scholarly journals Factors influencing migration and settlement of pastoralists in Nairobi City, Kenya

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Walter Magero Wafula ◽  
Oliver Vivian Wasonga ◽  
Oscar Kipchirchir Koech ◽  
Staline Kibet
Keyword(s):  
Land Tenure ◽  
Binary Logistic Regression ◽  
Policy Formulation ◽  
Snowball Sampling ◽  
Binary Logistic Regression Model ◽  
Grazing Land ◽  
Pastoral System ◽  
Pastoral Production ◽  
Alternative Markets ◽  
The City

AbstractPastoralism is globally recognized as the backbone of the economy in the vast arid and semi-arid rangelands. Despite its enormous economic contribution, the system is facing a myriad of challenges, among them, land use and land tenure changes, resulting in diminished grazing land. Accompanying such changes is the conversion of traditional grazing lands into other uses such as settlements, with urbanization being one of the key drivers of pastoral system dynamics. Understanding such dynamics in the face of compounding factors such as frequent droughts linked to climate change is key in guiding policy formulation and interventions aimed at achieving a sustainable pastoral production system. This study investigated factors determining migration and settlement of pastoralists in Nairobi City of Kenya. Data was collected through a snowball sampling approach using semi-structured household questionnaires, focus group discussions (FGDs) and key informant interviews (KIIs) in five Sub-counties of Nairobi City County. A total of 144 household interviews, 6 FGDs and 16 KIIs were conducted to elucidate drivers of urban pastoralism, opportunities and challenges encountered by pastoralists in the city. A binary logistic regression model was used to analyse the determinants of their migration. Results show that search for pasture and water resources, and alternative markets especially during droughts, are the main reasons for pastoralists’ migration to the city. In addition, educated herders were found to be more likely to migrate to the city as they pursue wage employment. Whereas these findings revealed that migration to the city exposes pastoralists to diverse livelihood opportunities, they are equally faced by a number of challenges - mainly road accidents involving livestock, frequent land displacements to pave way for development of real estates, and livestock poisoning from sewage and garbage wastes. There is a need for policy and regulatory interventions to recognize pastoralism alongside other forms of urban farming and addressing challenges facing sustainable pastoral production.

scholarly journals PREDIKSI SPASIAL DINAMIKA AREAL TERBANGUN KOTA SEMARANG DENGAN MENGGUNAKAN MODEL REGRESI LOGISTIK

2016 ◽  
Vol 18 (2) ◽  
pp. 95 ◽  
Author(s):  
Irmadi Nahib
Keyword(s):  
Land Use ◽  
Logistic Regression ◽  
Cellular Automata ◽  
Land Cover ◽  
Open Space ◽  
Land Use Changes ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Model ◽  
Rate Of Increase ◽  
The City

<p class="JudulABSInd"><strong>ABSTRAK</strong></p><p class="abstrak">Salah satu indikator perkembangan fisik wilayah kota dapat diidentifikasi melalui fenomena perubahan tutupan lahan bervegetasi menjadi lahan terbangun. Perubahan lahan tersebut dapat berdampak terhadap penurunan kualitas lingkungan, akibat berkurangnya ruang terbuka hijau. Kota Semarang dengan visi terwujudnya Semarang sebagai kota perdagangan dan jasa yang berbudaya menuju masyarakat sejahtera, merupakan  wilayah yang rentan mengalami perubahan penggunaan lahan yang cenderung kearah lahan terbangun. Penelitian ini mengintegrasikan model <em>Cellular Automata</em> (CA) dan regresi logistik biner untuk memprediksi dinamika lahan terbangun di Kota Semarang. Citra yang digunakan adalah Citra Ikonos 2002, Ikonos 2006 dan <em>Quic</em><em>kbird</em> 2012. Model CA pada penelitian ini digunakan untuk memprediksi sebaran penutup lahan tahun 2022 dan 2032 dengan mempertimbangkan jarak terhadap jalan, jarak terhadap sungai, jarak terhadap lahan terbangun, ketinggian, kepadatan penduduk, <em>evidence likelihood </em>perubahan lahan dan indeks pengembangan kelurahan yang diakomodasi dalam peta sub-model transisi hasil model regresi logistik biner. Hasil penyusunan model ini adalah peta prediksi penutup lahan dengan akurasi 78,21 % validitas model yang dihasilkan dapat dikategorikan “<em>moderate</em>” mengindikasikan bahwa peta yang dihasilkan dapat digunakan. Hasil pemodelan menunjukkan bahwa Kota Semarang pada tahun 2022 terjadi pertambahan luas lahan terbangun rata-rata 284 ha/tahun dan pada tahun 2032 rata-rata 226 ha/tahun.</p><p><strong><em>Kata </em></strong><strong><em>k</em></strong><strong><em>unci</em></strong><em>: </em><em>cellular automata, pemodelan, regresi logistik biner, lahan terbangun</em></p><p class="judulABS"><em><strong>ABSTRACT</strong></em></p><p class="Abstrakeng">One indicator of the physical development of the city can be identified by phenomenon of land expansion, vegetated land cover changes to be built-up area. The land use changes can impact to environmental degradation, due to reduced green open space. Semarang as a city of trade and services cultured toward a prosperous community, a region that is vulnerable to changes in land use tends toward small plots. This research integrates the model of Cellular Automata (CA) and binary logistic regression to predict the dynamics of builtup area in the city of Semarang. The image used is a Ikonos imagery (2002), Ikonos imagery (2006) and Quickbird (2012). Model CA in this research use to predict the distribution of land cover 2022 and 2032 with respect to: distance to roads, the distance to the river, the distance to the built-up area, elevation, population density, evidence likelihood of land use change and development villages index were accommodated in the map sub-model transition binary logistic regression model results. The results of this study are predictive maps of built-up area  with an accuracy of 78,21 % so that the validity of the resulting model can be categorized as "moderate", indicates that the probability map is valid. Modeling results showed that Semarang City in 2022 predicted rate of increase of  built-up area an average 284  ha / year and in 2032 rate of increase of built-up area an average 226 ha / year.</p><p><strong><em>Keywords</em></strong><em>: cellular automata, modelling, binary logistic regression, built-up area</em></p>

Performance of Small Businesses in Tanzania: Human Resources-Based View

2020 ◽  
pp. 097215092092735
Author(s):  
Robert Galan Mashenene ◽  
Neema P. Kumburu
Keyword(s):  
Human Resources ◽  
Small Businesses ◽  
Behavioural Change ◽  
Binary Logistic Regression ◽  
Sampling Technique ◽  
Snowball Sampling ◽  
Binary Logistic Regression Model ◽  
Multi Stage ◽  
Data Content ◽  
Training Programmes

This study examined the performance of small businesses in Tanzania using human resources-based approach. A sample of 380 small business owners was surveyed using a structured questionnaire. Multi-stage sampling technique was used, whereas proportionate stratified sampling was applied which was followed by random sampling technique. In addition, snowball sampling technique was used to select cases of interest for in-depth interview. Excel and a Statistical Package for Social Sciences (SPSS) were used as analytical tools for quantitative data. Content analysis was used to analyse qualitative data from four case studies developed. Binary logistic regression model was used to estimate the effects of employees’ commitment, trustfulness and competencies on the performance of small businesses. The study revealed that employees’ commitment, honesty and competencies had a significant positive effect on the performance of small businesses. The study recommends that special training programmes should be designed and implemented for the purpose of imparting competencies and behavioural change to employees of small businesses.

scholarly journals Urban Intersections and Traffic Safety in the City of San Antonio

2019 ◽  
Vol 271 ◽  
pp. 06003
Author(s):  
Qasim Adegbite ◽  
Khondoker Billah ◽  
Hatim Sharif ◽  
Samer Dessouky
Keyword(s):  
Traffic Safety ◽  
Traffic Control ◽  
San Antonio ◽  
Binary Logistic Regression ◽  
Crash Severity ◽  
Binary Logistic Regression Model ◽  
Crash Frequency ◽  
Crash Rates ◽  
Traffic Control System ◽  
The City

Intersections are high-risk locations on roadways and often experience high incidence of crashes. Better understanding of the factors contributing to crashes and deaths at intersections is crucial. This study analyzed the factors related to crash incidence and crash severity at intersections in San Antonio for crashes from 2013 to 2017 and identified hotspot locations based on crash frequency and crash rates. Binary logistic regression model was considered for the analysis using crash severity as the response variable. Factors found to be significantly associated with the severity of intersection crashes include age of driver, day of the week, month, road alignment, and traffic control system. The crashes occurred predominantly in the highdensity center of the city (downtown area). Overall, the identification of risk factors and their impact on crash severity would be helpful for road safety policymakers to develop proactive mitigation plans to reduce the frequency and severity of intersection crashes.

Bureaucrats, Cattle, and Public Policy

1980 ◽  
Vol 13 (3) ◽  
pp. 313-356 ◽  
Author(s):  
Louis A. Picard
Keyword(s):  
Public Policy ◽  
Land Use ◽  
Land Tenure ◽  
Policy Formulation ◽  
General Interest ◽  
African Countries ◽  
Grazing Land ◽  
Using Data ◽  
The Third World ◽  
The Relationship

The formulation of a policy on land use offers a clear example of the relationship between a bureaucracy and policy formulation. In much of the Third World, and particularly in many African countries, national and local government employees are to all intents and purposes the dominant socioeconomic elite in the country. This would suggest that those who are formulating and implementing a new policy such as that on land use may have the most to gain from the policy which is under examination. Using data from the Southern African country of Botswana, this article examines the relationship between the socioeconomic status of the bureaucracy and the type of land tenure policy which is likely to result. Since the movement from communal to individual tenure of both grazing land and farm land is occurring throughout the African continent, the relationship between bureaucratic interests and public policy is of general interest beyond this specific case study.

scholarly journals A Binary Logistic Analysis on Factors Affecting the Participation of Smallholder Farmers in the Market of Indigenous Chickens (ICs)

2020 ◽  
Vol 58 (4) ◽  
Author(s):  
Lesiba Andries Sekele ◽  
Jenny Potsiso Mokhaukhau ◽  
Machuene Sharlyn Cholo ◽  
Anele Mayekiso
Keyword(s):  
Smallholder Farmers ◽  
Binary Logistic Regression ◽  
Snowball Sampling ◽  
Binary Logistic Regression Model ◽  
Indigenous Chicken ◽  
Factors Affecting ◽  
Indigenous Chickens ◽  
Logistic Analysis ◽  
Factors Influencing ◽  
Female Farmers

Participation in the market of Indigenous Chickens (ICs) enables farmers an opportunity to generate income and alleviate household food insecurity. The objectives of the study were to profile the socioeconomic characteristics of smallholder farmers and; to determine the factors influencing participation of smallholder farmers in the market of ICs. The study employed purposive and snowball sampling techniques to identify 50 indigenous chicken farmers in Makhuduthamaga Local Municipality, South Africa. Descriptive statistics and Binary Logistic regression model were employed to address the objectives of the study. The results revealed that, most smallholder farmers do not participate in the market of ICs. Moreover, experience in rearing chickens, land size, number of chickens owned, and purpose of rearing the chickens were found as factors influencing participation of farmers in the market of ICs. The study also confirmed that female farmers dominate in the market of ICs when compared to males. This, therefore, calls for government intervention in the promotion of ICs markets to encourage participation by farmers thus improving livelihoods.

scholarly journals Factors Affecting the Adoption of Water Harvesting Technologies: A Case Study of Jordanian Arid Area

2016 ◽  
Vol 6 (1) ◽  
pp. 80 ◽  
Author(s):  
Samia Akroush ◽  
Boubaker Dehehibi ◽  
Bezaiet Dessalegn ◽  
Omamah Al-Hadidi ◽  
Malek Abo-Roman
Keyword(s):  
Land Tenure ◽  
Significant Relationship ◽  
Binary Logistic Regression ◽  
Water Harvesting ◽  
Binary Logistic Regression Model ◽  
Institutional Variables ◽  
Factors Affecting ◽  
Adoption And Diffusion ◽  
Farmer Education ◽  
Privately Owned

In this article, we investigate the determinants of farmers’ decisions to adopt water harvesting technologies (WHT) in the arid agricultural area of Jordan. In particular, we investigate the effect of different socio-demographic, economic, and institutional factors on the adoption of WHT. For doing so, we empirically apply a binary logistic regression model on a micro-dataset (59 farmers) in Jordanian Badia. Empirical findings indicate that there is no significant relationship between age and the probability of adoption of WHT. However, our findings show significantly positive relationships at 10% level for farmer education and experience which implies that farmers with higher education and experience level are more likely to adopt WHT. In contrast, it was found that labor and institutional variables such as credit services do not significantly influence adoption of WHT. Results also reveal a significant relationship between land tenure and adoption implying higher adoption rates on communal land as opposed to privately owned land. Based on our empirical results, this research will assist decision makers to prioritize the factors influencing adoption of WHT and provide insights for targeted dissemination, adoption, and diffusion of WHT in the Jordanian arid areas.

Credit constraints and Rural Migration: Evidence from Six Villages in Uttar Pradesh

2018 ◽  
Vol 15 (3) ◽  
pp. 389-398
Author(s):  
Ruchi Singh
Keyword(s):  
Credit Constraints ◽  
Uttar Pradesh ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Model ◽  
Male Migration ◽  
Rural Economies ◽  
And Migration ◽  
The Relationship ◽  
The Empirical Analysis

Rural economies in developing countries are often characterized by credit constraints. Although few attempts have been made to understand the trends and patterns of male out-migration from Uttar Pradesh (UP), there is dearth of literature on the linkage between credit accessibility and male migration in rural Uttar Pradesh. The present study tries to fill this gap. The objective of this study is to assess the role of credit accessibility in determining rural male migration. A primary survey of 370 households was conducted in six villages of Jaunpur district in Uttar Pradesh. Simple statistical tools and a binary logistic regression model were used for analyzing the data. The result of the empirical analysis shows that various sources of credit and accessibility to them play a very important role in male migration in rural Uttar Pradesh. The study also found that the relationship between credit constraints and migration varies across various social groups in UP.

Prevalence of facemask use among general public when visiting wet market during Covid-19 pandemic: An observational study. (Preprint)

2020 ◽  
Author(s):  
Gobi Hariyanayagam ◽  
Sera Selvanthan Sundram Gunasekaran ◽  
Shargunan Selvanthan Gunasekaran ◽  
Nur Syafina Insyirah Zaimi ◽  
Nor Amirah Abdul Halim
Keyword(s):  
High Risk ◽  
General Population ◽  
Binary Logistic Regression ◽  
Final Analysis ◽  
High Risk Population ◽  
Binary Logistic Regression Model ◽  
Chi Square ◽  
Hygiene Measures ◽  
Wet Market ◽  
Medical Grade

BACKGROUND In late December 2019, an outbreak of a novel coronavirus disease (COVID-19; previously known as 2019-nCoV) was epidemiologically linked to seafood and wet animal market in Wuhan, Hubei, China. This event has instigated negative stigma among the general population to view the wet market as a high-risk location for potential transmission of coronavirus. OBJECTIVE This study investigated the prevalence of facemask use among general population visiting the wet market as well as factors contributing to unacceptable facemask practice. Setting The visitors to a district wet market selling range of live or freshly slaughtered animals during COVID-19 pandemic outbreak was observed for facemask practice. METHODS All Individuals visiting the market were observed for the type, category and practice of wearing facemas. Subjects were categorized into two groups of acceptable and unacceptable facemask practice. The Pearson chi-square was used to test for differences in investigated variables in the univariate setting and Binary Logistic regression model was used in the multivariate setting. Main outcome measure Prevalence, acceptance practice and odds ratio of unacceptance of facemask use. RESULTS Among 1697 individuals included in the final analysis, 1687 (99.7%) was observed wearing facemask with 1338 (78.8%) using medical-grade facemask. Among them, 1615 (95.7%) individuals facemask practice was acceptable while the reaming 72 (4.3%) individuals were observed with unacceptable facemask practice. Individuals using medical-grade facemask and high-risk age group are 6.4 times (OR=6.40; 95% CI, 2.00-20.43; p=.002) and 2.06 times practice (OR=2.06; 95% CI, 1.08-3.94; p=.028) more likely to have unacceptable facemask practice respectively. CONCLUSIONS High saturation of facemask among the general population is an adequate indicator of public hygiene measures strategy which can help to mitigate the COVID-19 epidemic impact. Alarmingly, the unacceptable facemask practice among high-risk population raises the need for a targeted approach by healthcare authorities to ensure satisfactory facemask use.

scholarly journals Least Likely Observations in Regression Models for Categorical Outcomes

2002 ◽  
Vol 2 (3) ◽  
pp. 296-300 ◽  
Author(s):  
Jeremy Freese
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Binary Logistic Regression ◽  
Positive Outcome ◽  
Ordinary Least Squares ◽  
Parameter Estimates ◽  
Least Squares Regression ◽  
Binary Logistic Regression Model ◽  
Ordered Logistic Regression ◽  
Negative Outcome

This article presents a method and program for identifying poorly fitting observations for maximum-likelihood regression models for categorical dependent variables. After estimating a model, the program leastlikely will list the observations that have the lowest predicted probabilities of observing the value of the outcome category that was actually observed. For example, when run after estimating a binary logistic regression model, leastlikely will list the observations with a positive outcome that had the lowest predicted probabilities of a positive outcome and the observations with a negative outcome that had the lowest predicted probabilities of a negative outcome. These can be considered the observations in which the outcome is most surprising given the values of the independent variables and the parameter estimates and, like observations with large residuals in ordinary least squares regression, may warrant individual inspection. Use of the program is illustrated with examples using binary and ordered logistic regression.

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