him or her at home. However, if the main earner was not likely to return to the household during the time enumerator was in the area, then the spouse of the main earner, second earner or spouse of the second earner of that household was interviewed in that order of preference , as long as he or she was between the ages of 18—54 years for men and 15—49 for women.

    If no-one fitting this description was in the household we excluded that household and moved to the next selected household. In addition, in order for the research to be approved by the ERC, all named researchers on the research protocol completed National Institutes of Health online training and individual ethical approval was obtained from the University of Southampton as the lead institution in the ESPA Deltas consortium in the UK and the Ecosystem Services for Poverty Alleviation Directorate. The ERC, an independent body to safeguard the physical, mental and social well-being of the participants, is guided by the relevant international regulations and is responsible to the Board of Trustees of icddr,b.

    The committee reviews each protocol involving human participants and accords approval, and the decision of the ERC in this matter is final. The icddr,b ERC is internationally recognized ethics review committee and pioneer in Bangladesh. The ESPA Deltas social survey was implemented in the south west and south central coastal zone of Bangladesh and included rural areas of Satkhira, Khulna and Bagerhat districts of Khulna Division and all districts in Barisal Division except Jhalokati.

    ACPR together with icddr,b provided intensive training on survey tools, data collection methodology and ethical grounds of social data collection. Several days of field testing of the survey tools were carried out before each round of survey as there were minor modifications of questionnaire in each round of survey e. A field guide was distributed to teams carrying out water sample collection and measuring blood pressure in Bengali and English. The questionnaire was pretested in the field in a pilot phase, before data collection. Thirty skilled field staff were recruited along with ten experienced supervisors for the study and retrained on standard methods of obtaining physical measurements.

    Seven teams were assigned to seven zones, each team consisted of one supervisor, three interviewers and one porter to carry height scale and other equipment. To ensure the quality of the data, a monitoring team from icddr,b checked one percent of the data and held periodic meetings to provide necessary feedback to the field work.

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    Prior to data collection from individual household members, written consent was taken in presence of a witness. In the second and third round surveys enumerators tried to reach the same household and interview the same household members as in the first round. Three to five percent of households were absent in the second and third round for reasons such as permanent or temporary migration, or unwillingness to give the interview. All study participants were interviewed only after giving informed consent according the Belmont principles of respect for persons and using consent forms approved by the Ethical Review Committee of icddr,b.

    In addition, where applicable, assent as also taken. Efforts were made to ensure that all respondents were appropriately informed about the study and thoroughly understood their participation in the study. Participation was voluntary and interviewers ensured that participants knew that refusal to participate would not lead to any adverse consequences. According to ERC requirement, one copy of the signed informed consent form was handed over to all potential study participants. Height and weight were collected from respondent and his or her counterpart.

    These measurements were also taken from eldest under five children if available. If the eldest child under five was less than a year old, then length was measured instead.

    Background

    While measuring height, the Frankfort position was confirmed and reading was noted accurately. Standard tools were used to measure for height stadiometer and weight Uni Scale by Seca. The three rounds of survey were implemented between June and March see Table 2.

    Direct observation or spot checking in selected villages, and re-interviewing with a quality control questionnaire in selected households, formed part of monitoring process. This study did not use any computer codes to generate the dataset. Three different datasets are available in association with this research.

    Raster data model in GIS (theory)

    While it provides only limited data, the large sample size may facilitate analysis. All variables are named according to their number in the questionnaire, and fully described in the variable labels. The household listing and three survey instruments can be downloaded in English and act as the code book for the datasets. The quality of the dataset is ensured by: a thorough pre-testing of the questionnaire; b translating the questionnaire into Bengali, including local terminology, and reverse translating to check quality of translations; c recruitment of experienced enumerators and comprehensive training in survey implementation; d quality control questionnaires being carried out alongside the main data collection and high levels of supervision in the field; e double data entry and numerous quality checks on the final digital dataset, including cross-referencing original paper surveys.

    These are detailed in the following paragraphs. The survey questions were designed based on the research questions of the project, using questions from other surveys already implemented in Bangladesh where appropriate, and drawing on the qualitative data collection and expert judgement to create new questions.

    To ensure that the questions are relevant and meaningful, extensive pre-testing of the quantitative questionnaire was conducted in the study area prior to finalisation of the survey questions. Training of the enumerators is essential for effective implementation of a survey. A deep understanding of the questions and philosophy of the survey ensures that enumerators are able to help the surveyed households in answering the questions properly.

    Developments in Multidimensional Spatial Data Models

    To achieve this, the enumerator team was selected for its long track record in doing similar surveys and was trained over a period of a month by the ESPA Deltas research team. Role play and field practice was carried out for every section of the questionnaire. Specialists were brought in as required, for example doctors for the blood pressure measurement and experts in using global positioning systems for the location data. Rigorous training on anthropometry was carried out. A quality control team was assigned in the field to monitor data collection. Spot checking, direct observation and re-interviewing of five percent were carried out.

    A senior level supervision team also frequently visited the data collection activities in the field. During the survey the field supervisor checked all completed questionnaires. The interviewer cross-checked each questionnaire for internal consistency at end of the day. Section 3 of the survey provides general information on the range of incomes which subsequent sections investigate in more depth. As such, Section 3 was used with other key indicators to check that all appropriate income and expenditure questions had been completed.

    If not, the respondent returned to the household. Analysis of these variables reveals a highly detailed data set that has captured household differences For example, attributes such as monthly income and food expenditure show high variance between households despite a four month recall period.

    Completed questionnaires were checked before data entry by an office editor. In the process of data entry all possible logical checks were built into the program CSPro. Dual entry and comparison between datasets ensured correct data entry. Data management experts from icddr,b thoroughly checked all possible inconsistencies e. The survey used paper for recording the data.

    The original paper version is kept to allow the team to check individual records in the digital dataset if necessary. Finally, the dataset was thoroughly analysed for outlier and extreme values to ensure that typing errors are eliminated from the final dataset. To identify such typing errors, individual and composite variables were calculated and summarised as minimum, median, mean, maximum and compared with other data sources and reports or, if similar data was not available for Bangladesh or the study area, these were evaluated by expert judgement.

    A benefit of the data is their spatial nature that allows social factors to be analysed in the context of environmental conditions and resources. Therefore, the location of the villages is included in the dataset. However, this increases the sensitivity of the data as it creates the potential for households within each village to be identified from the survey data. For commercial use, please contact the UK Data Service at help ukdataservice. The survey focused on collecting in-depth information on the ways people use natural resources.

    A system of skips and checks were put in place to improve the survey experience for the respondent which creates the potential for double-counting when analysing ecosystem services based income data. Section 3 of the survey collects information on income of all household livelihood sources. If the respondent mentions agriculture, aquaculture, fishing or mangrove forest collection activities in Section 3, then the corresponding part of Section 4 is also completed.

    Therefore, data on income from agriculture, aquaculture, fishing or mangrove forest collection is collected twice: first as a rough estimate in Section 3, and then in more depth in Section 4. Thus, either the information from Section 3 or from Section 4 can be used but not both. Due to attrition of households between survey rounds, not all cases are present in all three rounds.

    Data should be filtered before proceeding before carrying out longitudinal analysis on any of the variables. An additional 10 percent was added to the initial sample in order to account for expected attrition. Actual attrition rates were much lower: 4.

    Developments in Multidimensional Spatial Data Models Developments in Multidimensional Spatial Data Models
    Developments in Multidimensional Spatial Data Models Developments in Multidimensional Spatial Data Models
    Developments in Multidimensional Spatial Data Models Developments in Multidimensional Spatial Data Models
    Developments in Multidimensional Spatial Data Models Developments in Multidimensional Spatial Data Models
    Developments in Multidimensional Spatial Data Models Developments in Multidimensional Spatial Data Models
    Developments in Multidimensional Spatial Data Models Developments in Multidimensional Spatial Data Models
    Developments in Multidimensional Spatial Data Models Developments in Multidimensional Spatial Data Models
    Developments in Multidimensional Spatial Data Models Developments in Multidimensional Spatial Data Models
    Developments in Multidimensional Spatial Data Models

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