Papers by Abdelhalim E El-Farouk
ARTICLE INFO ABSTRACT This study attempted to evaluate social progress in a rural setting taking ... more ARTICLE INFO ABSTRACT This study attempted to evaluate social progress in a rural setting taking Alkumur as a case study. This study used both scenery and primary data. The secondary data obtained from (UNDP) study for all countries of the world in 2016. The SPI was estimated a Sudan national Figure by 16 points. The SPI was also calculated from primary data through questioner conducted in Alkumur village 2017. Fifty interval estimat conclusion of the study reveals that from secondary data, Alkumur SPI amounted to 46.93% versus 30.55% for whole of Sudan. The difference is 16.38%. The SPI in Alkumur is approxim which represents the mean of test of association reflects a statistically significant association between SPI at the household level and all other prediction. From the including males and females, it appears that villagers are full of optimism and enthusiasm to promote their village in all aspects of life. The coherence the population of the village repres force that makes the potential of social progress very promising and reliable. The only one recommendation suggested by the study is to establish a vocational training center for both sexes.
Aim: Boosting health care expenditures is becoming a priority and a major health policy concern a... more Aim: Boosting health care expenditures is becoming a priority and a major health policy concern almost worldwide. To achieve solving that concern, it is crucial to know the main factors that underlie the growth in health care expenditures. This will help in supporting decision-makers to find best policies to manage health care costs. Here in this study, our aim is to examine the determinants of health care spending in Saudi Arabia over the period (1979-2013). Material and Method: Data used in this study have been collected from different sources that are mentioned in the text when come by. Variables included in this study include per capita GDP, number of physicians, population age structure variables; (population under 15 and/or over 65), infant mortality rate (IMR), population growth and lagged healthcare expenditure. These variables have been singled out by this study as the key determinants of health care expenditure. Variables have been displayed in their descriptive formats to check for their minimum, maximum, range, mean, standard deviation and coefficient of variation. A correlation matrix was produced to check for relationships between the variables. A stepwise regression method was adopted to described determine the factors that most affect and determine health care expenditure in KSA. Results: Our study findings revealed that the GDP per capita and the lagged health care expenditures (i.e. per capita healthcare expenditure of the previous year) are the major two factors that affect health care expenditure levels. Together, the two variables are responsible for 93.3% of the variations in the per capita health care expenditure. The regression model excluded all of the remaining variables because of the fact that they are statistically insignificant and do not fit with the model at a (0.05) level of significance. They do not contribute significantly to the explanation of the per capita health care spending variation. These variables are number of physicians, annual population growth rate, percent of the population aged 65 years and older, percent of the population under 15 years and infant mortality rate.
The presentation (of graphs and maps alike) is in two formats, the first of which is "comparison"... more The presentation (of graphs and maps alike) is in two formats, the first of which is "comparison" of data by area for the starting and ending years. The second is a trend presentation for each variable of each administrative area for the whole SJIF Impact Factor 4.161 ABSTRACT Aim : Graphing and mapping of some of the healthcare facilities in Saudi Arabia are the prime aims of this paper. Displaying the progress of growth of these resources is important in visualizing how these resources progressed over a period of more than twenty years. One of the two previous published papers by the authors of this paper accomplished the task of discussing determinants of healthcare expenditures in the kingdom (El-Farouk, A. E., et. al., EJPMR, 2016). The second paper studied and discussed the equality of the distribution of these facilities and resources using Lorenz curve and Gini Coefficient (El-Farouk, A. E., et. al. EJEC, 2016) Therefore, this paper is trying to portray in a visualized manner how these facilities are distributed over the plain of the thirteen administrative areas of the Kingdom and nationwide. Material and Methods: Many people consider visualization as one of the best ways to get your message across and to quickly draw attention to the key messages. By presenting data visually it's also possible to uncover surprising patterns and observations that wouldn't be seen from looking at numbers alone. Today, there's plenty of free graphic design software to help us do just that; either graphically or cartographically. Such software includes google developers, visualize, Easel.ly, infogr.am, and the most famous ArcGIS. By visualizing information, we turn it into a landscape that you can explore with your eyes, a sort of information map. And when you're lost in information, an information map is kind of useful. There are various types of statistical maps used by geographers and other scholars. These include choropleth (color shaded) maps, proportional symbol maps, and dots maps. The first type is suitable for showing standardized data such as rates, densities or percentages. A different color is used for each of a number of bands, allowing users to identify which areas have high, low or middling values. The second type, dot maps, suits where individual events or groups of events are marked with a dot, allowing users to geographic patterns such as clusters. The third type, which is used in this paper, is the proportional symbol map that uses symbols that are proportional in size to the values they represent, such that the biggest symbol will fall in the area with the highest value. Symbols can include histograms, circles, bars, or objects indicating what is being measured. Sources of Variable Data under the Study: A data matrix of the thirteen administrative areas by the four health resources mentioned below was constructed based on the available government data of the Central Department of Statistics and Information (CDSI). The type of health resources under the study (health center, hospital, hospital beds, and physicians). This data matrix is for 22 years starting from 1992 to 2013 for the health resources (Statistical Year Books) with the exception of the data for physicians. It's available for up to 2007, after which it has been produced at the level of the Kingdom. In order to obtain the relative share of an area of each health resource, the total number of, for example, health centers, is divided by the corresponding total population of the area and multiplied by k, which equals 100,000 (for health centers and hospitals), 1000 (for hospital beds) and 10,000 (for physicians). Looking at the absolute numbers, alone, of each area variable regardless of its corresponding population will lead to a faulty conclusion. Total populations, of administrative areas, were projected using the declared growth rates (by the CDSI) between censuses. Human population calculator was used to running the projection at this website (http://www.metamorphosisalpha.com/ias/population.php). Thus, relative shares are used instead of numbers because they provide a more realistic picture.
The presentation (of graphs and maps alike) is in two formats, the first of which is "comparison"... more The presentation (of graphs and maps alike) is in two formats, the first of which is "comparison" of data by area for the starting and ending years. The second is a trend presentation for each variable of each administrative area for the whole SJIF Impact Factor 4.161 ABSTRACT Aim : Graphing and mapping of some of the healthcare facilities in Saudi Arabia are the prime aims of this paper. Displaying the progress of growth of these resources is important in visualizing how these resources progressed over a period of more than twenty years. One of the two previous published papers by the authors of this paper accomplished the task of discussing determinants of healthcare expenditures in the kingdom (El-Farouk, A. E., et. al., EJPMR, 2016). The second paper studied and discussed the equality of the distribution of these facilities and resources using Lorenz curve and Gini Coefficient (El-Farouk, A. E., et. al. EJEC, 2016) Therefore, this paper is trying to portray in a visualized manner how these facilities are distributed over the plain of the thirteen administrative areas of the Kingdom and nationwide. Material and Methods: Many people consider visualization as one of the best ways to get your message across and to quickly draw attention to the key messages. By presenting data visually it's also possible to uncover surprising patterns and observations that wouldn't be seen from looking at numbers alone. Today, there's plenty of free graphic design software to help us do just that; either graphically or cartographically. Such software includes google developers, visualize, Easel.ly, infogr.am, and the most famous ArcGIS. By visualizing information, we turn it into a landscape that you can explore with your eyes, a sort of information map. And when you're lost in information, an information map is kind of useful. There are various types of statistical maps used by geographers and other scholars. These include choropleth (color shaded) maps, proportional symbol maps, and dots maps. The first type is suitable for showing standardized data such as rates, densities or percentages. A different color is used for each of a number of bands, allowing users to identify which areas have high, low or middling values. The second type, dot maps, suits where individual events or groups of events are marked with a dot, allowing users to geographic patterns such as clusters. The third type, which is used in this paper, is the proportional symbol map that uses symbols that are proportional in size to the values they represent, such that the biggest symbol will fall in the area with the highest value. Symbols can include histograms, circles, bars, or objects indicating what is being measured. Sources of Variable Data under the Study: A data matrix of the thirteen administrative areas by the four health resources mentioned below was constructed based on the available government data of the Central Department of Statistics and Information (CDSI). The type of health resources under the study (health center, hospital, hospital beds, and physicians). This data matrix is for 22 years starting from 1992 to 2013 for the health resources (Statistical Year Books) with the exception of the data for physicians. It's available for up to 2007, after which it has been produced at the level of the Kingdom. In order to obtain the relative share of an area of each health resource, the total number of, for example, health centers, is divided by the corresponding total population of the area and multiplied by k, which equals 100,000 (for health centers and hospitals), 1000 (for hospital beds) and 10,000 (for physicians). Looking at the absolute numbers, alone, of each area variable regardless of its corresponding population will lead to a faulty conclusion. Total populations, of administrative areas, were projected using the declared growth rates (by the CDSI) between censuses. Human population calculator was used to running the projection at this website (http://www.metamorphosisalpha.com/ias/population.php). Thus, relative shares are used instead of numbers because they provide a more realistic picture.
Aim: Boosting health care expenditures is becoming a priority and a major health policy concern a... more Aim: Boosting health care expenditures is becoming a priority and a major health policy concern almost worldwide. To achieve solving that concern, it is crucial to know the main factors that underlie the growth in health care expenditures. This will help in supporting decision-makers to find best policies to manage health care costs. Here in this study, our aim is to examine the determinants of health care spending in Saudi Arabia over the period (1979-2013). Material and Method: Data used in this study have been collected from different sources that are mentioned in the text when come by. Variables included in this study include per capita GDP, number of physicians, population age structure variables; (population under 15 and/or over 65), infant mortality rate (IMR), population growth and lagged healthcare expenditure. These variables have been singled out by this study as the key determinants of health care expenditure. Variables have been displayed in their descriptive formats to check for their minimum, maximum, range, mean, standard deviation and coefficient of variation. A correlation matrix was produced to check for relationships between the variables. A stepwise regression method was adopted to described determine the factors that most affect and determine health care expenditure in KSA. Results: Our study findings revealed that the GDP per capita and the lagged health care expenditures (i.e. per capita healthcare expenditure of the previous year) are the major two factors that affect health care expenditure levels. Together, the two variables are responsible for 93.3% of the variations in the per capita health care expenditure. The regression model excluded all of the remaining variables because of the fact that they are statistically insignificant and do not fit with the model at a (0.05) level of significance. They do not contribute significantly to the explanation of the per capita health care spending variation. These variables are number of physicians, annual population growth rate, percent of the population aged 65 years and older, percent of the population under 15 years and infant mortality rate.
The presentation (of graphs and maps alike) is in two formats, the first of which is "comparison"... more The presentation (of graphs and maps alike) is in two formats, the first of which is "comparison" of data by area for the starting and ending years. The second is a trend presentation for each variable of each administrative area for the whole SJIF Impact Factor 4.161 ABSTRACT Aim : Graphing and mapping of some of the healthcare facilities in Saudi Arabia are the prime aims of this paper. Displaying the progress of growth of these resources is important in visualizing how these resources progressed over a period of more than twenty years. One of the two previous published papers by the authors of this paper accomplished the task of discussing determinants of healthcare expenditures in the kingdom (El-Farouk, A. E., et. al., EJPMR, 2016). The second paper studied and discussed the equality of the distribution of these facilities and resources using Lorenz curve and Gini Coefficient (El-Farouk, A. E., et. al. EJEC, 2016) Therefore, this paper is trying to portray in a visualized manner how these facilities are distributed over the plain of the thirteen administrative areas of the Kingdom and nationwide. Material and Methods: Many people consider visualization as one of the best ways to get your message across and to quickly draw attention to the key messages. By presenting data visually it's also possible to uncover surprising patterns and observations that wouldn't be seen from looking at numbers alone. Today, there's plenty of free graphic design software to help us do just that; either graphically or cartographically. Such software includes google developers, visualize, Easel.ly, infogr.am, and the most famous ArcGIS. By visualizing information, we turn it into a landscape that you can explore with your eyes, a sort of information map. And when you're lost in information, an information map is kind of useful. There are various types of statistical maps used by geographers and other scholars. These include choropleth (color shaded) maps, proportional symbol maps, and dots maps. The first type is suitable for showing standardized data such as rates, densities or percentages. A different color is used for each of a number of bands, allowing users to identify which areas have high, low or middling values. The second type, dot maps, suits where individual events or groups of events are marked with a dot, allowing users to geographic patterns such as clusters. The third type, which is used in this paper, is the proportional symbol map that uses symbols that are proportional in size to the values they represent, such that the biggest symbol will fall in the area with the highest value. Symbols can include histograms, circles, bars, or objects indicating what is being measured. Sources of Variable Data under the Study: A data matrix of the thirteen administrative areas by the four health resources mentioned below was constructed based on the available government data of the Central Department of Statistics and Information (CDSI). The type of health resources under the study (health center, hospital, hospital beds, and physicians). This data matrix is for 22 years starting from 1992 to 2013 for the health resources (Statistical Year Books) with the exception of the data for physicians. It's available for up to 2007, after which it has been produced at the level of the Kingdom. In order to obtain the relative share of an area of each health resource, the total number of, for example, health centers, is divided by the corresponding total population of the area and multiplied by k, which equals 100,000 (for health centers and hospitals), 1000 (for hospital beds) and 10,000 (for physicians). Looking at the absolute numbers, alone, of each area variable regardless of its corresponding population will lead to a faulty conclusion. Total populations, of administrative areas, were projected using the declared growth rates (by the CDSI) between censuses. Human population calculator was used to running the projection at this website (http://www.metamorphosisalpha.com/ias/population.php). Thus, relative shares are used instead of numbers because they provide a more realistic picture.
The presentation (of graphs and maps alike) is in two formats, the first of which is "comparison"... more The presentation (of graphs and maps alike) is in two formats, the first of which is "comparison" of data by area for the starting and ending years. The second is a trend presentation for each variable of each administrative area for the whole SJIF Impact Factor 4.161 ABSTRACT Aim : Graphing and mapping of some of the healthcare facilities in Saudi Arabia are the prime aims of this paper. Displaying the progress of growth of these resources is important in visualizing how these resources progressed over a period of more than twenty years. One of the two previous published papers by the authors of this paper accomplished the task of discussing determinants of healthcare expenditures in the kingdom (El-Farouk, A. E., et. al., EJPMR, 2016). The second paper studied and discussed the equality of the distribution of these facilities and resources using Lorenz curve and Gini Coefficient (El-Farouk, A. E., et. al. EJEC, 2016) Therefore, this paper is trying to portray in a visualized manner how these facilities are distributed over the plain of the thirteen administrative areas of the Kingdom and nationwide. Material and Methods: Many people consider visualization as one of the best ways to get your message across and to quickly draw attention to the key messages. By presenting data visually it's also possible to uncover surprising patterns and observations that wouldn't be seen from looking at numbers alone. Today, there's plenty of free graphic design software to help us do just that; either graphically or cartographically. Such software includes google developers, visualize, Easel.ly, infogr.am, and the most famous ArcGIS. By visualizing information, we turn it into a landscape that you can explore with your eyes, a sort of information map. And when you're lost in information, an information map is kind of useful. There are various types of statistical maps used by geographers and other scholars. These include choropleth (color shaded) maps, proportional symbol maps, and dots maps. The first type is suitable for showing standardized data such as rates, densities or percentages. A different color is used for each of a number of bands, allowing users to identify which areas have high, low or middling values. The second type, dot maps, suits where individual events or groups of events are marked with a dot, allowing users to geographic patterns such as clusters. The third type, which is used in this paper, is the proportional symbol map that uses symbols that are proportional in size to the values they represent, such that the biggest symbol will fall in the area with the highest value. Symbols can include histograms, circles, bars, or objects indicating what is being measured. Sources of Variable Data under the Study: A data matrix of the thirteen administrative areas by the four health resources mentioned below was constructed based on the available government data of the Central Department of Statistics and Information (CDSI). The type of health resources under the study (health center, hospital, hospital beds, and physicians). This data matrix is for 22 years starting from 1992 to 2013 for the health resources (Statistical Year Books) with the exception of the data for physicians. It's available for up to 2007, after which it has been produced at the level of the Kingdom. In order to obtain the relative share of an area of each health resource, the total number of, for example, health centers, is divided by the corresponding total population of the area and multiplied by k, which equals 100,000 (for health centers and hospitals), 1000 (for hospital beds) and 10,000 (for physicians). Looking at the absolute numbers, alone, of each area variable regardless of its corresponding population will lead to a faulty conclusion. Total populations, of administrative areas, were projected using the declared growth rates (by the CDSI) between censuses. Human population calculator was used to running the projection at this website (http://www.metamorphosisalpha.com/ias/population.php). Thus, relative shares are used instead of numbers because they provide a more realistic picture.
The objective of this paper is to examine the interrelationships and links between population act... more The objective of this paper is to examine the interrelationships and links between population activities (mainly in agriculture and forestry) and environmental degradation in the Sudan, and their impact on the population at large and on women in particular. Most studies made in the area suggest that these activities and the environment are not in harmony. Despite its diversity and high potential, the environment in the Sudan is not sustaining the needs of a growing population and intensifying socio-economic activities. This is because of a high rate of
population growth, a high trend of spatial concentration, excessive migration and misuse of resources. The result is a rapid degradation of the natural resource base, recent spread of famines, malnutrition, degraded health conditions, and disturbance of social and health conditions. It is important to throw light on these issues to promote awareness of the need for a comprehensive population-environment
policy that takes into account the impact of this environmental
degradation on the population and the measures that can be taken to combat this threat.
Aim: Boosting health care expenditures is becoming a priority and a major health policy concern ... more Aim: Boosting health care expenditures is becoming a priority and a major health policy concern almost worldwide. To achieve solving that concern, it is crucial to know the main factors that underlie the growth in health care expenditures. This will help in supporting decision-makers to find best policies to manage health care costs. Here in this study, our aim is to examine the determinants of health care spending in Saudi Arabia over the period (1979-2013).
Material and Method: Data used in this study have been collected from different sources that are mentioned in the text when come by. Variables included in this study include per capita GDP, number of physicians, population age structure variables; (population under 15 and/or over 65), infant mortality rate (IMR), population growth and lagged healthcare expenditure. These variables have been singled out by this study as the key determinants of health care expenditure. Variables have been displayed in their descriptive formats to check for their minimum, maximum, range, mean, standard deviation and coefficient of variation. A correlation matrix was produced to check for relationships between the variables. A stepwise regression method was adopted to described determine the factors that most affect and determine health care expenditure in KSA.
Results: Our study findings revealed that the GDP per capita and the lagged health care expenditures (i.e. per capita healthcare expenditure of the previous year) are the major two factors that affect health care expenditure levels. Together, the two variables are responsible for 93.3% of the variations in the per capita health care expenditure. The regression model excluded all of the remaining variables because of the fact that they are statistically insignificant and do not fit with the model at a (0.05) level of significance. They do not contribute significantly to the explanation of the per capita health care spending variation. These variables are number of physicians, annual population growth rate, percent of the population aged 65 years and older, percent of the population under 15 years and infant mortality rate.
من بين أهم المقاييس المستخدمة لقياس النزعة المركزية في الأنماط النقطية (المكانية) المتعددة يبرز م... more من بين أهم المقاييس المستخدمة لقياس النزعة المركزية في الأنماط النقطية (المكانية) المتعددة يبرز معامل تحليل صلة الجوار كأحد القرائن المستخدمة من قبل الجغرافيين. وخلافا لمعامل صلة الجوار فإن معظم المعايير التي تستخدم لوصف وتحليل نمط التوزيع المكاني للنقاط لا تخلو من ضعف من حيث اعتمادها في نهاية الأمر على الوصف وافتقارها إلى وجود الدليل (index) أو المؤشر المُوحد لقياس نمط التوزيع. ويعتبر معامل صلة الجوار واحدا من المعايير القليلة التي تعتمد في تحليل توزيع النقاط على معيار كمي مستمر (continuous) يبدأ بنقطة التطرف الأولى في سلم المعيار (صفر) وفيها تتجمع جميع نقاط التوزيع في مكان واحد مارا بجميع النقاط حتى نقطة التطرف الأخيرة (2.15) دلالة على انتظام التوزيع، بينما القيمة الوسطى (1) تعني عشوائية التوزيع. ورغما عن وجود الكثير مما كتب حول تحليل صلة الجوار، إلا أن هذه الدراسة تحاول أن تتطرق للتفاصيل الدقيقة المتعلقة بخطوات حساب معامل صلة الجوار، وبكيفية إخضاعه لاختبار الفروض، وبعرض جوانب التميز والقصور في هذا المقياس. ومن أهم أهداف هذه الورقة تسهيل مهمة الباحثين عامة وطلاب الدراسات العليا في مجال الجغرافيا بصفة خاصة وذلك بتقديم شرح تفصيلي لمفهوم تحليل صلة الجوار وتعريفه وشرح كيفية حسابه بصورة جلية. كما تهدف لتقديم تطبيق عملي يتم فيه استخدام تحليل صلة الجوار لمراكز الاستيطان في منطقة مكة المكرمة، و لفت انتباه الباحث الجغرافي بصفة خاصة إلى ضرورة إخضاع قيمة معامل صلة الجوار للاختبار الإحصائي وإلا فإنه قد يبني خلاصة أرائه على نتائج غير ذات دلالة إحصائية معنوية.
ولقد أبرزت الدراسة العملية التي طبقت على مراكز الاستيطان في منطقة مكة المكرمة أن النتائج النهائية لأي دراسة تتعلق بحساب قد تخلص إلى نتائج خاطئة إذا لم يؤخذ عدد نقاط التوزيع في الاعتبار عند تفسير القيمة النهائية لهذا المعامل. وهنا نوهت الدراسة إلى أهمية الأخذ في الاعتبار لعدد نقاط التوزيع وشرحت الكيفية لذلك. كما أكدت الدراسة على وجوب إخضاع قيمة المعامل على الاختبار الإحصائي Z وذلك لاختبار فرض العدم الذي يقول إن نمط توزيع النقاط محور الدراسة نمط عشوائي (حتى وإن كانت قيمة أكبر من واحد صحيح) بينما ينص الفرض البديل على أن نمط توزيع النقاط محور الدراسة نمط غير عشوائي .
The outstanding characteristics of the population geography of the Sudan are
seen in its vastness... more The outstanding characteristics of the population geography of the Sudan are
seen in its vastness of the area, its low population density, its high population fertility,
decreasing mortality rates and uneven distribution of developmental projects
among its regions. All these have important effects upon the economic and demographic
characteristics of its population. They also underline the uneven distribution
of the country's population, as a result of which large-scale migration
movements occur.
Three types of population movements in the Sudan are discussed; inter-provincial
movements, rural-urban migration and seasonal migration for cotton picking in the
Gezira scheme. In the discussion of the first type, the analysis covers issues related
to the general levels of movements amongst the 18 provinces of the country, rates
of in- and out-migration in each province and their net migration balances. Also,
it discusses the spatial structure of the movement, and some gaining and losing
provinces are singled out. The impacts of the movements and their selective nature
are also revealed.
Rural-urban migration to the capital city of Khartoum is studied using the
1983 census data, other published data and the author's 1988/89 survey of migrant
households in the city. The scale of the migration and the characteristics of the
migrants are analyzed. Additionally, the structure of the migrant households,
literacy, occupation contrasts and links with the village are investigated. The
reasons behind the migration decision and the reward of the rural-urban migration
are also shown.
Seasonal migration is discussed to disclose the nature of the movement and
its patterns which are associated with the cotton picking operation in the Gezira
ll
scheme. The types of labour involved and labour market conditions are also investigated.
The findings verified the seasonality of the movement to the scheme and
the consistent relationship between migrants and tenants in the scheme.
The aim of this study is to evaluate
the inequality of geographical distribution of
health center... more The aim of this study is to evaluate
the inequality of geographical distribution of
health centers, hospitals, hospital-beds and
physicians among the thirteen administrative
areas that comprises the map of the Kingdom of
Saudi Arabia (KSA). It uses the Lorenz curves
and the Gini coefficient.
Methods: A data matrix of the thirteen
administrative areas by four health resources was
constructed based on the available government
data of the Central Department of Statistics and
Information (CDSI). This data matrix is for 22
years starting from 1992 to 2013 for the health
resources with the exception of the data for
physicians, which is available up to 2007. To
obtain the relative share of an area of each health
resource, the total number of it is divided by
the corresponding total population of the area.
Total populations, of administrative areas, were
projected using the between censuses declared
growth rates. Human population calculator was
used to run the projection at this website. Thus,
relative shares are used instead of numbers
because they provide a more realistic picture.
Lorenz curves were constructed depending on
excel, while Gini coefficients were calculated
using an online Gini calculator.
Drafts by Abdelhalim E El-Farouk
إن الغرض الرئيس من هذه الورقة هو إعتبارها فرصة لاستكشاف وق ا رءة وتوضيح العلاقة بين
الجغ ا رفيا و... more إن الغرض الرئيس من هذه الورقة هو إعتبارها فرصة لاستكشاف وق ا رءة وتوضيح العلاقة بين
الجغ ا رفيا والتنمية المستدامة وذلك بحسب ما ورد في "إعلان لوزيرن" الصادر بعنوان "إعلان ح ول
التعليم الجغ ا رفي من أجل تنمية مستدامة" في يوليو 7002 ، والذي أصدرته لجنة التعليم الجغ ا رفي
بتفويض من الجمعية الجغ ا رفية العالمية استجابة وتنفيذا لخطة الأمم المتحدة العشرية تحت شعار
– .)7002 "تعليم من أجل تنمية مستدامة ) 7002
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Papers by Abdelhalim E El-Farouk
population growth, a high trend of spatial concentration, excessive migration and misuse of resources. The result is a rapid degradation of the natural resource base, recent spread of famines, malnutrition, degraded health conditions, and disturbance of social and health conditions. It is important to throw light on these issues to promote awareness of the need for a comprehensive population-environment
policy that takes into account the impact of this environmental
degradation on the population and the measures that can be taken to combat this threat.
Material and Method: Data used in this study have been collected from different sources that are mentioned in the text when come by. Variables included in this study include per capita GDP, number of physicians, population age structure variables; (population under 15 and/or over 65), infant mortality rate (IMR), population growth and lagged healthcare expenditure. These variables have been singled out by this study as the key determinants of health care expenditure. Variables have been displayed in their descriptive formats to check for their minimum, maximum, range, mean, standard deviation and coefficient of variation. A correlation matrix was produced to check for relationships between the variables. A stepwise regression method was adopted to described determine the factors that most affect and determine health care expenditure in KSA.
Results: Our study findings revealed that the GDP per capita and the lagged health care expenditures (i.e. per capita healthcare expenditure of the previous year) are the major two factors that affect health care expenditure levels. Together, the two variables are responsible for 93.3% of the variations in the per capita health care expenditure. The regression model excluded all of the remaining variables because of the fact that they are statistically insignificant and do not fit with the model at a (0.05) level of significance. They do not contribute significantly to the explanation of the per capita health care spending variation. These variables are number of physicians, annual population growth rate, percent of the population aged 65 years and older, percent of the population under 15 years and infant mortality rate.
ولقد أبرزت الدراسة العملية التي طبقت على مراكز الاستيطان في منطقة مكة المكرمة أن النتائج النهائية لأي دراسة تتعلق بحساب قد تخلص إلى نتائج خاطئة إذا لم يؤخذ عدد نقاط التوزيع في الاعتبار عند تفسير القيمة النهائية لهذا المعامل. وهنا نوهت الدراسة إلى أهمية الأخذ في الاعتبار لعدد نقاط التوزيع وشرحت الكيفية لذلك. كما أكدت الدراسة على وجوب إخضاع قيمة المعامل على الاختبار الإحصائي Z وذلك لاختبار فرض العدم الذي يقول إن نمط توزيع النقاط محور الدراسة نمط عشوائي (حتى وإن كانت قيمة أكبر من واحد صحيح) بينما ينص الفرض البديل على أن نمط توزيع النقاط محور الدراسة نمط غير عشوائي .
seen in its vastness of the area, its low population density, its high population fertility,
decreasing mortality rates and uneven distribution of developmental projects
among its regions. All these have important effects upon the economic and demographic
characteristics of its population. They also underline the uneven distribution
of the country's population, as a result of which large-scale migration
movements occur.
Three types of population movements in the Sudan are discussed; inter-provincial
movements, rural-urban migration and seasonal migration for cotton picking in the
Gezira scheme. In the discussion of the first type, the analysis covers issues related
to the general levels of movements amongst the 18 provinces of the country, rates
of in- and out-migration in each province and their net migration balances. Also,
it discusses the spatial structure of the movement, and some gaining and losing
provinces are singled out. The impacts of the movements and their selective nature
are also revealed.
Rural-urban migration to the capital city of Khartoum is studied using the
1983 census data, other published data and the author's 1988/89 survey of migrant
households in the city. The scale of the migration and the characteristics of the
migrants are analyzed. Additionally, the structure of the migrant households,
literacy, occupation contrasts and links with the village are investigated. The
reasons behind the migration decision and the reward of the rural-urban migration
are also shown.
Seasonal migration is discussed to disclose the nature of the movement and
its patterns which are associated with the cotton picking operation in the Gezira
ll
scheme. The types of labour involved and labour market conditions are also investigated.
The findings verified the seasonality of the movement to the scheme and
the consistent relationship between migrants and tenants in the scheme.
the inequality of geographical distribution of
health centers, hospitals, hospital-beds and
physicians among the thirteen administrative
areas that comprises the map of the Kingdom of
Saudi Arabia (KSA). It uses the Lorenz curves
and the Gini coefficient.
Methods: A data matrix of the thirteen
administrative areas by four health resources was
constructed based on the available government
data of the Central Department of Statistics and
Information (CDSI). This data matrix is for 22
years starting from 1992 to 2013 for the health
resources with the exception of the data for
physicians, which is available up to 2007. To
obtain the relative share of an area of each health
resource, the total number of it is divided by
the corresponding total population of the area.
Total populations, of administrative areas, were
projected using the between censuses declared
growth rates. Human population calculator was
used to run the projection at this website. Thus,
relative shares are used instead of numbers
because they provide a more realistic picture.
Lorenz curves were constructed depending on
excel, while Gini coefficients were calculated
using an online Gini calculator.
Drafts by Abdelhalim E El-Farouk
الجغ ا رفيا والتنمية المستدامة وذلك بحسب ما ورد في "إعلان لوزيرن" الصادر بعنوان "إعلان ح ول
التعليم الجغ ا رفي من أجل تنمية مستدامة" في يوليو 7002 ، والذي أصدرته لجنة التعليم الجغ ا رفي
بتفويض من الجمعية الجغ ا رفية العالمية استجابة وتنفيذا لخطة الأمم المتحدة العشرية تحت شعار
– .)7002 "تعليم من أجل تنمية مستدامة ) 7002
population growth, a high trend of spatial concentration, excessive migration and misuse of resources. The result is a rapid degradation of the natural resource base, recent spread of famines, malnutrition, degraded health conditions, and disturbance of social and health conditions. It is important to throw light on these issues to promote awareness of the need for a comprehensive population-environment
policy that takes into account the impact of this environmental
degradation on the population and the measures that can be taken to combat this threat.
Material and Method: Data used in this study have been collected from different sources that are mentioned in the text when come by. Variables included in this study include per capita GDP, number of physicians, population age structure variables; (population under 15 and/or over 65), infant mortality rate (IMR), population growth and lagged healthcare expenditure. These variables have been singled out by this study as the key determinants of health care expenditure. Variables have been displayed in their descriptive formats to check for their minimum, maximum, range, mean, standard deviation and coefficient of variation. A correlation matrix was produced to check for relationships between the variables. A stepwise regression method was adopted to described determine the factors that most affect and determine health care expenditure in KSA.
Results: Our study findings revealed that the GDP per capita and the lagged health care expenditures (i.e. per capita healthcare expenditure of the previous year) are the major two factors that affect health care expenditure levels. Together, the two variables are responsible for 93.3% of the variations in the per capita health care expenditure. The regression model excluded all of the remaining variables because of the fact that they are statistically insignificant and do not fit with the model at a (0.05) level of significance. They do not contribute significantly to the explanation of the per capita health care spending variation. These variables are number of physicians, annual population growth rate, percent of the population aged 65 years and older, percent of the population under 15 years and infant mortality rate.
ولقد أبرزت الدراسة العملية التي طبقت على مراكز الاستيطان في منطقة مكة المكرمة أن النتائج النهائية لأي دراسة تتعلق بحساب قد تخلص إلى نتائج خاطئة إذا لم يؤخذ عدد نقاط التوزيع في الاعتبار عند تفسير القيمة النهائية لهذا المعامل. وهنا نوهت الدراسة إلى أهمية الأخذ في الاعتبار لعدد نقاط التوزيع وشرحت الكيفية لذلك. كما أكدت الدراسة على وجوب إخضاع قيمة المعامل على الاختبار الإحصائي Z وذلك لاختبار فرض العدم الذي يقول إن نمط توزيع النقاط محور الدراسة نمط عشوائي (حتى وإن كانت قيمة أكبر من واحد صحيح) بينما ينص الفرض البديل على أن نمط توزيع النقاط محور الدراسة نمط غير عشوائي .
seen in its vastness of the area, its low population density, its high population fertility,
decreasing mortality rates and uneven distribution of developmental projects
among its regions. All these have important effects upon the economic and demographic
characteristics of its population. They also underline the uneven distribution
of the country's population, as a result of which large-scale migration
movements occur.
Three types of population movements in the Sudan are discussed; inter-provincial
movements, rural-urban migration and seasonal migration for cotton picking in the
Gezira scheme. In the discussion of the first type, the analysis covers issues related
to the general levels of movements amongst the 18 provinces of the country, rates
of in- and out-migration in each province and their net migration balances. Also,
it discusses the spatial structure of the movement, and some gaining and losing
provinces are singled out. The impacts of the movements and their selective nature
are also revealed.
Rural-urban migration to the capital city of Khartoum is studied using the
1983 census data, other published data and the author's 1988/89 survey of migrant
households in the city. The scale of the migration and the characteristics of the
migrants are analyzed. Additionally, the structure of the migrant households,
literacy, occupation contrasts and links with the village are investigated. The
reasons behind the migration decision and the reward of the rural-urban migration
are also shown.
Seasonal migration is discussed to disclose the nature of the movement and
its patterns which are associated with the cotton picking operation in the Gezira
ll
scheme. The types of labour involved and labour market conditions are also investigated.
The findings verified the seasonality of the movement to the scheme and
the consistent relationship between migrants and tenants in the scheme.
the inequality of geographical distribution of
health centers, hospitals, hospital-beds and
physicians among the thirteen administrative
areas that comprises the map of the Kingdom of
Saudi Arabia (KSA). It uses the Lorenz curves
and the Gini coefficient.
Methods: A data matrix of the thirteen
administrative areas by four health resources was
constructed based on the available government
data of the Central Department of Statistics and
Information (CDSI). This data matrix is for 22
years starting from 1992 to 2013 for the health
resources with the exception of the data for
physicians, which is available up to 2007. To
obtain the relative share of an area of each health
resource, the total number of it is divided by
the corresponding total population of the area.
Total populations, of administrative areas, were
projected using the between censuses declared
growth rates. Human population calculator was
used to run the projection at this website. Thus,
relative shares are used instead of numbers
because they provide a more realistic picture.
Lorenz curves were constructed depending on
excel, while Gini coefficients were calculated
using an online Gini calculator.
الجغ ا رفيا والتنمية المستدامة وذلك بحسب ما ورد في "إعلان لوزيرن" الصادر بعنوان "إعلان ح ول
التعليم الجغ ا رفي من أجل تنمية مستدامة" في يوليو 7002 ، والذي أصدرته لجنة التعليم الجغ ا رفي
بتفويض من الجمعية الجغ ا رفية العالمية استجابة وتنفيذا لخطة الأمم المتحدة العشرية تحت شعار
– .)7002 "تعليم من أجل تنمية مستدامة ) 7002