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ARREST LOG Wilmington Police Make 4 Arrests Issue 4 Summonses

first_imgWILMINGTON, MA — According to Wilmington Police Logs, Wilmington Police issued the following arrests and summonses between November 22, 2018 and November 28, 2018:Thursday, November 22Max D. Faulkingham (21, Wilmington) was issued a summons for Operating A Motor Vehicle with a Suspended License (Subsequent Offense).Friday, November 23Neil K. Joshi (40, Wilmington) was arrested for OUI Liquor; Leaving Scene Of Property Damage; and Marked Lanes Violation.John Anthony Gallinaro (27, North Billerica) was arrested for Warrants; Operating A Motor Vehicle With A Suspended or Revoked License; License Not In Possession; and Receiving Stolen Property Valued at $1,200+Alexa Marquard (22, Wilmington) was arrested for Receiving Stolen Property Valued at $1,200+.Saturday, November 24NoneSunday, November 25NoneMonday, November 26Melisa Marie Yeo (30, Billerica) was issued a summons for Operating A Motor Vehicle With A Suspended License For OUI and Speeding In Violation Of A Special Regulation Posted.Kameron T. Conway (22, North Billerica) was issued a summons for Operating A Motor Vehicle With A Suspended Or Revoked License and Failure To Wear Seat Belt.Tuesday, November 27NoneWednesday, November 28Justin ER Harvey (21, Lowell) was issued a summons for Operating A Motor Vehicle With A Suspended License (Criminal Subsequent Offense) and Speeding In Violation Of Special Regulation Posted.Reginald O. Babineau (61, Wilmington) was arrested for OUI Liquor (2nd Offense), Negligent Operation Of A Motor Vehicle, Operating A Motor Vehicle With A Suspended License, Possession Of Open Container Of Alcohol In Motor Vehicle; and Crosswalk Violation.(DISCLAIMER: This information is public information.  An arrest does not constitute a conviction.  Any arrested person is innocent until proven guilty.)Like Wilmington Apple on Facebook. Follow Wilmington Apple on Twitter. Follow Wilmington Apple on Instagram. Subscribe to Wilmington Apple’s daily email newsletter HERE. Got a comment, question, photo, press release, or news tip? Email wilmingtonapple@gmail.com.Thank You To Our Sponsor: Share this:TwitterFacebookLike this:Like Loading… RelatedARREST LOG: Wilmington Police Make 4 Arrests & Issue 3 SummonsesIn “Police Log”ARREST LOG: Wilmington Police Make 5 Arrests & Issue 4 SummonsesIn “Police Log”ARREST LOG: Wilmington Police Make 2 Arrests & Issue 2 SummonsesIn “Police Log”,Thank You To Our Sponsor:last_img read more

Pakistan doctor who helped track bin Laden likely to be released

first_imgA roadside vendor sells newspapers with headlines about the death of al-Qaeda leader Osama bin Laden, in Lahore. Reuters file photoPakistani prison authorities have moved the jailed doctor believed to have helped the CIA hunt down Osama bin Laden, his attorney said on Saturday, speculating it could be a prelude to his release.The continued imprisonment of Dr Shakil Afridi has long been a source of tension between Pakistan and the United States, which cut military aid over accusations Pakistan continues to shelter Taliban militants fighting US and Afghan soldiers across the border in Afghanistan.A jail official in the northwestern city of Peshawar told Reuters on condition of anonymity that Afridi had been transferred to Adiala prison in Rawalpindi, near the capital Islamabad, but said the reasons were unclear and could simply be security-related.Afridi’s lawyer, Qamar Nadeem, confirmed the transfer of his client but said he was not sure where he was now. Judicial officials could not be reached on Saturday, nor could embassy officials for the United States, which has for years called on Pakistan to release Afridi.Afridi was accused of treason after word spread he had helped the CIA collect genetic samples of the bin Laden family, paving the way for a US Navy SEAL raid in 2011 in the town of Abbottabad that killed the al Qaeda leader accused of plotting the 11 Sept 2001 attacks on the United States that killed nearly 3,000 people.He was arrested days after the U.S. operation – which Pakistan called a violation of its sovereignty – and charged with aiding terrorists.Afridi was sentenced to 23 years’ jail for financing terrorism. That conviction was overturned in 2013, but he is still serving time for other terrorism-related convictions, his lawyer said.He also faced a murder trial related to the death of a patient more than a decade ago.However, the layer said Afridi had recently had his latest sentence reduced to seven years in a clemency action, and had served about that amount of time already.“So I think he can be released very soon,” Nadeem told Reuters.There were no other immediate indications of any release in the works, however.A US State Department official, speaking on the condition of anonymity, called on the Pakistani government to ensure Afridi’s safety.“We are aware ýof reports that Dr. Afridi has been transferred to another prison, and call on the Government of Pakistan to take all necessary measures to ensure Dr. Afridi’s safety,” the official said.“We don’t have anything else at this time and would refer you to the government of Pakistan as to the reasons for his transfer,” the official added.In January 2017, Pakistan’s then-law minister said the country would not release Afridi under any U.S. pressure.“Afridi worked against the law and our national interest, and the Pakistan government has repeatedly been telling the United States that under our law he committed a crime and was facing the law,” Zahid Hamid was quoted as saying at the time.last_img read more

Top 7 libraries for geospatial analysis

first_imgThe term geospatial refers to finding information that is located on the earth’s surface. This can include, for example, the position of a cellphone tower, the shape of a road, or the outline of a country. Geospatial data often associates some piece of information with a particular location. Geospatial development is the process of writing computer programs that can access, manipulate, and display this type of information. Internally, geospatial data is represented as a series of coordinates, often in the form of latitude and longitude values. Additional attributes, such as temperature, soil type, height, or the name of a landmark, are also often present. There can be many thousands (or even millions) of data points for a single set of geospatial data. In addition to the prosaic tasks of importing geospatial data from various external file formats and translating data from one projection to another, geospatial data can also be manipulated to solve various interesting problems. Obvious examples include the task of calculating the distance between two points, calculating the length of a road, or finding all data points within a given radius of a selected point. We use libraries to solve all of these problems and more. Today we will look at the major libraries used to process and analyze geospatial data. GDAL/OGR GEOS Shapely Fiona Python Shapefile Library (pyshp) pyproj Rasterio GeoPandas This is an excerpt from the book, Mastering Geospatial Analysis with Python by Paul Crickard, Eric van Rees, and Silas Toms. Geospatial Data Abstraction Library (GDAL) and the OGR Simple Features Library The Geospatial Data Abstraction Library (GDAL)/OGR Simple Features Library combines two separate libraries that are generally downloaded together as a GDAL. This means that installing the GDAL package also gives access to OGR functionality. The reason GDAL is covered first is that other packages were written after GDAL, so chronologically, it comes first. As you will notice, some of the packages covered in this post extend GDAL’s functionality or use it under the hood. GDAL was created in the 1990s by Frank Warmerdam and saw its first release in June 2000. Later, the development of GDAL was transferred to the Open Source Geospatial Foundation (OSGeo). Technically, GDAL is a little different than your average Python package as the GDAL package itself was written in C and C++, meaning that in order to be able to use it in Python, you need to compile GDAL and its associated Python bindings. However, using conda and Anaconda makes it relatively easy to get started quickly. Because it was written in C and C++, the online GDAL documentation is written in the C++ version of the libraries. For Python developers, this can be challenging, but many functions are documented and can be consulted with the built-in pydoc utility, or by using the help function within Python. Because of its history, working with GDAL in Python also feels a lot like working in C++ rather than pure Python. For example, a naming convention in OGR is different than Python’s since you use uppercase for functions instead of lowercase. These differences explain the choice for some of the other Python libraries such as Rasterio and Shapely, which are also covered in this chapter, that has been written from a Python developer’s perspective but offer the same GDAL functionality. GDAL is a massive and widely used data library for raster data. It supports the reading and writing of many raster file formats, with the latest version counting up to 200 different file formats that are supported. Because of this, it is indispensable for geospatial data management and analysis. Used together with other Python libraries, GDAL enables some powerful remote sensing functionalities. It’s also an industry standard and is present in commercial and open source GIS software. The OGR library is used to read and write vector-format geospatial data, supporting reading and writing data in many different formats. OGR uses a consistent model to be able to manage many different vector data formats. You can use OGR to do vector reprojection, vector data format conversion, vector attribute data filtering, and more. GDAL/OGR libraries are not only useful for Python programmers but are also used by many GIS vendors and open source projects. The latest GDAL version at the time of writing is 2.2.4, which was released in March 2018. GEOS The Geometry Engine Open Source (GEOS) is the C/C++ port of a subset of the Java Topology Suite (JTS) and selected functions. GEOS aims to contain the complete functionality of JTS in C++. It can be compiled on many platforms, including Python. As you will see later on, the Shapely library uses functions from the GEOS library. In fact, there are many applications using GEOS, including PostGIS and QGIS. GeoDjango, also uses GEOS, as well as GDAL, among other geospatial libraries. GEOS can also be compiled with GDAL, giving OGR all of its capabilities. The JTS is an open source geospatial computational geometry library written in Java. It provides various functionalities, including a geometry model, geometric functions, spatial structures and algorithms, and i/o capabilities. Using GEOS, you have access to the following capabilities—geospatial functions (such as within and contains), geospatial operations (union, intersection, and many more), spatial indexing, Open Geospatial Consortium (OGC) well-known text (WKT) and well-known binary (WKB) input/output, the C and C++ APIs, and thread safety. Shapely Shapely is a Python package for manipulation and analysis of planar features, using functions from the GEOS library (the engine of PostGIS) and a port of the JTS. Shapely is not concerned with data formats or coordinate systems but can be readily integrated with such packages. Shapely only deals with analyzing geometries and offers no capabilities for reading and writing geospatial files. It was developed by Sean Gillies, who was also the person behind Fiona and Rasterio. Shapely supports eight fundamental geometry types that are implemented as a class in the shapely.geometry module—points, multipoints, linestrings, multilinestrings, linearrings, multipolygons, polygons, and geometrycollections. Apart from representing these geometries, Shapely can be used to manipulate and analyze geometries through a number of methods and attributes. Shapely has mainly the same classes and functions as OGR while dealing with geometries. The difference between Shapely and OGR is that Shapely has a more Pythonic and very intuitive interface, is better optimized, and has a well-developed documentation. With Shapely, you’re writing pure Python, whereas with GEOS, you’re writing C++ in Python. For data munging, a term used for data management and analysis, you’re better off writing in pure Python rather than C++, which explains why these libraries were created. For more information on Shapely, consult the documentation. This page also has detailed information on installing Shapely for different platforms and how to build Shapely from the source for compatibility with other modules that depend on GEOS. This refers to the fact that installing Shapely will require you to upgrade NumPy and GEOS if these are already installed. Fiona Fiona is the API of OGR. It can be used for reading and writing data formats. The main reason for using it instead of OGR is that it’s closer to Python than OGR as well as more dependable and less error-prone. It makes use of two markup languages, WKT and WKB, for representing spatial information with regards to vector data. As such, it can be combined well with other Python libraries such as Shapely, you would use Fiona for input and output, and Shapely for creating and manipulating geospatial data. While Fiona is Python compatible and our recommendation, users should also be aware of some of the disadvantages. It is more dependable than OGR because it uses Python objects for copying vector data instead of C pointers, which also means that they use more memory, which affects the performance. Python shapefile library (pyshp) The Python shapefile library (pyshp) is a pure Python library and is used to read and write shapefiles. The pyshp library’s sole purpose is to work with shapefiles—it only uses the Python standard library. You cannot use it for geometric operations. If you’re only working with shapefiles, this one-file-only library is simpler than using GDAL. pyproj The pyproj is a Python package that performs cartographic transformations and geodetic computations. It is a Cython wrapper to provide Python interfaces to PROJ.4 functions, meaning you can access an existing library of C code in Python. PROJ.4 is a projection library that transforms data among many coordinate systems and is also available through GDAL and OGR. The reason that PROJ.4 is still popular and widely used is two-fold: Firstly, because it supports so many different coordinate systems Secondly, because of the routes it provides to do this—Rasterio and GeoPandas, two Python libraries covered next, both use pyproj and thus PROJ.4 functionality under the hood The difference between using PROJ.4 separately instead of using it with a package such as GDAL is that it enables you to re-project individual points, and packages using PROJ.4 do not offer this functionality. The pyproj package offers two classes—the Proj class and the Geod class. The Proj class performs cartographic computations, while the Geod class performs geodetic computations. Rasterio Rasterio is a GDAL and NumPy-based Python library for raster data, written with the Python developer in mind instead of C, using Python language types, protocols, and idioms. Rasterio aims to make GIS data more accessible to Python programmers and helps GIS analysts learn important Python standards. Rasterio relies on concepts of Python rather than GIS. Rasterio is an open source project from the satellite team of Mapbox, a provider of custom online maps for websites and applications. The name of this library should be pronounced as raster-i-o rather than ras-te-rio. Rasterio came into being as a result of a project called the Mapbox Cloudless Atlas, which aimed to create a pretty-looking basemap from satellite imagery. One of the software requirements was to use open source software and a high-level language with handy multi-dimensional array syntax. Although GDAL offers proven algorithms and drivers, developing with GDAL’s Python bindings feels a lot like C++. Therefore, Rasterio was designed to be a Python package at the top, with extension modules (using Cython) in the middle, and a GDAL shared library on the bottom. Other requirements for the raster library were being able to read and write NumPy ndarrays to and from data files, use Python types, protocols, and idioms instead of C or C++ to free programmers from having to code in two languages. For georeferencing, Rasterio follows the lead of pyproj. There are a couple of capabilities added on top of reading and writing, one of them being a features module. Reprojection of geospatial data can be done with the rasterio.warp module. Rasterio’s project homepage can be found on Github. GeoPandas GeoPandas is a Python library for working with vector data. It is based on the pandas library that is part of the SciPy stack. SciPy is a popular library for data inspection and analysis, but unfortunately, it cannot read spatial data. GeoPandas was created to fill this gap, taking pandas data objects as a starting point. The library also adds functionality from geographical Python packages. GeoPandas offers two data objects—a GeoSeries object that is based on a pandas Series object and a GeoDataFrame, based on a pandas DataFrame object, but adding a geometry column for each row. Both GeoSeries and GeoDataFrame objects can be used for spatial data processing, similar to spatial databases. Read and write functionality is provided for almost every vector data format. Also, because both Series and DataFrame objects are subclasses from pandas data objects, you can use the same properties to select or subset data, for example .loc or .iloc. GeoPandas is a library that employs the capabilities of newer tools, such as Jupyter Notebooks, pretty well, whereas GDAL enables you to interact with data records inside of vector and raster datasets through Python code. GeoPandas takes a more visual approach by loading all records into a GeoDataFrame so that you can see them all together on your screen. The same goes for plotting data. These functionalities were lacking in Python 2 as developers were dependent on IDEs without extensive data visualization capabilities which are now available with Jupyter Notebooks. We’ve provided an overview of the most important open source packages for processing and analyzing geospatial data. The question then becomes when to use a certain package and why. GDAL, OGR, and GEOS are indispensable for geospatial processing and analyzing, but were not written in Python, and so they require Python binaries for Python developers. Fiona, Shapely, and pyproj were written to solve these problems, as well as the newer Rasterio library. For a more Pythonic approach, these newer packages are preferable to the older C++ packages with Python binaries (although they’re used under the hood). Now that you have an idea of what options are available for a certain use case and why one package is preferable over another, here’s something you should always remember. As is often the way in programming, there might be multiple solutions for one particular problem. For example, when dealing with shapefiles, you could use pyshp, GDAL, Shapely, or GeoPandas, depending on your preference and the problem at hand. Read Next: Introduction to Data Analysis and Libraries 15 Useful Python Libraries to make your Data Science tasks Easier “Pandas is an effective tool to explore and analyze data”: An interview with Theodore Petrou Using R to implement Kriging – A Spatial Interpolation technique for Geostatistics datalast_img read more

Israel tourism numbers jumped 25 in 2017

first_img Posted by Share Thursday, January 4, 2018 Travelweek Tags: Israel Israel tourism numbers jumped 25% in 2017 JERUSALEM — Israel posted huge tourism numbers in 2017, with reports of a 25% increase and a record 3.6 million visitors bringing in US$5.8 billion of revenue.According to Israel’s Tourism Ministry, the increase of 700,000 tourists last year was a result of 18 new routes flying into Tel Aviv’s Ben-Gurion Airport as well as more flights to the Red Sea resort of Eilat, reports Reuters.The country saw additional airlift from Ryanair, Lot, WizzAir and WOW, as well as Air Canada and Transat. Hainan Airlines in particular is credited with boosting tourism from China by 46% with a new route.The United States, Russia, France, Germany and Great Britain were the top incoming markets in 2017. The top two tourist destinations were Jerusalem followed by Tel Aviv.Israel’s Tourism Ministry has been making big strides in infrastructure and tourism initiatives. Tourism Minister Yariv Levin has noted several initiatives are proving effective, including giving financial incentives to airlines to open new routes, collaborating with online travel agents, and working with entrepreneurs to build more hotels. << Previous PostNext Post >> About Latest Posts Travelweek Latest posts by Travelweek (see all) Another win for The Travel Agent Next Door: RCI Partner of the Year, Canada – March 28, 2019 You’re not imagining it, that waterfall really is coming out of a sksyscraper – July 26, 2018 Israel tourism numbers jumped 25% in 2017 – January 4, 2018last_img read more