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One of the most celebrated findings in complex systems in the last decade is that different indexes y (e.g. patents) scale nonlinearly with the population x of the cities in which they appear, i.e. y∼xβ,β≠1. More recently, the generality of this finding has been questioned in studies that used new databases and different definitions of city boundaries. In this paper, we investigate the existence of nonlinear scaling, using a probabilistic framework in which fluctuations are accounted for explicitly. In particular, we show that this allows not only to (i) estimate β and confidence intervals, but also to (ii) quantify the evidence in favour of β≠1 and (iii) test the hypothesis that the observations are compatible with the nonlinear scaling. We employ this framework to compare five different models to 15 different datasets and we find that the answers to points (i)–(iii) crucially depend on the fluctuations contained in the data, on how they are modelled, and on the fact that the city sizes are heavy-tailed distributed.
1. Introduction
The study of statistical and dynamical properties of cities from a complex-systems perspective is increasingly popular [1]. A celebrated result is the scaling between a city-specific observation y (e.g. the number of patents filed in the city) and the population x of the city as [2] 1.1with a non-trivial (β≠1) exponent. Super-linear scaling (β>1) was observed when y quantifies creative or economic outputs and indicates that the concentration of people in large cities leads to an increase in the per capita (y/x) production. Sublinear scaling (β<1) was observed when y quantifies resource use and suggests that large cities are more efficient in the per capita (y/x) consumption. Since its proposal, nonlinear scaling has been reported in an impressive variety of different aspects of cities [3–9]. It has also inspired the proposal of different generative processes to explain its ubiquitous occurrence [10–14]. Scalings similar to the one in equation (1.1) appear in physical (e.g. phase transitions) and biological (e.g. allometric scaling) systems suggesting that cities share similarities with these and other complex systems (e.g. fractals).
More recent results cast doubts on the significance of the β≠1 observations [15–17]. Reference [15] agrees that economic outputs are faster than linear in x, but claims that the population x has a limited explanatory factor on the per capita rate y/x of cities and function (1.1) is not better than alternative ones (see [6,18] for opposing arguments). Reference [16] focuses on the case of CO2 emissions and shows that depending on whether city boundaries or metropolitan areas are used, the value of β changes from β>1 to β<1. This point was carefully analysed in [17] for different datasets y. Through a careful study of different possible choices of city boundaries, the authors report that the evidence for β≠1 virtually vanishes. These results ask for a more careful statistical analysis that rigorously quantifies the evidence for β≠1 in different datasets.
In this paper, we propose a statistical framework based on a probabilistic formulation of the scaling law (1.1) that allows us to perform hypothesis testing and model comparison. In particular, we quantify the evidence in favour of β≠1 comparing (through the Bayesian information criterion, BIC) models with β≠1 to models with β=1. We apply this approach to 15 datasets of cities from five regions and find that the conclusions regarding β vary dramatically not only depending on the datasets, but also on assumptions of the models that go beyond (1.1). We argue that the estimation of β is challenging and depends sensitively on the model because of the following two statistical properties of cities:
(i) the distribution of city population has heavy tails (Zipf's law) [1,19] and
(ii) there are large and heterogeneous fluctuations of y as a function of x (heteroscedasticity).
Points (i) and (ii) are shown, respectively, in figure 1a,b.
The paper is organized as follows. We start by describing the problem and the datasets we use (in §2) and discussing (in §3) the limitations of the usual statistical approach based on least-squares fitting in log-scale. We then propose a probabilistic formulation together with different statistical models (in §4) and describe (in §5) how they can be compared with each other and to data. Finally, we discuss our main findings (in §6) and summarize our conclusions (in §7).
2. Data
The general problem we are interested in is to test and estimate the parameters of equation (1.1) based on observations (xi,yi) for i=1,…,N cities, where xi is the population and yi is the amount of the quantity of interest in city i (as in figure 1b). The quantities xi,yi are estimated within a measurement precision which, in principle, could also be included in the analysis. However, in most cases, this information is not available, and only single measurements of xi,yi exist. The datasets we choose include a variety of different regions, aggregation methods to define city boundaries, and quantities y. They include the data from various countries and regions: 100 metropolitan areas of the United Kingdom (UK), aggregated as in [17]; 381 metropolitan areas of the United States of America (USA), as discussed in [12]; 459 urban areas of the USA; 472 large cities of the European Union (EU); 275 large cities from the members of the Organization for Economic Co-operation and Development (OECD); and 5565 municipalities (administrative units) from Brazil. For each database, we use indexes of economical activity (weekly income, GDP), innovation (patents filed), transportation (miles travelled, number of train stations), access to culture (number of theatres, number of cinema seats, number of cinema attendances in 1 year, etc.) and health condition (AIDS infections, death by external causes). Further details are presented in appendix A.
3. Limitations of the usual statistical analysis
The following three steps summarize the usual approach used to test a nonlinear scaling in equation (1.1) (see [2–4,8,11,12,16–18] for scalings in cities and [20] for scalings in biology):
The parameters of equation (1.1) are chosen based on least-squares fitting in log-transformed data , i.e. α,β are such that is minimized.
The quality of the fitting is quantified by the coefficient of determination . R2 close to 1 is taken as evidence of the agreement between the fit and the data.
The 95% confidence interval [βmin,βmax] around β is computed from the sum of the residuals squared and β∉[βmin,βmax] is taken as an evidence that β≠1.
This usual approach is appealing because of its simplicity and ease of numerical implementation. However, it contains the following assumptions and limitations that are usually ignored:
The parameters obtained through least-squares fitting are maximum-likelihood estimators if (i) the data points are independent and (ii) the fluctuations around the mean , , are Gaussian distributed in with a variance independent of . The value of β obtained in the usual approach is meaningful if these assumptions hold.
R2 does not quantify the statistical significance of the model, it quantifies the correlation between data and model (the amount of the variation in the data explained by the model). In particular, R2 close to one is not an evidence that the data are a likely outcome of the model. Below, we obtain that datasets are typically not consistent with the model underlying the usual approach.
The confidence interval [βmin,βmax] is a range in which the true value of β is expected to be found only if the model holds [21]. Therefore, in the typical case in which the data are not compatible with the model, one cannot conclude that β≠1 based on the observation that 1∉[βmin,βmax]. Usually, in this case, both β=1 and β≠1 are incompatible with the data.
A further limitation of the usual approach is that it requires removing the datapoints with yi=0 (because it requires computing ). This filtering is arbitrary, because y=0 is usually a valid observation (e.g. cities without any patents filed).
In the study of scaling laws in biology, the underlying hypothesis and alternatives to the usual least-squares fitting have been extensively discussed [22,23]. In city data, statistical analysis beyond the usual approach was performed in [3,5,7,8,10]. It typically amounts to an analysis of the residuals , e.g. a (visual) comparison of the residuals of the fit to the Gaussian distribution predicted by the model underlying the linear fit in log–log scale. The controversies regarding a nonlinear scaling β≠1 motivate us to search for an alternative statistical framework to test the scaling (1.1) beyond the usual approach with residual analysis.
4. Probabilistic models
The statistical analysis we propose is based on the likelihood of the data being generated by different models. Following [5], we assume that the index y (e.g. number of patents) of a city of size x is a random variable with probability density P(y|x). We interpret equation (1.1) as the scaling of the expectation of y with x 4.1where is computed over the ensemble of cities with fixed x. This relation does not specify the shape of P(y|x), e.g. it does not specify how the fluctuations of y around scale with x. Here, we are interested in models P(y|x) satisfying 4.2This choice corresponds to Taylor's law [24]. It is motivated by its ubiquitous appearance in complex systems [25], where typically δ∈[1,2], and by previous analysis of city data that reported non-trivial fluctuations [8,26,27]. The fluctuations in our models aim to effectively describe the combination of different effects, such as the variability in human activity and imprecisions on data gathering. In principle, these effects can be explicitly included in our framework by considering distinct models for each of them.
Below, we specify different models P(y|x) compatible with equations (4.1) and (4.2). We consider two classes of models. In the first class, which we call city models, we a priori choose a parametric form for P(y|x), and we use equations (4.1) and (4.2) to fix the free parameters. In the second class, which we call person models, we derive P(y|x) from a generative process for the assignment of y to people that is compatible with equations (4.1) and (4.2). In both cases, the likelihood of the model is written as a function of the data {(xi,yi)}i=1,…,N and at most four free parameters (α,β,γ and δ).
4.1. City models
In this class of models, we assume that each data point yi is an independent realization from the conditional distribution P(y|xi) and therefore the log-likelihood can be written as 4.3In order to explore how the choice of P(y|x) affects the outcome of the statistical analysis, we consider two different continuous distributions (Gaussian and lognormal).1
4.1.1 Gaussian fluctuations
Here, we consider that P(y|x) is given by a Gaussian distribution with parameters and 4.4The relations (4.1) and (4.2) are fulfilled choosing the parameters as 4.5The log-likelihood (4.3) is given by 4.6This model has P(y≤0|x)>0 and therefore observations with yi≤0 can be accounted for. For the observables considered here, y=0 is a valid observation but y<0 is not.
We consider two cases:
Fixed δ=1. This is the typical fluctuation scaling found when yi is the result of a sum of random variables [25].
Free δ∈[1,2]. The general functional form that fulfils equation (4.2). We exclude δ>2, because, in this case, the probability P(y<0|x) of negative values (not feasible for most observables y) remains large for large x.
4.1.2. Lognormal fluctuations
Here, we consider that P(y|x) is given by a lognormal distribution with parameters and : 4.7The relations (4.1) and (4.2) are fulfilled choosing the parameters as (see appendix B) 4.8The log-likelihood (4.3) is given by 4.9This model has P(y≤0|x)=0 and therefore observations with yi≤0 cannot be accounted for. We again consider two cases:
Fixed δ=2. This scaling is obtained when yi is the product of independent random variables. Furthermore, and the fluctuations of are independent of x and therefore the maximum-likelihood estimation of β coincides with the estimation obtained with minimum least-squares for , as discussed in §3.
Free δ∈[1,3]. The general functional form that fulfils equation (4.2).
4.2. Person model
The starting point for this class of models is the natural interpretation of equation (1.1) that people's efficiency (or consumption) scales with the size of the city they are living in. This motivates us to consider a generative process in which tokens (e.g. a patent, a dollar of GDP, a mile of road) are produced or consumed by (assigned to) individual persons, in the same spirit as in [12,14]. Specifically, consider j=1,…,M persons living in i=1,…,N cities, on which the population of the city i is given by xi such that . Consider also that there is a total of k=1,…,Y tokens that are randomly assigned to the M persons. A super-linear (sublinear) scaling suggests that a token is more likely to be assigned to someone living in a more (less) populous city. In this spirit, we assume that the probability that a token is assigned to person j depends only on the population x(j) of the city where person j lives as 4.10where Z(β) is the normalization constant, i.e. . For β=1, p( j)=1/M and each person is equally likely to be assigned a token (independently of the population of its city). Equation (4.10) is a microscopic model, and we are now interested in the macroscopic behaviour of the city: the probability that a city i gets yi tokens, given that its population is xi. Assuming that besides their city, individuals are indistinguishable, the probability p(i) that a token is assigned to a city i is given by a sum of p(j) over persons j on city i, which contains exactly xi terms. Because x(j)=xi when the person j lives in city i, represented by j∈i, we obtain 4.11The probability of observing yi tokens in each city of size xi is a multinomial distribution 4.12Thus, the likelihood can be written as a function of the observed quantities (xi,yi) as 4.13The scaling of the average and variance of y, i.e. equations (4.1), (4.2), is recovered as 4.14where we identify that α=Y/Z(β), γ=1 and δ=1. For yi≫1, this model coincides with the city model with normal fluctuations and the latter choice of parameters. Note that the fluctuations of this model account only for fluctuations of the assignment, and neglects potential fluctuations of measurement imprecisions.
5. Results
In this section, we compare the models presented above against our 15 datasets. In particular, we address the following questions whose answers are summarized in table 1:
1. What is the estimated value of β?
For each model, we calculate the parameters (α,β,γ,δ) that maximize (see appendix C for details). In table 1, we report β.
2. What is the error bar b around the estimated β?
We estimate b using bootstrapping with replacement (see appendix D for details). In table 1, b is shown in parentheses. The interval [β−b,β+b] can be interpreted as the 95% confidence interval of β when the model is not rejected. Otherwise, it can be interpreted as the robustness of the estimated β against fluctuations in the data (cross validation).
Hypothesis testing
3. Are the data compatible with the model?
We test the hypothesis that the data were generated by the model. Specifically, for each model, we compute a p-value that quantifies (i) whether the fluctuations in the data are compatible with the expected fluctuations from the model and (ii) whether the residuals are uncorrelated (see appendix E for details). In the case the model is not rejected, i.e. p-value >0.05, the corresponding entry in table 1 is marked with an asterisk.
Model comparison
4. What is the statistical evidence for β≠1?
We quantify the evidence for β≠1 by comparing the maximum-likelihood of each model with the corresponding model where we fix β=1. We account for the different number of free parameters (e.g. to avoid overfitting) by using the BIC, , where k is the number of free parameters and N the number of observations (see appendix F for details). The difference in the BIC, ΔBIC≡BICβ=1−BICβ indicates whether the model with β≠1 provides a sufficiently better description of the data. From this we infer that, for (i) ΔBIC<0 the model with fixed β=1 (linear scaling) is better, (ii) 0≤ΔBIC<6 the evidence for β≠1 is inconclusive, and (iii) ΔBIC≥6 the model with β≠1 (nonlinear scaling) is better. In table 1, these results are indicated by the symbols (i) (linear), (ii) open circle (inconclusive), or (iii) ↘ (sublinear) or ↗(super-linear).
5. What is the statistical evidence for fluctuation scaling (Taylor's law)?
We quantify the evidence for δ≠1 (δ≠2), i.e. non-trivial scaling in the fluctuations in equation (4.2), in the models of cities with Gaussian (lognormal) noise. Within each class, we calculate ΔBIC≡BICδ*−BICδ, where we compare the BICs of the model where (i) δ is fixed (BICδ*) and (ii) where δ is a free parameter (BICδ). In case of ΔBIC>0, the model with δ as a free parameter (non-trivial fluctuation scaling) provides a better description of the data (see appendix F for details). In table 1, the entry for the selected model is highlighted with a grey background.
6. Which model best describes the data?
We calculate the BIC of each of the five models (see appendix F for details) and select the one with the lowest BIC as the one that best describes the data. In table 1, the β of the selected model is printed in bold.
6. Discussion
In this section, we interpret the outcome of the statistical analysis summarized in table 1. We focus on specific findings and their significance to the problem of scaling in cities.
6.1. Data are almost never compatible with the proposed models
In almost all cases, the data are not a typical outcome of any of the five proposed models, leading to a rejection of the models (p-value <0.05). The only exceptions (marked by an asterisk in table 1) are the two lognormal models in UK-income and UK-train stations, and the Gaussian model with free δ for OECD-GDP. There are several possible reasons for the widespread rejection of the models: fluctuations of the data may differ from the fluctuations P(y|x) of the models (e.g. measurement errors are not correctly accounted for by P(y|x)); the observations are not independent (e.g. there are correlations between residuals and city size); different scalings are observed for small and large cities (as discussed in [28] and figure 3).
The rejections of the models considered here are a consequence of their strong simplifying hypothesis and show that the development of better models is needed in order to understand the observations and clarify the existence of the nonlinear scaling (1.1). It shows also that the estimated confidence interval cannot be used (in the rejected models) to discard a linear scaling β=1 [21]. Still, the widespread rejection of models does not imply that the nonlinear scaling (1.1) is rejected altogether because it is possible that the data are well described by another (unknown) model consistent with equation (4.1) but different from the ones considered here, e.g. having different fluctuations in P(y|x). These alternative models can have different fluctuation relations or can account for the known (e.g. spatial [3]) correlations in the data. In particular, the generative process underlying the person model could be generalized to account for other effects beyond city-size population (e.g. individuals could be segmented by income).
Even if most models are rejected, some models can still describe the data better than others (in terms of BIC). The conclusions drawn from such model comparison analysis depend on the used set of models and may change by the introduction of a better model in the future. Our investigations of scaling laws in cities in the next sections are mostly based on model comparison: we analyse which model and parameters best describe the data, with particular interest in the parameter β.
6.2. Different datasets are best described by different models
There is no single model that best describes all databases (the bold value in table 1 appears in different rows). A systematic observation on the 15 datasets is that the person model and the Gaussian model with fixed δ are never the best ones. This indicates that the fluctuations in the (large) cities are much larger than predicted by the scaling δ=1 used in both models. For the other models, there are databases in which they are the best models: the lognormal with fixed δ=2 is the best model in the three UK cases and for USA-GDP; the lognormal model with free δ is the best model for USA-roads and EU-cinema capacity; and the Gaussian model with free δ is the best for EU-cinema usage, OECD-GDP and EU-libraries. The inclusion of the additional parameter δ in the lognormal model, related to Taylor's law in equation (4.2), is considered beneficial in eight out of the 15 cases (shaded grey regions in the two first rows of table 1). Altogether, these results show that the model underlying the usual approach (lognormal with fixed δ) is often not the best model.
6.3. The estimated β depends on the model
Models consistent with the average scaling (4.1), but that have different assumptions regarding the fluctuations, can lead to different estimations of β. Consider the case of EU-cinema attendance. The value estimated from the lognormal model with fixed δ is β=1.46±0.19. It coincides with the usual approach (least-square fitting) and suggests a super-linear relation between the number of cinema visitors and the population of cities. However, if we allow for a different fluctuation scaling as in the lognormal model with free δ, a model that is preferred according to our BIC test, we obtain that β=1.00±0.30, i.e. a linear scaling. Conflicting conclusions are observed also in the EU-theatres database. The data and fittings for these two cases are shown in figure 2. Visual inspection of the graph can be misleading because of the log-scale and the different density of points, and shows the need for more careful (quantitative) statistical analysis. Altogether, the variation of β across different models shows that conclusions regarding β (e.g. β≠1) cannot be done independently from the analysis of the fluctuations. Considering also that different models are preferred for different databases (previous point), this confirms the practical importance of going beyond the usual approach (least-square fitting) both in terms of methods and models, as proposed in this paper.
6.4. Models are dominated either by the small or the large cities
The variation on the estimation of β across the different models can be better understood by analysing how the city size distribution shown in figure 1a influences the estimation of β. The least-square fitting minimizes the distance between the curve and the points in logarithmic scales (). Therefore, when data are viewed in the usual double logarithmic plot, the best curve will be the one that passes close to most points, i.e. it weights a village as much as a million-size city. The fit will be thus dominated by the large number of small cities. The disadvantage of this is that, even if the model describes well most cities, it may fail to describe the behaviour of most of the population. Our person model addresses this issue by giving the same weight to each person, leading to the problem of describing most people but potentially not most cities. To see this, consider the example of the 5565 Brazilian cities. Half of the Brazilian population lives in the 201 largest cities (3.6% of cities); yet, 50% of smallest cities account for only 8.2% of the total population. This is a direct consequence of the heavy-tailed distribution of city sizes, which holds in all our databases (figure 1a). Our city models with free δ in equation (4.2) allow cases beyond the least-squares fitting (δ=2) and person model (δ=1). The exponent δ controls how the variance of P(y|x) grows with x. A small variance for large x, obtained for small δ, will force the fitted curve (average) to pass close to the points of large cities. The weight of the large cities is inversely proportional to δ.
The general considerations above explain a great extent of the variation of β across the models observed in table 1. The values obtained for the Gaussian model with δ=1 and the person model are dominated by large cities, in the lognormal δ=2 case, they are dominated by small cities, whereas for the free δ models, it depends on which best δ is obtained. In the Brazil-AIDS dataset δ≥2 and β is dominated by the small cities (δ=2 in the Gaussian model and δ=2.79 in the lognormal model). Accordingly, the values of β for these two models in the second to last row of table 1 are β≪1 in agreement with the lognormal with δ=2 case and in contrast with the Gaussian δ=1 and person model which have β>1 and are dominated by the large cities. Figure 3 shows the results for this dataset and emphasizes how different models describe different city sizes. The same reasoning also explains the values of β of other databases reported in table 1 (e.g. all UK cases).
In summary, the 'weights' each statistical model attributes to cities have an impact on the estimated value of β and, in particular, on the visual agreement between the data and the fit in the usual double-logarithmic plots. When the scaling relation (4.1) holds for all x, the difference between the models will not be significant. However, as we showed in §6.1, data are typically not compatible with models. In the cases in which β varies substantially across models, generalization beyond the simple scaling (1.1) [6] should be considered in order to account for the x dependence of β. In this case, the heavy-tailed distribution of city sizes leads many models to be dominated either by the large amount of small cities or by the few cities containing most of the population. This reasoning explains why cut-offs in minimum city size and aggregation of cities (different city borders) [9,16,17] influence the estimated β. All these procedures have a strong influence on the small cities, which are the dominant ones in the least-square fitting (e.g. aggregation of cities into metropolitan areas reduces the number of small cities). While applying cut-offs for small cities increases the visual agreement between the data and the fit in the log–log plot, this is only justified if the scaling (1.1) is interpreted as being valid only for large cities. The latter interpretation limits the relevance of the scaling because it becomes limited to a small fraction of the total cities.
6.5. Is the scaling nonlinear?
New answers to this central question emerge from the results of our paper (summarized in table 1). In three of the 15 cases, we found models which are reasonably compatible with the data, and we can base our conclusions on these models, i.e. on the obtained β and on the model comparison with the case β=1 (arrows in table 1). This leads to the conclusion that the UK-income and UK-train stations show linear and OECD-GDP shows super-linear scaling. In the remaining 12 cases, conclusions are based solely on model comparison, and we feel more confident to give an answer to this question only when the same conclusion is obtained for models with different fluctuations (i.e. we compare the conclusions obtained in the best model with lognormal and Gaussian fluctuations). We find such an agreement in eight of the 12 cases, so that the scalings are: UK-patents and OECD-patents are linear; USA-GDP, EU-museum usage and Brazil-GDP are super-linear; USA-roads, EU-libraries and Brazil-AIDS are sublinear. For the remaining four cases, our analysis is inconclusive on the question of linear or nonlinear scaling. Two reasons can lead to this conclusion. The first is that the nonlinear scaling qualitatively changes from β<1 to β>1 depending on the assumptions of the fluctuations (e.g. EU N. theatres). The second reason is that in one of the best models there is no sufficient statistical evidence for β≠1 (marked by an open circle in table 1, EU-cinema capacity, EU-cinema usage and Brazil-external). One interesting case falling in this second reason is EU-cinema usage, for which both the lognormal with fixed δ and the best model (Gaussian with free δ) yield β>1. We still consider this case inconclusive, because the best model, despite showing β=1.13±0.11, only marginally improves (0<BIC<6) upon the model with β=1. In this case, additional data are required in order to increase the statistical evidence in favour of either situation. The possibility of reaching an inconclusive answer shows the advantage of the statistical framework proposed here. In summary, in 15 datasets, we found four linear, four super-linear and three sublinear scalings.
7. Conclusion
In summary, we investigated the existence of non-trivial β≠1 scalings in city datasets. We introduced five different models, showed how to compare them and how to estimate β, and finally tested our methods and models in 15 different datasets. We found that in most cases models are rejected by the data and conclusions can be based only on the comparison between the descriptive power of the different models considered here. Moreover, we found that models which differ only in their assumptions on the fluctuations can lead to different estimations of the scaling exponent β. In extreme cases, even the conclusion on whether a city index scales linearly β=1 or nonlinearly β≠1 with city population depends on assumptions on the fluctuations. A further factor contributing to the large variability of β is the broad city-size distribution that makes models to be dominated either by small or by large cities. In particular, these results show that the usual approach based on least-square fitting is not sufficient to conclude on the existence of nonlinear scaling.
Recent works focused on developing generative models of urban formation that explain nonlinear scalings [10–14]. Our finding that most models are rejected by the data confirms the need for such improved models. The significance of our results on models with different fluctuations is that they show that the estimation of β and the development of generative models cannot be done as separate steps. Instead, it is essential to consider the predicted fluctuations not only in the validation of the model, but also in the estimation of β. Finally, the methods and models used in our paper can be applied to investigate scaling laws beyond cities [20,23].
Data accessibility
Datasets and code used in this paper are available at http://dx.doi.org/10.5281/zenodo.49367.
Authors' contributions
All the authors were involved in the design of the project, data analysis and the preparation of the manuscript.
Competing interests
The authors declare that they have no competing interests.
J.C.L. acknowledges support from the Portuguese Foundation for Science and Technology (FCT), scholarship SFRH/BD/90050/2012.
We thank E. Arcaute for kindly sharing the UK databases and D. Rybski and L. Bettencourt for helpful discussions.
Appendix A. Databases
We used 15 datasets from five different databases. In each database (UK, USA, EU, OECD and Brazil), the same cities xi were used, and the different datasets are different indexes y. Some of our models cannot consider yi≤0. In order to allow for a comparison across all models, we ignored yi≤0 in all cases, and below we report the number N of cases yi>0 in each dataset.
— UK: this database corresponds to fig. 5b of [17], was provided by the authors of that paper, includes the aggregation of population in cities proposed in that paper, and corresponds to the period 2000–2011.
— Income: N=100, total income (weekly).
— Train stations: N=97, number of train stations.
— Patents: N=93, number of patents filed in the period.
— USA: this database corresponds to metropolitan areas of the USA (GDP) and urban areas (roads) in 2013. It was constructed from three different sources: the population was provided by US Census Bureau [29]; the GDP was provided by the US Bureau of Economics Analysis of the Department of Commerce [30]; and the miles of roads was provided by the US Federal Administration of Highways of the Department of Transportation (table HM-71) [31]. Similar data were used in [12].
— GDP: N=381, gross domestic product of metropolitan areas.
— Roads: N=459, length (in miles) of roads of urban areas.
— EU: this database is provided by Eurostat [32]. It contains population and different indexes related to culture in European cities in the year 2011.
— Cinema capacity: N=418, total number of seats of cinemas.
— Cinema usage: N=221, attendance of cinemas in the year.
— Museums usage: N=443, attendance of museums in the year.
— Theatres: N=398, number of theatres.
— Libraries: N=597, number of public libraries.
— OECD: this database contains indexes of cities from the Organization for Economic Co-operation and Development in the years 2000–2012 [33].
— GDP: N=275, gross domestic product in 2010.
— Patents: N=218, number of patents filed in 2008.
— Brazil: this database contains different indexes of all municipalities of Brazil. The data are from the year 2010 and are provided by Brazil's Health Ministry (Brazilian Health Ministry. July 2015; population corresponds to census data).
— GDP: N=5565, gross domestic product.
— AIDS: N=1812, number of deaths by AIDS.
— External: N=5286, number of deaths by external causes.
All the above databases are provided at http://dx.doi.org/10.5281/zenodo.49367.
Appendix B. Taylor's law in lognormal
Here, we express the parameters of the lognormal distribution, and , as a function of the parameters of the scaling laws 4.1 and 4.2α,β,γ and δ. Noting that the expectation and the variance of the lognormal distribution, equation (4.7), are given by B 1and B 2we find a unique solution for and by comparing with equations (4.1) and (4.2): 4.8
Appendix C. Maximization of the likelihood
The maximization of the likelihood is performed by minimizing minus the log-likelihood, using the algorithm 'L-BFGS-B' [34], whose implementation can be found on the Python package SciPy [35], and the details can be found at http://dx.doi.org/10.5281/zenodo.49367. Given that the minimization algorithm can converge to a local minimum, our procedure repeats the optimization 512 times, each with random initial parameters, and selects the lowest. We confirmed that increasing from 256 to 512 samples did not change the computed minima, an indication that the algorithm found the global one.
Appendix D. Computation of the error estimates
The error estimates were computed using bootstrap [36]. The method consists of sampling N pairs (xi,yi) with replacement from the set of N available data points, and repeating the maximization procedure outlined in the previous section for each set. This procedure (sampling+maximization) was repeated 100 times for each combination (model, dataset) and the error estimates were computed as the standard deviation of the distances from the measured parameters to the estimated parameter from the true dataset. We confirmed that the bootstrap error estimates for the lognormal fixed-δ case are within 1% equal to the values of the least-square fit.
Appendix E. Computation of the p-value
The computation of the p-value was done by defining a statistic that tests the hypothesis used in each model; in the case of the lognormal and normal models these are (i) data are independent and (ii) the data are compatible with the model. We used a statistic based on the D'Agostino K2 test [37] (over or y, respectively) that computes the deviations from 0 of the empirical kurtosis and skewness; the test consist of comparing it with the fluctuations expected from a finite-size sample from the (null) model. In detail, we compute two statistics, Zs and Zk for the kurtosis and skewness, respectively. Each of them has a distribution under the null hypothesis, so the sum has a distribution (with 2 degrees of freedom). Because this test does not test independence of the samples, we include in the test statistic the Spearman's rank correlation [38] of the residuals of the fit, ZS (also distributed as a ) because if the residuals are correlated, the data are not independent. The p-value is thus computed by measuring how extreme is in the distribution (with 3 degrees of freedom). The implementation of this is available at http://dx.doi.org/10.5281/zenodo.49367.
In the population model, the calculation of the p-value must be different, because the variance is not being left as a free parameter, so we take a more classical approach. The p-value is computed by measuring how extreme is the difference between the data and its fit with respect to the difference between a sample from the model and its fit. In practice, we use a χ2 statistic to measure the distance between two sets of points {yi}i (data) and {mi}i (the model), . Then, we generate from the model 200 different samples. For each of these samples, we compute the χ2 between the sample values and their fits. Finally, we compute the p-value as the fraction of samples whose χ2 is bigger than the one that belongs to the real data. Note that this statistic is not taking into account independence of the residuals (if we consider the multinomial distribution as the null model, then they should not be independent) or normality in the strict sense, so this test is more permissive than the previous.
Appendix F. Model comparison using Bayesian information criterion
We compare two models m=1,2 by calculating the BIC [39], , where N is the number of data points (observations), is the maximum-likelihood of the model, and km is the number of estimated (free) parameters of the model. In this approach, the model with a lower value for the BIC gives a better description of the data.
We can quantify how much better one model compares with the other by looking at the Bayes factor [40], B12=P(data|m=1)/P(data|m=2), where P(data|m) is the evidence for model m, i.e. the probability of the data given the model. It can be shown [36] that this quantity can be approximated by F 1where ΔBIC≡BIC2−BIC1 is the difference of the respective BICs. Thus, if BIC1<BIC2, it follows that B12>1, i.e. that model 1 provides a better description of the data than model 2. Regarding the decision about nonlinear scaling, i.e. β≠1, we require that ΔBIC≡BICβ=1−BICβ≥6 (see main text), in line with [40], where it is suggested that this implies strong or very strong evidence for a model with β≠1. This corresponds to B12≥e3≈20.08, i.e. it is at least 20 times more likely that the data come from a model with β≠1.
One contribution to a special feature 'City analytics: mathematical modelling and computational analytics for urban behaviour'.
↵1 This framework also allows the use of discrete distributions.
- Received November 30, 2015.
- Accepted June 15, 2016.
- © 2016 The Authors.
Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.


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Having spent more than three decades working in UK advertising, he passed away in Toronto, his home city for the past five years.
He worked almost until the end at The Conversation Farm despite a long battle with ALS.
More commonly known as Lou Gehring's Disease, ALS is a progressive disease that causes muscle weakness, paralysis and, ultimately, respiratory failure.
His career resulted in a string of awards including two Cannes Gold Lions as well as D&AD and Campaign press and poster silvers for work on Nike, Bass, Kimberly-Clark and Prudential.
During that time he also served with Jonathan Mildenhall as joint chairman of the IPA's Ethnic Diversity Group.
Michael Scher, The Conversation Farm's founder, said: "Ray fought hard to keep his sense of humour as well as his sense of purpose and continued to exercise his most exceptional mind right until the end."
After studying for a degree in fine art, Barrett began his agency career at FCB and was appointed creative director of DDB Court Burkitt aged just 26.
He moved to Ogilvy & Mather as head of art before becoming a creative director at WCRS.
In 1996 he linked with two other ex O&M senior staffers to launch Barrett Delves Fletcher Matthews, an agency that subsequently evolved into Barrett Cernis.
Justin Cernis, Barrett's former business partner, said: "Ray was passionate about good work and would never stand for anything mediocre.
"He was always challenging and strategically focused and never shied away from telling clients that what they thought was OK work would mean nothing to consumers. And he had a smile that could light up a room."
Barrett Cernis closed in 2005 after Barrett's decision to quit the agency to take creative command of Manchester's BJL. However, he left after just a year in the job to join the Toronto-based Hey Harry Worldwide.
A memorial evening for Barrett will be held in London later this year.

subset: commoncrawl


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Title Page
The five-hundred and forty-ninth volume of the Archives of Maryland series was originally published in 1982. The Archives of
Maryland edition was published in 2003 by the Maryland State Archives. This volume covers The Charter of Maryland, June 20, 1632.
The Charter of Maryland, June 20, 1632. Maryland State Archives 350th Anniversary Document Series. Annapolis, MD: Maryland
Hall of Records Commission, 1982; Maryland State Archives, 1990.
This information resource of the Maryland State Archives is presented here for fair use in the public domain. When this material is used, in whole or in part, proper citation and credit must be attributed to the Maryland State Archives. PLEASE NOTE: Rights assessment for associated source material is the responsibility of the user.
An Archives of Maryland electronic publication. For information contact


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Planet earth with mirrors in space
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Keywords: 3 dimensional, 3d, artwork, digitally generated, earth, environment, environmental, environmental issues, illustration, mirrors, no one, no-one, nobody, planet, science, scientific, space, technological, technology, three dimensional
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One of the oldest and influential producers and djs of the Tenerife island (Canarias, Spain). His tracks are supported by very important people such as Roger Sanchez, Bob Sinclar, Peter Gelderblom, DONS, Crazibiza and many more great international artists. Signed to the best labels in the world such as Stealth Recordings, Hotfingers, King Street, Pacha or Bedroom Muzik among others. He has remixed artists such as Barbara Tucker, Tuccillo, Supernova or Tony Thomas and his tracks have been remixed by great artist like Crazibiza, Fabian Argomedo, Lauer & Canard or Hector Couto.
Creator and founder of SUMA RECORDS and WHOBEAR RECORDS along with his sidekick Luis Pitti. He has been a resident for many years of the best discos on the Tenerife island such as Barock, Dreams, Cirkus, Camel Bar and also of the best Canarian parties such as BOAT FUN PARTY or the Carnival of Santa Cruz de Tenerife afterhours.
He has also shared stage with many of the best DJ's and producers around the world such as STEVEN LEE (Lee Cabrera), SILICONE SOUL, DJ OLIVER, ISMAEL RIVAS, REBEKA BROWN, RENÉ AMESZ, KURD MAVERICK, IAN CAREY, NOIR , FUNKAGENDA, OSCAR CANO, MARK KNIGHT, WALLY LOPEZ, MATINÉE GROUP, SARAH MAIN, JESSE GARCIA, NALAYA BROWN, SUPERMARTXÉ IBIZA, etc…
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"Normally I'd say no to nudity," 42-year-old Damon tells Playboy in its January/February issue, on newsstands Dec. 18.
"I mean, it's tastefully done. Steven Soderbergh directed it and Michael Douglas plays Liberace. But this movie's not going to be for everyone."
In the film, due for release next year, Damon plays Liberace's much-younger lover Scott Thorson – who exposed his life with Lee in a tell-all book and also sued for $113 million in palimony.
The film is so risque that the Oscar winner even shares a romantic, on-screen kiss with Michael Douglas.
"Michael was a wonderful kisser," jokes Damon.
"For one scene, I had to come out of the pool, go over to Michael Douglas, straddle him on a chaise lounge and start kissing him. It's not like I kiss him just once. We drew it up like a football plan."
Damon — who is also co-writing and starring in Gus Van Sant's Promised Land, in theaters January 4 — opened up to the magazine about his private life, including why he is "sh*tty" at spicing up his relationship with wife, Luciana.
On the roles he most regrets having to pass up: "Having to say no to Avatar was tough because I particularly wanted to work with James Cameron … I remember thinking, Oh God, not only do I have to say no because of scheduling, but he's going to make a star out of some guy who's going to start taking jobs from me later … Milk was another hard one because I was excited it would have been for Gus Van Sant, and I would have had the chance to do scenes with Sean Penn … But when I saw Milk, Josh Brolin was so f*cking good."
On his image in the media: "The narrative about me kind of goes, 'He's a boring married guy,' which is great, because I don't get any of that other stuff like Brad Pitt and George Clooney do … People think I'm kind of vanilla and they leave me alone to work, have an actual private life and be a husband and dad."
On beginning to look his age (42 years old): "I remember having dinner with Tommy Lee Jones 20 years ago. I was looking at his face and thinking, Sh*t, I can't wait to have lines like that. A guy like that can just sit there and be so expressive. I mean he's also one of the best actors ever, but I remember being in a hurry to get some of those lines. I'm getting more lines every day."
On the Bourne legacy coming to an end: "You know what? They might have taken the Bourne series out back and shot it in the head. If that's the end of it, that's just the end of it. I hope not. I love the character and the three movies we did, so I'd love to figure out a way to do another one.
On learning new things that he's frightened of on set: "I learned that I am afraid of heights. When I was doing Syriana, they arranged for my wife, Lucy, and me to go up to watch the sunset over the Arabian Peninsula from the top of that seven-star Dubai hotel that's shaped like a sail, the Burj Al Arab. So we go to the very top–-60 stories or something—we're given champagne, and we go, 'Oh! My God, this is great." But as I started to walk toward the edge, my legs locked up. I was absolutely frozen. [Lucy] thought it was hysterical."
On his workout routine: "I don't have much time between work, parenting and other pursuits I'm involved in, but I lift weights. If somebody's around to box with, I can hit the focus pads, but I haven't done that in a while. I used to jog a lot, but now I'll do maybe one long run a week, or I'll do sprints."
On being a man in an otherwise all-girl family:"Lucy and the girls can definitely bring me to my knees. They just know. My wife gives me sh*t because it's harder for me to discipline my girls, probably because they're girls. With boys, I could relate more and it would probably be easier. Growing up, girls are so mysterious to us. Even as a grown man, they remain mysterious."
On growing up with a hot temper: "I would throw blazing temper tantrums as a kid—my parents worried about me. I remember being so angry that I saw red, and no amount of encouragement, love or hugging would get me out of that … there's a great thing that comes along with being stubborn and obsessive, which is a passion for life. Things matter to you."
On spicing up his marriage with romantic gestures: "I'm sh*tty at that. I wish I were better because my wife deserves somebody who surprises her with a gift or flowers or some wonderful idea. I've never been good at that, and she's really good at it, which makes me feel even more like sh*t."
On his legacy: "When I turned 40 I had my whole family and some friends together. I realized I had to make a toast and hadn't thought of anything to say. I stood up, and what came to me seemed incredibly true in the moment and even more so as time has gone by. I said, 'I think I might actually be the luckiest guy on earth. I really might be him.'"
As Liberace's larger-than-life story is set to hit the big screen in 2013, Scott Thorson, a former lover of the flamboyant entertainer, gives insight into the fascinating world of the late performer in this video, below.
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- Instant Download: No Waiting!
Released: 04/20/2013 - Debut Mixtape From: kyleFLowen // kyleFLowen is an artist born & raised in St. Petersburg, FL. Real name, Kyle Owen, adapted the initials FL following his beloved home state. Dedicated to the truth, kyleFLowen, keeps this a common trend through his music; revealing his soul, flaws, and ambitions in the process. With his eyes set on the goals of legends, kyleFLowen humbly provides you his inner most thoughts in an attempt to help listeners relate. Through the journey of life as we strive to find ourselves, kyleFLowen does the same. Join him on the flight of TRUTH and LIFE, as we may come to find out…the GIFTS of life are often seen when LIFTED. // Recorded & Mixed at RL Studio Tampa, Fl by James Wood // Label/Entertainment Group: World Dynasty // Production: Assorted // Written by: Kyle Owen // Featuring Vocals by: Luxury // Cover Art by: Kyle Owen // Song Cover Art by: @bo_zart (Bo Vu)
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- TillyBilly
- 5 years ago
- Wedding: October 2012
Why hello there lovely ladies. I'm pretty new within this community but could use a bit of objective advice.
My husband and I have been married for just over 12 months – we have been together 7 years. I am 25 and he is 32 (33 next month) We own our own home and we both work full time in steady jobs. We have a bit in savings and no personal debt other than the mortgage. DH and I had decided to TTC at the end of next year as he is concerned about being a new father in his late 30's. I know it isn't that old, but that is easy for me to say while I am still in my 20's. We plan to have 2 – 3 children.
We had a plan……… And like most plans it has been derailed…… I was up for a HUGE promotion at work (which had been promised to me by the GM, which I thought was good enough) which was supposed to come through this month. Needless to say management decided to go a different way. I was furious. Ready to walk out there and then kind of furious. I took some time and decided that I feel as though I am done with the company. So I started looking around and thought of this a fresh start. I will be able to get out of the coorporate management and get back to project management.
All things are good but then I realized that I am going to have to push back TTC as I will need to be employed for a minium of 12 months to qualify for maternity leave….. Then another thought… I will need to be in a job for 2 – 3 years to get to know it inside and out and feel comfortable leaving for 12 months before heading back in.
Things have calmed down at work and I am able to go to work each day without hating myself. I don't want to work there forever but I do have alot of flexibility plus I have 6 months personal sick leave (pretty good for working while pregnant).
Before I get to the options I have I just want to quickly go over the Australian maternity leave as I know most of you are in the US.
We are elegible for 12 months unpaid leave in which your employer is required to have a position available for you when you return. At the moment our government will pay us for18 weeks of that at our minium wage (which I think is about $570 a week). We recently have elected a new PM who has put a new scheme forward. This goes into effect for all children born after July 2015 and will allow for 6 months leave at full pay (they will match your salary up to $150,000 per year).
So the options….
1) I can find a new job and put of TTC until I am able to in the new position which would be 2 years minimum. This would allow me to take advantage of the 6 months paid leave but make my husband at least 35 before his first child. I also will miss out on the sick leave associated with my current job.
2) We can start TTC now and I just stick it out at work until the baby is born, take the lesser paid leave option and head back after 12 months and look for a new job then. The down side with this option is that I miss out on alot of money from the maternity leave as I earn well above minium wage, the mortgage isn't exactly where we wanted it and for a purely selfish reason, I have a huge backpacking trip through South America planned with my best friend
I'm not really sure on what decision to make. I don't think that there is a right or a wrong one. I'm just hoping for some impartial views.
Thanks in advance


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Panasonic Lumix DMC-G7 Mirrorless Micro Four Thirds Digital Camera - Black
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Product Description
Offering true recording versatility, the black Panasonic Lumix DMC-G7 is a true hybrid mirrorless camera that blends 4K UHD video with advanced still capture and burst shooting capabilities. Revolving around a 16 MP Live MOS Micro Four Thirds sensor and Venus Engine 9 image processor, the G7 features notable low-light sensitivity to ISO 25600, continuous shooting to 8 fps with single-shot AF, as well as a trio of 30 fps shooting rates based on the 4K UHD video recording. Complementing the fast burst shooting modes is an equally adept DFD autofocus system, which works to quicken focusing speeds and emphasize accurate subject tracking for consistently sharp imagery in fast-paced and trying working conditions. Rounding out the feature-set of the G7 is a contemporary body design accentuated by physical exposure control dials, six customizable function buttons, both a high-resolution EVF and 3.0" tilting touchscreen LCD, and built-in Wi-Fi for wireless sharing and remote camera control. Bridging the gap between stills and video, the Lumix DMC-G7 pairs these two high-resolution mediums with refined focusing and shooting controls for the utmost in shooting versatility.
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If you are not satisfied with your order and wish to initiate a return, please contact us via firstname.lastname@example.com. All returns must be accompanied with an RA number assigned by a Sixth Avenue Electronics Express representative. Products must be in Brand New condition, including the manufacturer's box with all included accessories. We will not accept any returns without the recorded serial numbers and upc numbers intact on the box. Returned items will be subject to a restocking fee of 5%-20% of the total price. Shipping price is not refundable. The buyer is responsible for shipping charges to return an item back to us. If an item arrives Damaged, please contact us immediately. We will file a claim with the shipper, and ship a replacement as soon as the claim is approved (between 1-6 business days). We hope items never arrived damaged, but unfortunately it does infrequently occur. We thank you in advance for working with us. Certain products are not eligible to be returned at all. Those include hygienic and personal care products (e.g shavers), a/v equipment, and special order products.
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DALLAS — For the past two falls, there was relative quiet on Saturdays at Southern Methodist University.
''You didn't miss it so much in September, when it was still warm,'' said Steve Wilensky, who oversees the Mustang Club, a booster group. ''But there's something when the air gets crisp on a Saturday afternoon and the angle of the sun is just right. You know you should be watching football.


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Demo • Blog • Join • Get @toobbox
Formed in 1980, Depeche Mode is an English electronic band from Essex.
Original members were: Dave Gahan (lead vocals), Martin Gore (keyboards, guitar, vocals, songwriter), Andrew Fletcher (keyboards), Vince Clarke (keyboards, songwriter). After the release of their debut album, Speak & Spell, in 1981 Vince Clarke left the group and was replaced by Alan Wilder who then left the group in 1985. Gahan, Gore and Fletcher continued as a trio.
"Q magazine call Depeche Mode "The most popular electronic band the world has ever known"."
|Alternative • Blues • Cartoons • Classical • Classic Rock • Comedy • Country • Documentaries • Electronica • Exercise • Heavy Metal • Hip Hop • Indie Rock • Music Festivals • Punk Rock • Reggae • Skateboarding • World


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Hurricane Fly and Jezki renew rivalry with the three mile trip the big unknown
The Ladbrokes World Series Hurdle at Punchestown (5.30pm) features an intriguing clash between two former Champion Hurdle runners, Hurricane Fly and Jezki, who are both trying 3m for the first time.
The pair finished third and fourth respectively in last month's renewal of the Champion Hurdle and Jezki has since come out and won over 2m4f at Aintree, where he perhaps benefited from the last hurdle fall of Arctic Fire. A half-brother to last year's winner of this race Jetson, he promises to stay this far. The same can be said for Hurricane Fly and, indeed, the 11-year-old could be even better suited by staying contests at this stage of his career.
However, they've both got to prove their stamina on the track and the current odds of 9/4 and 3/1 are simply not big enough to compensate for any doubts on that score. On the the hand, 7/1 on proven stayer DEDIGOUT looks a steal. Tony Martin's rejuvenated hurdler has been in fine form this season, winning three Grade 2 races, and was unsuited by the quick ground when pulled up in the World Hurdle at Cheltenham. With rain having hit the track this week, he will find conditions far more to his liking and could well outstay the leading pair. Bryan Cooper, who is 3-3 on him this season, is back on board and he has the tactical nous to ensure this is a proper stamina test.
The brilliant Arkle winner Un De Sceaux has scared away most of the opposition on the Ryanair Novice Chase (6.40pm) and this should amount to no more than an exercise canter, with only Smashing likely to put up any resistance. Unfortunately, he won't be making us rich at odds of 1/6.
One who could win us a few quid at the current 5/1 is Richard Fahey's LOOM OF LIFE, who contests the 1m1f handicap (3.55pm) at the seaside track. The three-year-old showed little in three runs at two and started this season on a lowly handicap mark of 64 as a result, serving notice he can win off it when finishing third on his reappearance at Newcastle. Gelded since last season and still green on his return, there is almost certainly better to come from him and his stable has a great record in this race, with three winners and a second from four runners.
He has David O'Meara's Thankstomonty to beat after that horse ran second behind a well-backed favourite on his seasonal debut at Beverley. He has a race in him off his current mark on that evidence, but will need to learn how to settle better before reaching his full potential.
Redcar 3.55, Loom Of Life @ 9/2 Paddy Power
Punchestown 5.30, Dedigout @ 7/1 Paddy Power
All odds were correct at time of posting.


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In Defense Of The Wii & Kinect
Let it be known right from the start that I:
Back in the day, the biggest Nintendo purchases I owned were an N64 (and that was GIVEN to me and I barely used it) and every game boy and to be honest, the game boys were solely for Pokemon. However, after playing my 360 for so long, last July I decided to finally pick up a Nintendo DS Lite. I wanted to play the new Pokemon for it and a few other games, like Scribblenauts for example. But the main reason I did this was because I didn't want to play my Xbox that much anymore. I get very very stressed when playing video games on the xbox and honestly, gaming is supposed to be a FUN hobby, not a stressful job. And I like the Nintendo philosophy of "it ain't broken, we ain't fixin it". So I picked up the DS and the first thing I noticed was how much calmer everything was with it, and how much more fun I was having overall?
But why is this?
I think it's because the games for both the Wii and the DS harken back to the games of my youth. Old side scrollers, light hearted adventure titles and some puzzles games. So when I got my Wii, I bought the red one because it came with the new Mario game. I've never been much of a fan of Mario (I was a Sonic kid growing up), but I LOVED it. It's a phenomenal game. After playing it, and now looking back at what the Wii is, it dawned on me that it's much more than a gaming console. Now armed with Hulu Plus, Netflix streaming and a Youtube app (all of which work WAY better than on the 360), and their little online store with wiiware original games and some of the old titles-including old genesis titles like Vectorman-the Wii accomplished what both the 360 and the ps3 tried to do. Being an entertainment system. My Wii gets far more playtime and use than my 360 and DS COMBINED. Why is this? Because I use Netflix streaming every single day. It's my television now, and I soon found myself purchasing new games for it too. I bought Animal Crossing City Folk because the 1st on the Gamecube is one of my all time favorite titles, and more recently Epic Mickey and Kirbys Epic Yarn and Super Paper Mario. These have all been far superior than the majority of the triple AAA titles I've purchased on the xbox in it's entire run, give or take a few like all the Bioshocks, or more recently Tomb Raider. I think it's because it's playing games for playing games sake. The one thing the Wii didn't buy into that the 360 started and the ps3 tacked on was the Trophy/Achievement system. Meaning, I can actually sit down with my Wii and have FUN. I don't have to worry about scores or anything else. It's all about just enjoying the game for what the game is. A work of art.
Not to mention the Wii is the most economically feasible console because their games are pretty cheap compared to the alternatives, especially if you shop on Amazon. I wanted Epic Mickey, but Gamestop had it used for 20 dollars, and it's been out a few years. I bought it on Amazon for 8 bucks. Take what you want from that. And now owning a Kinect for a few days, I have come to a singular conclusion…
These things, motion gaming (or really, in the Wii's sake, a console nobody took seriously) are great if you can find the gems among the trash. The Wii isn't without its flaws for sure. First off, there's WAY too many peripheries. The wiimote, the nunchuck, the classic controller, an N64 controller for the N64 games you buy and a Gamecube controller if you want to play those, and then throw in things like the Mario Kart wheel (which you don't HAVE to have), the Tony Hawk Board, the Rock Band instruments, the Wii Fit Balance Board and for one of the Zelda games, you HAVE to have Wii Motion Plus to play it which to me is a deterrent to buying the game entirely. But overall, it's fantastic and I wish more people could see these things with a more open mind instead of just dismissing them right out the gate. "Oh motion gaming is stupid" or "Oh the Wii has too much garbage on it" or "Oh Kinect is for little kids and single moms who want to get in shape!" but no, if you find the right titles like some of the Wii ones I mentioned or for the Kinect-the Gunstringer, Haunt, Fruit Ninja-they're absolutely worth your time.
So I guess that's all I have to say about that.


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This page is a printable version of: http://www.cityandhackneyccg.nhs.uk/services/transforming-care.htm
Date: 21 October 2018
Transforming Care aims to improve life for people with learning disabilities and people with autism who have challenging behaviour. It comes in the aftermath of the Winterbourne View enquiry that found these most vulnerable people often living in institutional and poor quality, unsafe services, far from family and friends. Our plan is to give the right support in the right place at the right time with each person.
We've held events to find out what's needed and have spoken to families, support providers, and a wide range of staff as well as experts from outside our area to help form our plan. Thanks to all of them for their time, frankness, and for their ideas. Our plan will help us to:
If you would like to be involved in the Transforming Care work, please contact email@example.com
The CCG local contacts for Transforming Care are email@example.com
The City of London contact for Transforming care is email@example.com
The London Borough of Hackney contact for Transforming Care is firstname.lastname@example.com


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Why is this so? The man has created and sold off (at a considerable profit) several major ad agencies. He is back in the ad business today and even though constrained from using his last name in business, is apparently doing very well indeed. He has an attractive, loving and talented (for 20 years a staple on New York television) wife, Judy Licht, and several appealing moppets. The Della Feminas live well, having a number of homes including a spectacular pleasure dome sited atop the dunes of East Hampton overlooking the ocean, and cheek by jowl with such neighbors as Mort Zuckerman, Calvin Klein and two or three generations of Fords from Detroit (they make cars and own the Detroit Lions).
To give you an idea of the scope and style of Jerry's life in East Hampton the last time I attended a party at his house, Barbra Streisand was hanging out in the kitchen.
So why is Jerry kvetching?
But he is. And in a recent newspaper column for The Independent, a local East Hampton newspaper in which he owns a piece of the action, Jerry wrote, "I have made up my mind that as soon as I can, I will sell off my share of these three properties, and I will never invest another penny of my money in East Hampton again."
The three properties are his classic Della Femina restaurant on North Main Street; the Jerry & David's Red Horse Market on Montauk Highway, a gourmet food emporium; and East Hampton Point, a vast operation on Three Mile Harbor comprising a big waterfront restaurant, a marina, a boatyard, and adjacent condos.
What angered Jerry began three years ago when he first tangled with village authorities. It was autumn and outside the Red Horse Market he had erected a small display of pumpkins. Innocent enough? So one might think. But not if one were the government of East Hampton Village, which informed Jerry this display was against the rules regulating outdoor advertising. They went back and forth on it, arguing the rules, arguing freedom of speech, until the village, in what I think can be characterized as pecksniffery (an unctuous and pompous manifestation of high moral superiority), threatened to arrest Jerry.
Until then, the whole thing was in reasonably good spirit. So much so, in fact, that when the arrest was scheduled Jerry showed up only to find the cops weren't prepared and he cheerfully agreed to come out from the city (about 100 miles) on another, more convenient day, to be cuffed and booked.
Lawsuits followed. More lawsuits. Legal bills started to pile up. Good cheer turned bitter. Jerry was not only suing the village, he was running for office on a "Throw the rascals out!" plank. He lost, spending 40 grand or so and getting the fewest votes. The Maidstone Club, he thundered, was ganging up on him. But the courts kept knocking down the village's case and sustaining Jerry. And he kept demanding his court costs be covered.
Until, last month, an out-of-court settlement was reached. Wrote Jerry of this defining moment, "The check from the village covering my settlement will arrive any day now. I am forbidden to discuss the amount that the village gave me when they threw in the towel on the 'Pumpkin Case.' All I'm allowed to say by law is that I'm 'happy' with the settlement. But am I happy? Even though I won, I feel strangely sad."
He isn't the only one. The other newspaper in town, The East Hampton Star, wrote a letter demanding to know more. Why a gag order? Why shouldn't village taxpayers (of whom I am one) know how much this damned case cost all of us? Was Jerry really selling off his share of properties because he was angry with the pettifoggery of village rules? Or was business off in a largely seasonal resort and was this the opportune moment to bail out?
In the village, you hear the debate. Some are all for Jerry; others support the village. Some feel strongly both sides were stubborn and silly. Did Old Money East Hampton gang up on New Money Jerry? Did the Maidstone Club establishment delight in his problems, happy to see an "outsider" put in his place? As an "outsider" myself whose house sits within view of the Maidstone, I am fascinated.
But isn't it precisely the crotchedy ways of antique villages like this one (founded in 1640) a part of the reason why "new people" like Jerry and me and so


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You may be aware of recent reports about recalls involving air bags supplied by Takata. Ten different car companies, including Subaru, are affected. The passenger front air bag modules in Subaru vehicles sold in South Africa are a part of the recall.
|2011 model year
|Passenger front air bag module
|2004 - 2011 model year
|Passenger front air bag module
|2004 - 2014 model year
|Passenger front air bag module
|2010 - 2013 model year
|Passenger front air bag module
|2009 – 2012 model year
|Passenger front air bag module
|Legacy & Outback
|2005 – 2014 model year
|Passenger front air bag module
|2005-2013 model year
|Passenger front air bag module
To determine if your specific vehicle is affected by this or any other recall, insert your 17 digit vehicle identification number (VIN) in the space provided to the right of this section. The VIN can be found on the vehicle itself on the windscreen, licence disc or vehicle registration documents.
If your vehicle is one of those recalled, you will be directed to a form asking for your contact information. Once we are ready to schedule your visit to your closest dealership, we will contact you and do so.
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Senior Lawyer (10+ PQE)
- Recruiter
- Hays Legal
- Location
- England, London
- Salary
- Negotiable
- Posted
- 09 Mar 2018
- Closes
- 06 Apr 2018
- Ref
- 3246670
- Contact
- Iweta Kalinowska
- Practice area
- Intellectual property, Media and entertainment, Pension law
- Sector
- In-house, Public sector/Charities
- Hours
- Full time
- Contract type
- Permanent
An unparalleled opportunity for Senior Solicitors to join the Government Legal Department in London.
Your new company
The Government Legal Department is the single largest provider of legal services to the government. Its expert legal teams provide advisory services to nearly all of the main Whitehall departments. With more than 1800 qualified lawyers, working within 15 client-facing advisory divisions embedded with their client departments, it is one of the largest legal businesses in the country.
Your new role
A unique opportunity has arisen for Senior Lawyers to join the legal team on advising and developing legislation on unprecedented scale and complexity, and supporting ordinary business of government. A number of roles are available on fixed-term appointments, up to 2 years, as well as on a temporary basis. Shorter assignments as well as part-time and flexible working is available.
What you'll need to succeed
This exceptional opportunity to join one of the largest legal businesses in the country, at a truly ground-breaking time, is open to Senior Lawyers (Solicitors, Barristers and CILEX Officers) with relevant specialist expertise in at least one of the following fields:
- Legislative drafting, particularly secondary legislation
- Commercial contracts, particularly public procurement
- Rail franchising
- Financial services law
- International trade law
- Agricultural, fisheries or food law
- Environmental law
- Local government law, particularly housing and local government finance
- Pensions - private pensions and/or public sector schemes
- Media law
- Telecoms and broadband
The ideal candidate will have over 10 years' PQE, with particular expertise covering one of the areas listed above. Other experience levels will also be considered if demonstrating expertise in a given sector of law. You should be passionate about contributing to public service, particularly during this time of great change and opportunity.
What you'll get in return
This is an unparalleled opportunity to contribute to shaping the country during a time of extreme flux with Brexit fast approaching. You can get involved in exciting work of unprecedented complexity and scale, as well as contributing to public service. This is a remarkable chance to undertake career defining work, which can be an exciting highlight to your career as Partner or Head of Legal. The Government Legal Department offers flexible working and working from home, where appropriate, as well as good work-life balance.
What you need to do now
If you're interested in this role, click 'apply now' to forward an up-to-date copy of your CV, or call us now.
If this job isn't quite right for you but you are looking for a new position, please contact us for a confidential discussion on your career.
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You are here: Home Page >
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Hydropower Generation
Statkraft entered the Indian power market through its subsidiary SN Power in 2004 when it acquired 49 per cent of the shares in Malana Power Company Limited (MPCL).
Statkraft is currently involved in two significant hydropower plants in India- Malana and Allain Duhangan, and was the first foreign investor to enter the hydropower section in the country.
Statkraft has an exclusive partnership agreement with Tata Power to jointly develop hydropower projects in the region. Together the two companies hold the license for the 380 MW /1550 GWh Dugar greenfield project in the state of Himachal Pradesh. The feasibility of the project is currently being investigated and potential construction is planned to start in 2016.


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"I came here pretty much scared shitless, and it all came together." So said pro freerider Kurt Sorge following Sunday afternoon's championship run in Red Bull's Rampage, which is easily the craziest mountain biking contest in the world (we summed it up pretty well here). Sitting atop his bike near Virgin, UT, Sorge looked out over the sun-baked desert and continued: "The run was gnarly...It was actually fun, one of the funnest lines I've ever ridden in my life. I'm just stoked to get it in the bag." We're impressed that he not only survived two runs down the near-vertical red rock course, but that he also had the presence of mind to perform tricks. OK, that's kind of the point of the contest, but still. From a no-handed drop to a superman jump to a backflip step-down, Sorge had all the elements of a winning run in the final. Check out the video here:
Not everyone was as lucky as Sorge, though. 2010 runner-up Gee Atherton withdrew following a crash during Saturday's practice session, Casey Groves crashed out during a Sunday morning warm-up, and Brendan Fairclough injured himself during Friday's qualifier. The biggest scare came when defending champ Cam Zink bailed from his bike midway across the massive jump at Canyon Gap. He landed hard on the other side (without his bike), but was able to limp away after five minutes on the ground (check it out here, but be warned it's a bit graphic). He was back to watch the contest Sunday, on crutches. Overall, though, carnage was minimal compared to years past, and that's a good thing for the riders as this contest continues to grow bigger and bigger.
Click through to the slideshow to see some of the action. If you have three hours free (lucky you!), you can relive the excitement here, or wait to watch it on NBC come December 8.


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Green Hops: Energy Law & Plan, Big 5 Subsidized, Installed Wind Doubles
Today's Green Hops, focusing on energy supply, is a continuation of yesterday's.
Two important macro-policy documents are in the works. CELB reports that the comprehensive Energy Law may be passed in 2010 (though this Chinese clipping suggests it may be as early as this year), and that the 12th Five-Year Plan for Energy (2011-2015) is in draft mode. Nuclear, wind and hydro seem to bet the alternative energy sources of choice. This alternative energy review by China Daily, in its "Mixed Energy Forecast" seems to similarly suggest the short shrift given to solar. How unimaginative. I'm sure the solar industry would have something to say about that. In fact, it has (see solar section below).
Before turning to the knitty-gritty of the green and brown energy news developments over the past weeks, I would like to highlight a sage piece of advice from CELB, that recognizes that China is still in many ways, but especially economic development, very much a "Rule by Plan" rather than "Rule of Law" society:
A word of advice from CELB to US Climate and Energy negotiators with China: It is absolutely essential that you begin negotiations by requesting that what ever is agreed to will become a part of and be supported by this new Five-Year Energy plan.
Without further ado, let's get started with the energy roundup!
Fuels from Hell
The State Council is poised to approve a national plan to raise nuclear's share of power of production to 5% by 2020. This translates to some 60 to 70 GW. Currently, there are 11 nuclear reactors in operation with a combined capacity of about 9 GW, accounting for just over 1% of China's power.
King KONG (koal, oil and natural gas)
Plans for a strategic coal reserve by 2015 are being drafted by the State Council. In the meantime, Zhejiang and Shandong, the two major coal consuming provinces, have already started building pilot coal reserve bases.
It looks like more brown sectors will benefit from the stimulus package. We previously reported that steel and automobiles hit the jackpot, and soon perhaps, oil refining and petrochemicals as well. These "brown boosters" are hard to square with earlier statements by officials saying industries in 两高 sectors (i.e. high energy consumption and high pollution) will not benefit from the pump priming.
Some numbers on 2008–China's natural gas output was up 12.3% while LNG imports climbed 15% (this is good news for a coal-diversification strategy, though we'll have to see how the financial slowdown of 2009 affects this year's numbers); crude oil output increased by 2.3%. Coal, oil and electricity demand is poised to ebb in 2009.
China started construction of the eastern segment of the country's second West-East natural gas pipeline in Shenzhen City, Guangdong Province on Saturday. The pipeline, the second after the first West-East natural gas transfer project, will span 9,100 km across 15 regions and carry 30 billion cubic meters of natural gas every year from Turkmenistan and Xinjiang (which boasts China's largest reserves) to Zhejiang, Shanghai, Guangdong and Hong Kong, among others. It will reportedly be the world's longest natural gas pipeline.
Power Infrastructure
In the power sectors, the government continues its 上大压小 policy by pledging to shut down 31 GW of inefficient coal power plants over the next three years. The Big 5 power companies may receive as much as RMB 10 billion in subsidies to tide them over their record 2008 losses, which stemmed primarily from thei inability to cover volatile fuel (mostly coal) costs. The State Grid's profits were cut by 80% in 2008 due to disruption by natural disasters and higher power prices.
China Daily has a good article on China's efforts to build ultra-high voltage power line infrastructure.
Fuels from Heaven
32 bidders have emerged for the 10 MW solar power plant in Dunhuang, Gansu province. Wuxi's Suntech Power was selected to supply 5 MW of solar panels to Masdar City's proposed 10 MW solar farm.
China Daily has an Conference Communicate Center is a 50,000 sqm facilty boasting a solar collecting area of 6,500 sqm that integrates solar thermal hot water supply, heating, cooling, and PV power generation.
A group of major Chinese solar PV manufacturers, including Suntech, Trina, Canadian Solar and LDK have submitted an industry road map (see also original Chinese report) to the Ministry of Science and Technology that will see PV power prices being to RMB 1/kwh by 2015, or as early as 2012. Although RMB1/kwh which although is still roughly twice the current rate of urban retail electricity in China, would be cost-competitive in European markets. To put the RMB 1/kwh price in context, the NDRC announced preferential feed-in tariff rates of RMB 4/kwh for pilot projects in Chongming Island (near Shanghai) and Ordos in Inner Mongolia. A solar industry analyst suggested to GLF that such a submission by the solar industry was a pre-emptive move to give the government assurance that steady cost reductions were being achieved by the industry and to thereby encourage the government to be more forthcoming in provind subsidies and incentives to jumpstart China's domestic PV market.
China's installed wind capacity doubled last year from 5.9 GW to 12.3 GW, but what's installed isn't necessarily what generating power. Chinese wind farms, especially those using domestically produced wind turbines, are underperforming, notes Nature (sub's needed):
[O]n-shore turbines in other leading wind power countries have capacity factors of around 30%. China's is just 23%… Chinese wind farms using foreign models have a 5% higher overall capacity factor than those using domestic turbines. Domestic turbines are especially unproductive when first set up…Because the technology is newer and less tested, Chinese turbines are also more likely to be shut down for maintenance, according to anecdotal evidence.
China's largest wind power R&D facility will be established in Shaanxi province's Xi'an City. State-owned China Northern Locomotive and Rolling Stock Industry (Group) Corporation will provide RMB 6 billion in investments. This seems like an odd initiative for a locomotive company; however, and this may be pure conjecture on GLF's part, a glance at their product offerings suggest that there may be some synergies between locomotive and wind turbine parts. Part of the wind facility also includes the build out of a rail system, which may suggest (again pure speculation) some sort of manufacturing activities at the site. Danish wind company, Vestas, meanwhile, seeks to continue riding the swelling wind currents by investing a further $350 million into its Tianjin turbine manufacturing subsidiary.
Novozymes and its Chinese partner COFCO have entered a new partnership with major Chinese oil and energy company Sinopec to develop a commercial-scale process for producing cellulosic bioethanol from corn stover. China is the second largest producer of corn after the U.S.
China Hydroelectric, a U.S.-based firm was founded in 2006 in Hong Kong to buy and operate hydropower projects in mainland China, has has filed paperwork for a $200 million IPO on the Hong Kong Exchange. China Hydroelectric seeks to capitalize on what it identifies as a "one-time buyer's market" in China's hydropower sector, as local governments throughout the provinces seek to shed their hydropower assets to the private sector. Its two disclosed acquisition deals suggest an emphasis on small hydro–they have planned installed capacities of about 40 MW.
Large hydro, however, is being implicated for negative geological impacts. The bulging weight of 320 million tons of water held at Zipingpu dam may have "hastened the occurrence of" (but probably not solely responsible for) the Sichuan earthquakes of last May, according to this report. A map below, courtesy of AFP, shows Zipingpu's location relative to the epicentre. See also this post at International River's Wet, Wild & Wonky.
Energy Efficiency
The Asia Development Bank will provide three grants totaling $2.8 million to support China's efforts to reduce sulfur dioxide gas emissions, increase energy savings, and strengthen the existing clean development mechanism fund that supports clean energy projects.
U.S. company SmartHeat, recenltly listed on the NASDAQ small cap market and maker of plate-heat exchangers and compact plate-heat exchanger units to help capture and recycle waste heat, entered into a $1.2 million equipment supply contract with Chinese utility Uda Heat Power for 16 plate-heat exchange units to be delivered by the end of this month.
Comments (1)
Jul.26.2011@3:00 pm
I really appreciate great ideas and thoughtful editorial content. When it comes to this industry so much is compiled of fluff and nonsense and I really appreciate how you guys share the real deal and don't lead your readers astray from the facts. Thanks for your great work and I'll continue to be an avid follower for many years to come!


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UNLOCKING THE MYSTERY: A young girl gets split up from her family during the Holocaust and a modern-day journalist sets out to uncover her mysterious fate in the sometimes moving, other times maudlin Sarah's Key.
Be succinct, constructive, and relevant to the story. Leaving a comment means you agree to our Discussion Guidelines. We like civilized discourse. We don't like spam, lying, profanity, harassment or personal attacks.


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Hello, everyone. I was an atheist until at the age of 27 I began to study the Bible in order to debunk it. I learned quickly that the Bible was grossly misrepresented by apostate Christendom's adoption of pagan teachings such as the immortal soul from Socrates, the trinity from Plato, the cross from Constantine, hell from Dante and Milton, Easter from Astarte, Christmas from the winter solstice c…
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Position: Senior Operations Coordinator
Location: Abuja, Nigeria
Job Type: Employment
Girl Hub's mission is to empower the 250 million adolescent girls living in poverty to reach their full potential. Girls are a proven force for change and the catalyst to end global poverty – it's called the Girl Effect.
When you improve a girl's life through education, health, safety and economic opportunity, these changes have a positive ripple effect on their families, communities and nations. Our work aims to shift the social norms that hold girls back, and drive better investments that directly benefit girls. Everything we do is based on what girls tell us they need and what will work for them.
Girl Hub (GH) is a strategic collaboration between DFID and the Nike Foundation. We currently operate in Rwanda, Nigeria and Ethiopia with headquarters in London. More information on Girl Hub Nigeria can be found here: http://www.girleffect.org/the-girl-effect-in-action/girl-hub/nigeria/
Senior Operations Coordinator
Job Purpose:
The Senior Operations Coordinator will be responsible for the day-to-day administration of the Girl Hub Nigeria (GHN) Office.
Main Responsibilities:
Procurement and Logistics
- Lead on the procurement of goods and services for GHN in accordance with procurement policy and best practice value for money.
- Ensure all, particularly high value tenders, go through the appropriate process with adequate timing for a competitive process. This includes obtaining competitive quotations, completing bid analysis for supplier selection, completing business cases as well as establishing relationships with suppliers, and ensuring that all the necessary paperwork is completed adequately, accurately and on a timely basis.
- Ensure Consultant TORs are adequate and that feedback forms are completed on the conclusion of their work before final payment is made.
- Contract Management on Salesforce in line with GH contract management guidelines.
- Maintain the records of all procurement processes for audit trail purposes and be able to provide an update of all outstanding procurement at any one time for commitment & budget management purposes.
- Be responsible for proactively managing the transport needs of all staff in accordance with GHN policy. This is both in terms of vehicles (hired cars) and air travel.
- Provide logistical support to overseas visitors including help with visas, transport and accommodation bookings and travel advice.
- Oversee and manage logistical support for GHN events and workshops with support of Programme and Administrative Assistants.
- Update the Telephone Tree and Emergency Numbers as and when required based on new staff / visitors to the office distributing to the GHN Team.
- Establish strong networks with DFID Risk Management Office and Spearfish Security Company.
- Arrange risk assessments of Marker Hotels/Accommodation for visitors; ensure they meet basic security requirements.
- Provide security briefing for visitors on arrival to Nigeria.
- Ensuring staff comply with Girl Hub's Safeguarding policy.
- Plan risk assessments for new venues and expat staff residences with Spearfish as required.
- Coordinate with the GHN Team and complete the Quarterly Risk Register and Incident Reporting liaising with Senior Ops Manager in London.
- Provide any back stopping support for implementing security and risk mitigation procedures in addition to duty of care.
- Co-ordinate any visits to the focal states. Advance planning with Spearfish. Ensure all plans in place before travel.
- Undertake monthly tests of the Satellite phone.
HR Administration & Office Management
- Ensure the staff absence trackers are maintained and updated on a regular basis.
- Ensure staff medical scheme details is updated as required and renewed on a timely basis.
- Ensure all personnel files are maintained confidentially with appropriate records filed in line with the checklist.
- Facilitate the process of recruitment for in country positions, partnering with external suppliers and GH London team members where relevant.
- Facilitate the onboarding and off boarding of staff and ensure detailed induction plans are prepared for all new team members, liaising with the wider team where relevant.
- Ensure office supplies are procured and managed effectively.
- Ensure office equipment is fully functional. Photocopiers, scanners; flip chart stands and be proactive and efficient in resolving any problems that come to light.
- Oversee and manage GHN IT infrastructure, including management of any Service Level Agreements (SLA), and ensure best practice by all staff on data protection and IT security.
- Maintain the new IT Problem Log ensuring problems are addressed effectively, monitoring repeats and identifying on-going problem areas for solution.
- Upkeep and management of the video conference system.
- Oversee car hire service and driver. Renewing contract when required.
- Ensure the visitors and staff movement tracker is updated as and when staff travel
Asset & Inventory Management
- Manage and maintain the asset / inventory register ensuring all new assets are recorded in the register, provided with an asset code and are physically labeled.
- Use the required formats for documenting the issue of assets to staff and for tracking the temporary movement of all assets in and out of the office.
- Safeguard all of Girl Hub's physical assets ensuring high value items are secured daily and physical verification spot checks are carried out monthly with full asset verification quarterly. Ensure assets are functional and if not take appropriate action to repair the item or record same on the damaged asset format.
- Report any lost / stolen assets and process claims under GHN Insurance within 2 weeks of notification from staff/relevant party and mitigate loss to GHN.
- Review and ensure adequate asset, risk and cash insurance is in place for GHN.
Other/Administrative support
- Manage the Operations Budget with effective forecasting and expenditure
- Adhere to Girl Hub Global and Local policies and procedures.
- Work with the programme leads on contracts and procurement. Negotiating with consultants/service providers to ensure good value for money.
- Process Payment request within 2 days of receipt.
- Work with the Girl Experts as girl Safe guarding officer to ensure 100% compliance of the girl Safe Guarding policy.
- Ensure proper electronic (Dropbox) and paper filing and archiving.
- Other duties as agreed with the Line Manager
- Participate in all strategic planning, quarterly planning, budget reforecasting, training, team building and related activities of the Girl Hub Nigeria team and contribute to the overall development of the organisation.
Line Management
Line Management responsibilities for the Operations Assistant and Administrative Assistant.
Key Skills and Experience
- Degree level education in Administration, Management or other related fields.
- Experience with all aspects of office management and administration including procurement, administrative support, Finance, event/workshop management and IT.
- Computer literate (i.e. WORD, Excel, Outlook, Internet Explorer).
- Excellent personal organisational skills, including priority management and ability to meet deadlines. Self-motivated with excellent team-working skills.
- Good level of spoken and written English. Good communication skills.
- Good planning and time management skills
- Excellent team player
- Commitment to and understanding of Girl Hub's aims, values and principles.
Minimum 5 years' experience working for an international NGO or other organisations with a global remit.
Ability to multitask and work with limited supervision
Method of Application
To apply for this position, click here