The availability and sophistication degree of such services are fair measures of progress for any city. In this context, Geographic information systems " GIS " offers solutions that support the decision - making processes regarding management, planning and distribution of services, ultimately improving the standard of living in cities (Aziz, 2007, p. 11). Investigating services planning standards is one of the most relevant issues concerning human progress regarding its proper definition and needs. Planning standards can be reconsidered by studying the variation in the distribution of geographical phenomena and the characteristi cs of geographic areas. More effort should be exerted in defining these standards parallel to the characteristics of each region. Such efforts will facilitate appropriate allocation s of services and accurate definitions of future developmental efforts. The problem of the study is that the planning standards are not suitable for the characteristics of the Egyptian cities, which include more population and intensive daily use of services. The solution to this problem is to create new planning standards that suit the rapidly changing nature of cities, and to generate these criteria current services and their intensity and the built - up areas are going to be used to reflect the characteristics of the city, taking this abroach is a new way to generate such criteria. This study attempts to derive planning standards for public services in the city of Qena that are compatible with the characteristics of the city, the geographical distribution of the population, the built - up area, and the services therein.

Machine learning is playing an increasing role in hydrology, supplementing or replacing physics-based models. One notable example is the use of recurrent neural networks (RNNs) for forecasting streamflow given observed precipitation and geographic characteristics. Training of such a model over the continental United States has demonstrated that a single set of model parameters can be used across independent catchments, and that RNNs can outperform physics-based models. In this work, we take a next step and study the performance of RNNs for river routing in land surface models (LSMs). Instead of observed precipitation, the LSM-RNN uses instantaneous runoff calculated from physics-based models as an input. We train the model with data from river basins spanning the globe and test it in streamflow hindcasts. The model demonstrates skill at generalization across basins (predicting streamflow in unseen catchments) and across time (predicting streamflow during years not used in training). We compare the predictions from the LSM-RNN to an existing physics-based model calibrated with a similar dataset and find that the LSM-RNN outperforms the physics-based model. Our results give further evidence that RNNs are effective for global streamflow prediction from runoff inputs and motivate the development of complete routing models that can capture nested sub-basis connections.

Spatial joins allow to augment one spatial dataset with information from another spatial dataset by linking overlapping features. In this post I will provide an example showing how to augment a dataset containing school locations with socioeconomic data of their surrounding statistical region using R and the package sf (Pebesma 2018). This approach has the drawback that the surrounding statistical region doesn't reflect the actual catchment area of the school. I will present an alternative approach where the overlaps of the schools' catchment areas with the statistical regions allow to calculate the weighted average of the socioeconomic statistics. If we have no data about the actual catchment areas of the schools, we may resort to approximating these areas as circular regions or as Voronoi regions around schools.

While some particles can be identified by their type, such as electrons [3, 4] and muons [5, 6], most of the detected particles are light hadrons produced in collimated sprays called jets. Jets are the consequence of high energy quarks or gluons fragmenting into colorless hadrons. Experimentally, jets are defined by clustering schemes which group together measured calorimeter energy deposits or reconstructed charged particle tracks. A jet algorithm is a clustering scheme that connects the measured objects with theoretical quantities that can be calculated and simulated. At a hadron collider, the natural coordinates for describing particles arep T, y, and φ, where p T is the magnitude of the momentum transverse to the proton beam,y is the rapidity, andφ is the azimuthal angle. Particles or calorimeter energy deposits are clustered using jet algorithms based on distance metrics on their coordinates in (p T, ρ) (p T,y,φ) . In order for a jet algorithm to be useful to experimentalists and theorists, the collection of jets should be IRC safe in the following sense: 1. Infrared safe (IR): if a particlei is added with p T 0, the jets are unaffected.