It’s been three months since CARTO.js v4 reached the stable release state. Since then, we’ve been receiving very good feedback and researching what our customers are using the library for. Today, it’s my honor to introduce CARTO.js v4.1!

Focus on easier development

Our goal with the next milestone of CARTO.js is to help developers with the most common struggles we’ve seen so far. We’re introducing:

• Buffer map changes: we take care of map changes so you don’t run into limit bottlenecks.
• Source filters: an easier way to filter sources of data so you don’t have to maintain a heavy structure for filtering.
• Histogram range: now you can ask a certain range to a histogram to focus on the data you find more interesting.

Buffering map changes

With CARTO.js v4, we introduced a shift on the way of creating applications with our library. Instead of an out-of-the-box solution, we provide now simpler building blocks that you can structure on top of your applications. That change is super powerful. You are now not restricted by what our solution provides. You can leverage your knowledge of Leaflet or Google Maps and use CARTO.js as another piece to answer your needs.

But with great power comes great responsability. CARTO.js map changes being programmatic means that now we can provoke changes in the map at the speed of light. We’ve seen applications making changes to lots of dataviews and layers at the same time, hitting our Engine platform limits. The solution was to structure better the application with those limits in mind. Until now.

In CARTO.js v4.1 we’re freeing developers from changing the code to avoid running into performance problems. Now we buffer your changes and apply them once they’re all finished. You don’t have to worry about that anymore. And the best part is that’s transparent. We do it under the hood so no changes in your code are needed to get advantage of it.

Source filtering

Another pain point we’ve seen during the last months is related to source filtering. The way you can filter your data (through a widget, through code… ) forced you to maintain a structure to build SQL sentences taking that filter into account. Not a big deal with one filter. A cumbersome task when combining lots of filters.

In CARTO.js v4.1 we’re introducing two source filters: the category filter and the range filter.

Category filter

One of the most common filtering actions in a map is to show some information that belongs to certain categories. With the category filter now you can tell a source to show only the data that has certain values in a column. Indeed, you can apply a broad criteria: in, not in, equal, not equal, like, similar to… Reference.

As an example, imagine you want to show apartment rental prices only in certaing districts. With the new category filter is as easy as:

const source = new carto.source.Dataset('apartments');
const districtFilter = new carto.filter.Category('district_group', { in: ['Diamond District', 'Tudor City', 'Little Brazil'] });
source.addFilter(districtFilter);

If you change your mind later, just set other values and the source will react to the new filter criteria.

districtFilter.set('in', ['Kips Bay', 'Gramercy Park', 'Flatiron District']);

More info in Category filter Developers reference.

Range filter

The other common filtering action is to restrict the numeric information to certain values. It’s the operation made when selecting a range in a histogram, for instance.

As an example, imagine you want to show only locations with a rental price between 3K$ and 4K$. With the new range filter is as easy as:

const source = new carto.source.Dataset('apartments');
const priceFilter = new carto.filter.Range('price', { between: { min: 3000, max: 4000 } });

Of course, you can alter the filter afterwards.

priceFilter.setFilters({ between: { min: 4000, max: 5000 } });

More info in Range filter Developers reference.

Combining filters

The real power comes when using several filters at the same time. As you can see in the examples documentation you can apply a filter as complex as you want and not worry about a single line of SQL code.

For instance, you could combine the two filters above (district and filter) with another one that only selects those apartments that rents the entire home or those who has recent reviews.

const filtersCombination = new carto.filter.AND([
  neighbourhoodFilter,
  priceFilter,
  new carto.filter.OR([ entireHomeFilter, reviewsInLastYearFilter ])
]);

We encourage you to take a look on the developers portal to learn about this great enhancement. Examples

Custom histogram range

In CARTO.js v4, we added dataviews, objects able to extract data from a dataset in predefined ways. One of them is the histogram dataview, used to represent the distribution of numerical data.

Until CARTO.js v4.1, the histogram operated on the whole range of the selected column. That is, if you wanted to get the histogram of the column price, the histogram showed the data from the minimum value in the whole dataset to the maximum one. Although this is very convenient and it’s the most likely way to use it, there are some cases where you want to focus on a particular range of the histogram. The typical example is a column where most of the data falls into one bin and the rest of them are empty because of the existence of outliers. In this case, the histogram doesn’t tell you the right insights and, probably, you want to ask for the data in the most populated bin.

In CARTO.js v4.1 the histogram dataview now provides two extra parameters: start and end.

const histogramDataview = new carto.dataview.Histogram(
  source,
  'price', {
    bins: 5,
    start: 40,
    end: 60
  }
);

This dataview will return a histogram whose range goes from 40$ to 50$, instead of the whole price range.

We can’t wait to see your incredible location intelligence apps using these new features!!

Happy mapping!

Caros leitores,

Quero convidá-los a participarem do Curso Online de GeoServer que estarei ministrando pela GEOCURSOS. O objetivo do curso é que você aprenda a disponibilizar, compartilhar e editar dados geográficos na internet com o GeoServer.

No curso serão abordados tópicos como: configuração de dados, criação de estilo com SLD, padrões OGC, interface administrativa (web), visualização cartográfica com OpenLayers, REST API, Segurança, entre outros.

O curso ocorrerá entre os dias 21 e 30 de agosto (terças, quartas e quintas) das 20:00 as 22:00 (horário de Brasília).

Aqueles que poderem divulgar para seus contatos, agradeço. Quem quiser saber mais informações sobre o curso, pode obtê-las no site do curso (http://www.geocursos.com.br/geoserver), twitter (http://twitter.com/geo_cursos) e pelo facebook (http://www.facebook.com/geocursosbr).

• Java
• PHP 5.x
• .net (full framework)

• PHP 5.6 (our current bundled version of PHP) will be end-of-life on December 2018. Bundling and targeting an EOL version of PHP is not a good look. To bundle the current version of PHP (7.x) we need to be able to generate a compatible PHP extension for it. We can't do that with current internal copy of SWIG as the zend extension APIs have massively breaking changes from PHP5 to PHP7.
• .net Core is where the action is at in the .net space, and not having a presence in this space diminishes our story of being able to build MapGuide applications in .net because as time goes on, that definition of ".net" will assume to mean both the (windows-only) full framework and (cross-platform) .net core.
• We may want to add support for additional programming languages in the future. Can't do it with our super-modified copy of SWIG again because of the unclear history of changes made to this tool.

• A Java binding modeled on the non-crufty official Java binding. Requires Java 7 or higher.
• A (currently windows-only) PHP extension for PHP 7.1
• A netstandard2.0-compatible binding for .net that works on both .net Core and full framework and is also cross-platform for platforms where both .net Core and official MapGuide binary packages are available for. For Linux, that means this .net binding works in Ubuntu 14.04 64-bit (where .net Core also has packages available). The nuget package for this binding is fully self-contained and includes the necessary native dependencies (both 32 and 64-bit) needed for the .net library to work. For .net full framework, it includes an MSBuild .targets file to ensure all the required native dependencies are copied out to your application's output directory.

Dear Reader, we are glad to inform you that our R&D project SaveMyBike has been selected by the European Commission as one of the 21 finalists for the "REGIOSTARS AWARDS 2018" for Category 2 “Achieving sustainability through low carbon emissions”.

The main target of the project is to simplify the way we refer to the soft mobility, to make it convenient, immediate, a certainty in everyday life; we want to make soft mobility a pleasant habit and not a further difficulty. Our aim is to relief cities from the oppression of cars, to rediscover the serenity of alternative transport modes, to take on city traffic nodes and to release them once and for all. Sustainable mobility challenges are epochal, but we could begin from small gestures, from everyday life, to reach the target.

In a future post we are going to discuss about the technical infrastructure that is powering the project.

The GeoSolutions Team,

Last week, I traveled to Salzburg to attend the 30th AGIT conference and co-located English-speaking GI_Forum. Like in previous year, there were a lot of mobility and transportation research related presentations. Here are my personal highlights:

This year’s keynotes touched on a wide range of issues, from Sandeep Singhal (Google Cloud Storage) who – when I asked about the big table queries he showed – stated that they are not using a spatial index but are rather brute-forcing their way through massive data sets, to Laxmi Ramasubramanian @nycplanner (Hunter College City University of New York) who cautioned against tech arrogance and tendency to ignore expertise from other fields such as urban planning:

One issue that Laxmi particularly highlighted was the fact that many local communities are fighting excessive traffic caused by apps like Waze that suggest shortcuts through residential neighborhoods. Just because we can do something with (mobility) data, doesn’t necessarily mean that we should!

Not limited to mobility but very focused on open source, Jochen Albrecht (Hunter College City University of New York) invited the audience to join his quest for a spatial decision support system based on FOSS only at bit.ly/FiltersAndWeights and https://github.com/geojochen/fosssdss

The session Spatial Perspectives on Healthy Mobility featured multiple interesting contributions, particularly by Michelle P. Fillekes who presented a framework of mobility indicators to assess daily mobility of study participants. It considers both spatial and temporal aspects of movement, as well as the movement context:

Figure from Michelle Pasquale Fillekes, Eleftheria Giannouli, Wiebren Zijlstra, Robert Weibel. Towards a Framework for Assessing Daily Mobility using GPS Data. DOI: 10.1553/giscience2018_01_s177 (under cc-by-nd)

It was also good to see that topics we’ve been working on in the past (popularity routing in this case) continue to be relevant and have been picked up in the German-speaking part of the conference:

Of course, I also presented some new work of my own, specifically my research into PostGIS trajectory datatypes which I’ve partially covered in a previous post on this blog and which is now published in Graser, A. (2018) Evaluating Spatio-temporal Data Models for Trajectories in PostGIS Databases. GI_Forum ‒ Journal of Geographic Information Science, 1-2018, 16-33. DOI: 10.1553/giscience2018_01_s16.

My introduction to GeoMesa talk failed to turn up any fellow Austrian GeoMesa users. So I’ll keep on looking and spreading the word. The most common question – and certainly no easy one at that – is how to determine the point where it becomes worth it to advance from regular databases to big data systems. It’s not just about the size of the data but also about how it is intended to be used. And of course, if you are one of those db admin whizzes who manages a distributed PostGIS setup in their sleep, you might be able to push the boundaries pretty far. On the other hand, if you already have some experience with the Hadoop ecosystem, getting started with tools like GeoMesa shouldn’t be too huge a step either. But that’s a topic for another day!

Since AGIT&GI_Forum are quite a big event with over 1,000 participants, it was not limited to movement data topics. You can find the first installment of English papers in GI_Forum 2018, Volume 1. As I understand it, there will be a second volume with more papers later this year.

This post is part of a series. Read more about movement data in GIS.

Two new plugins have been published for the management of duplicate values ​​in a field of the attribute table of a vector layer.

The first plugin is an improvement of the already existing duplicate selection tool, where all the elements with duplicate values ​​were selected in a field of the table. With the new functionality the first one won’t be selected now. With this the user has both options available.

Keep in mind that in 2.4 version both tools are in two different menus (Selection and Table) and in two different buttons at toolbar, but from 2.4.1 they will be unified in the Selection menu, and in a drop-down button of the toolbar.

The second plugin allows us to count different values in a field of the attribute table. This new tool is added as a geoprocess in the Toolbox, and selecting the table and the field, we would obtain a new table (available in Project Manager-> Table) with the different values ​​of the selected field, and the number of times that each one is repeated.

To install both plugins we must access to the Add-ons Manager (Tools menu) and search “duplic” term, where they will appear. We will have to mark them and after being installed we must restart gvSIG.

At this video you can watch how these tools work:

Se han publicado dos nuevos plugins para la gestión de valores duplicados en un campo de la tabla de atributos de una capa vectorial.

El primero sería una mejora del ya existente de selección de duplicados, donde se seleccionaban todos los elementos con valores duplicados en un campo de la tabla. Con la nueva funcionalidad ahora nos dejaría el primero de ellos sin seleccionar. Con ello el usuario tiene ambas opciones disponibles.

Hay que tener en cuenta que en la versión 2.4 ambas herramientas se encuentran en dos menús diferentes (Selección y Tabla) y en dos botones diferentes, pero a partir de la 2.4.1 se unificarán en el menú Selección, y en un botón de la barra de herramientas con un desplegable.

El segundo plugin es el de conteo de valores en un campo de la tabla de atributos. Esta nueva herramienta se añade como un geoproceso más en la Caja de herramientas, y con él, seleccionando la tabla y el campo deseados, obtendríamos una nueva tabla (disponible en el Gestor de proyectos->Tabla) con los distintos valores del campo seleccionado, y el número de veces que se repite cada uno.

Para instalar dichas extensiones se debe entrar en el Administrador de complementos (menú Herramientas), y buscar el término “duplic”, donde nos aparecerán los dos paquetes, que se deben marcar y se descargarán. Tras instalarlos se debe reiniciar gvSIG.

En este vídeo podéis ver cómo funcionan ambas herramientas:

The PostGIS development team is pleased to release PostGIS 2.5.0beta1.

This release is a work in progress. No more api changes will be made from this point on before 2.5.0 release. Remaining time will be focused on bug fixes and documentation for the new functionality and performance enhancements under the covers. Although this release will work for PostgreSQL 9.4 and above, to take full advantage of what PostGIS 2.5 will offer, you should be running PostgreSQL 11beta2+ and GEOS 3.7.0beta1 which were released recently.

Best served with PostgreSQL 11beta2 which was recently released.

View all closed tickets for 2.5.0.

After installing the binaries or after running pg_upgrade, make sure to do:

ALTER EXTENSION postgis UPDATE;

— if you use the other extensions packaged with postgis — make sure to upgrade those as well

ALTER EXTENSION postgis_sfcgal UPDATE;
ALTER EXTENSION postgis_topology UPDATE;
ALTER EXTENSION postgis_tiger_geocoder UPDATE;

If you use legacy.sql or legacy_minimal.sql, make sure to rerun the version packaged with these releases.

2.5.0beta1

• source download
• NEWS
• pdf doc download
• html doc download
• epub doc download
• Changelog

Dear Reader,

in this post we want to talk about how we used GeoNode, GeoServer, and MapStore to build a platform for visualization and cost-benefit analysis to support decision makers with resilient development planning, public policy and investments in Afghanistan in cooperation with the GFDRR group at the World Bank. While in this post we will concentrate a little more on the technicalities behind the platform, in case you were interested more into the higher level motivations and objectives you can read this blog post from the GFDRR group.

The goal of the project is to improve the decision-making and data-extraction capabilities for Afghanistan by expanding GeoNode with two additional modules:

• The first, called Risk Data Extraction and Visualization, to allow users to easily generate maps and extract tabular results for their area and indicator of interest at different return periods and with the ability to drill down at different administrative levels.
• The second, called Cost/Benefit Analysis and Decision Tool, to  allow users to perform costs-benefits analysis for various hazards (primarily earthquakes and floods) using pre-calculated risk management options, thus allowing the user to discover the benefits of investing in risk reduction.

Risk Data Extraction & Visualization

This module enables users to easily visualize and extract risk data for the area of interest at different administrative levels (district, province, national). Based on the selection of administrative area, indicator and return period, the user is presented with a map and a series of charts about the risk assessments.

Input tables are delivered for each indicator (population, GDP, roads, etc), outlining damage/value for all return periods, an example is provided in the table below.

The tool is able to ingest tabular data (i.e. Excel files and CSV files), automatically process and convert inputs into internal model linked to administrative areas, and finally to present to the users a friendly and modern interface to navigate the collected information.  A summary of all the Risk Analysis available per Hazard type at the country level is presented to users as a first landing page (see below).

Users can click on the analysis panel to open the brief abstract and then click on 'Analysis Data' button to go into the detailed data page, as shown below.

The detailed view of the Risk Analysis presents the title of the analysis along with its brief description. An interactive chart that let the user to change the values shown in the map interactively. On top of the map, a toolbar gives the possibility to navigate through the data, get information or switch layers or dimensions.  The “Dimensions Switcher” tool allows to switch between “Scenarios” and “Return Periods” view instead. Charts and dashboards are updated dynamically. Additional GeoNode resources and documents (PDF reports, images, …) can also be linked to the analysis data and presented to the user for further readings. Last but not least, as shown below, data is available at multiple administrative levels (country, region, county) and we can use the interactive map to drill down geographically, as shown below.

Cost-benefit Analysis & Decision Tool

This module allows the users to access precomputed (obviously by domain experts) cost-benefit analyses of risk management options in a user-friendly way by making use of maps and charts. This various cost-benefit analyses have been conducted for a number of risk management options for floods, earthquakes, and landslides. For each of the options precomputed  reductions in disaster losses have been produced and ingested into the system (a specific ingestion system was developed suitably for this).  Input tables are delivered for each indicator with precomputed  data as in the table below.

The cost-benefits analysis overview is similar to the previous one, see below.

The cost-benefit detail dashboards change accordingly to the type of analysis. Typically cost-benefit analysis is conducted on site-specific use cases (roads, rivers, schools, sub-areas, etc…). Panels, charts and dashboards are automatically rendered by the tool and comparison charts and tables are presented to the users for each “Risk Reduction Scenario”. Here below an example for Flood hazard and its impact on the buildings in Kabul.

The entire framework leverage on GeoNode and GeoServer Parametric SQL Views while the front-end is based on modern technologies and concepts like material views on a single page application based on MapStore and D3-JS for the rendering of charts and dashboards. If you are interested in knowing more about the platform, please, feel free to send us some emails. The code is Open Source as usual, although these very specific modules will not be contributed back to the GeoNode community version.

Last but not least, if you are interested in learning about how we can help you achieving your goals with open source products like GeoServer, MapStore, GeoNode and GeoNetwork through our Enterprise Support Services and GeoServer Deployment Warranty offerings, feel free to contact us!

The GeoSolutions Team,

Ya está disponible para instalar en gvSIG el plugin que permite cargar los ficheros XML de SIGPAC, el Sistema de Información Geográfica de Parcelas Agrícolas de España.Para instalar esta extensión se debe acceder al administrador de complementos de gvSIG (menú “Herramientas”), seleccionando la opción por URL, y conectando al servidor por defecto. En la siguiente ventana escribiremos en la parte superior “SIGPAC”, y nos aparecerá la extensión, que debemos marcar. Una vez instalada deberemos reiniciar gvSIG y ya tendremos la herramienta disponible en el menú “Herramientas->SIGPAC”.

Si la capa XML es de líneas o puntos, se cargará como un fichero CSV, y si es de polígonos se cargará directamente como SHP.

Una vez nos aparece el fichero en la ventana de “Añadir capa”, si está en un sistema de referencia diferente al de la Vista deberemos entrar en sus propiedades e indicárselo. De esa forma se reproyectará al vuelo, por lo que deberemos exportar la capa a un nuevo SHP para que esté ya en el sistema de la Vista.

En el siguiente vídeo podéis ver el funcionamiento:

TLTR: This post is about Joppy, a new service I’m working on that tries to eliminate the pain currently exists in the communication among recruiters and tech professionals. Let me describe you the current scenario in the recruitment world and, please, any feedback will be welcome.

Tech professionals: Wherever I wrote tech professionals I mean any kind of role related with tech companies: software engineers, developers or programmers, manager, product owners, QA, designers, …

It all starts…

…taken a beer and asking your friends: How many connection requests do you receive per week from LinkedIn? All three (two developers and a UI/UX designer) answer the same enough to be annoying. Every tech professional want to have his/her CV updated in LinkedIn, it is a great service, but no one agrees with the myriad of emails asking for connections from recruiters that has awesome job offers from awesome companies.

Do you thing the job of a recruiter is easy? Well, let me say you are completely wrong. It is not an easy job and often ungrateful. If you think in a more or less important city with many tech companies you can image the competition existing among companies to get tech professional.

Currently there are two main things recruiters can do to arrive to candidates:

1. Publish offers in some kind of board and wait candidates applies (we all have in mind web sites that crawls and shows tons of job offers)
2. Make an active search of potential candidates. Recruiters need to use services like LinkedIn, where they can search techies in a given geographical area, that know about X, Y, Z skills and many other options. Once filtered they need to contact each of them where, probably, most of them are not interested in a change or in the position the recruiter is offering.

Dear Reader,

in this post we want to talk about how we twisted and extended GeoNode, GeoServer, and MapStore to build a platform for emergency response and early warning for the DeCATastrophize European Project (acronym DECAT).

DECAT Project

Effective systems for early warning, mitigation of impacts, and emergency management can save lives and protect people, property and the environment in the event of natural and man-made disasters. The goal of the DECAT project is to design, implement and operate a geospatial decision support system to assess, prepare for and respond to multiple and/or simultaneous natural and man-made hazards and disasters in a synergistic way. The DECAT platform provides:

• Workflows and functionalities for early warning and rapid notification for risk resilience at all levels
• Methodologies for rapid assessment and mitigation of impacts and decision-making through rapid mapping
• Ability to disseminate geospatial data and information about various types of multi-hazards
• Dedicated capabilities aimed to support impact assessment as well as emergency management based on activities suitable for overall operational scenarios

The types of hazards that were taken into account is extensive and so is the scenarios which the platform was tested; the list of hazards comprehends the following:

• Wildfire
• Tsunami
• Flood
• Earthquake
• Oil Spill

The DECAT Platform

The DECAT platform is composed by a few modules designed to support the needs of the three main phases of emergency management, in the order:

• Early Warning, during which events are collected and analyzed in order to understand if we are facing of a real hazard or a false alarm
• Impact Assessment, which triggers once an event has been confirmed to be an hazard  to model, under the guidance of a domain expert, the impact of an hazards on various targets
• Mitigation of Impact, which deals with the management of the emergency derived from the impact of the to mitigate its impact.

The above modules provide the operators with specific tools and customized layouts for each phase of emergency management.

The Early Warning module provides the operator with wizards to create, edit and update so called events which represent potential hazards occurring inside his area of competence; this is supported via geospatial tools to edit point features and record ancillary information useful to characterize events and assess the level of hazard, generally used to revise and debrief the emergency response. Each event can be searched, modified and updated, to evolve to an occurrence treating the community or back to ordinary conditions. In the first case, it will be promoted and notified as early warning, otherwise it will be archived (see below); once an event is promoted the other phases are enabled and the entire workflow to assess the impact and manage the emergency comes to life.

As mentioned above, once an event is confirmed (i.e. an earthquake has struck somewhere) the impact assessment phase triggers in order to evaluate and model the hazard as well as to assess its evolution and impact over time. The Impact assessment module allows the impact assessor (i.e. a domain expert with scientific background and experience regarding effects and losses occurring because of a specific type of disaster) to evaluate the context and the environment where the event is taking place, providing by modelling or pre-formulated scenario analyses, additional geospatial information, reports and documents useful to properly identify and locate specific needs of rescue and recovery interventions. This module has been designed to permit the creation and update of the so-called Common Operational Picture (COP) for the emergency managers, which is an evolving geospatial representation of the hazards integrated with preliminary localization of rescue and recovery targets, by integrating relevant hazard models outputs in real-time. The impact assessor has the possibility to create a reference map (the COP) for the emergency management coupling hazards modelling together with geospatial information relevant for emergency plan implementation (e.g. gathering areas, field hospital location, command and control field unit) as well as targets needing urgent intervention. The symbols used to visualize the feature can be adapted to the subject and changed according to its specific evolution. The COP can then be frozen to a specific instant, and shared with the emergency managers, responsible to assign rescue or recovery targets to work-force teams (see below), however the impact assessor can, at any time, perform a new assessment by updating information in the active COP to create an updated one that would more closely represent the current situation.

The last set of functionalities is related to the coordination of field workforce and Emergency Management; thanks to this module the platform provides the emergency managers with capabilities to collaboratively (and concurrently) manage online, directly on the COP geospatial features representing allocated teams, customized according to the type of workforce they belong. Such geospatial features can be updated over time to capture the status of resources engaged with the rescue operations on the field (see figure below) as well as the changing conditions on the field. Moreover, updates of the COP generated by newer impact assessment to capture the evolution of the disaster can be published at any time by the impact assessor and they will refresh background information used by the emergency managers.

Technologies and building blocks

The DECAT platform is implemented by leveraging on a few on well-known Open Source building blocks like GeoServer, GeoNode and MapStore, as shown below. GeoServer provides advanced geospatial data management and mapping capabilities according to the OGC Web Map Services (WMS), Web Coverage Services (WCS) and Web Feature Services (WFS) protocols while GeoNode acts as a broker for the data providing OGC Catalogue Services (CSW) capabilities, acting as the catalog for data and metadata discovery. MapStore is used as the mapping and visualization engine and provides geospatial visualization functionalities over the data ingested into the DSS by interacting with OGC protocols.

Authentication is provided through the support for OAUTH 2.0 protocol having GeoNode play the provider role (i.e. being responsible for the management of users’ credentials and live sessions) hence it takes care of creating and expiring users’ sessions as well as of managing access permission over the ingested geospatial data in coordination with GeoServer. In its default configuration data is private and accessible only to the publisher and the users within his organization. Several additional modules have been implemented during the project to create the DECAT DSS by extending the GeoNode and MapStore frameworks. The user-interface has been completely redesigned to follow a three phases approach during the management of the emergency, where specific back-end modules have been developed in GeoNode to manage alerts and hazards, to perform and disseminate impact assessments associated to hazards, up to the annotations used by emergency managers to support resource allocation to targets in the field.

Conclusions and way forward

A final Table Top / Command Post exercise was conducted in the area of Paphos, Cyprus to assess the utility and usability of the DECAT Platform in disaster preparedness and response. The main objective of the exercise was to test the platform in realistic scenarios to evaluate its impact on existing decision making processes during emergency situations. The tested scenarios included a wildfire threatening the forest of Paphos, nearby villages and other infrastructure as well as an heavy rainfall in the Town of Paphos with a great danger for floods. The DECAT platform was used in order to extract and provide valuable information to the decision makers during the exercise, all the three phases of the platform were used and successfully presented (Early Warning, Impact Assessment and Emergency Management).

The DECAT Platform has been an important step for GeoSolutions in order to strenghten its knowledge on the GeoNode framework given the size and depth of the customizations, moreover it allowed us to perform a first integration between MapStore and GeoNode which is part of our overall strategy for the future of GeoNode. Some of the implemented functionalities and fixes have been already contributed to the respected projects (e.g. map annotations developed to manage field resources during the emergency management phase are now port of MapStore and so are a large number of GeoNode fixes and improvements).

For those who are interested, the DECAT Platform is still up and running here (mind you, this is a snaphot of the cloud instance used during the tests so most data is password protected.; however, most data is not open to the public hence if you are interested in having a look we can schedule a demo. Last but not least, the source code is, obviously, Open Source like everything we do here at GeoSolutions. This work was cofinanced by DG-ECHO under the DECAT European project.

If you are interested in learning about how we can help you achieving your goals with open source products like GeoServer, MapStore, GeoNode and GeoNetwork through our Enterprise Support Services and GeoServer Deployment Warranty offerings, feel free to contact us!

The GeoSolutions Team,

We are very excited to announce that QGIS Server has been successfully certified as a compliant WMS 1.3 server against the OGC certification platform, and moreover, it is even considered as a reference implementation now!

This is the first step on our roadmap of having a fast, compliant and bullet proof web map server that is straightforward to publish from a classical QGIS project.

What does it mean?

Having a certified server means that QGIS Server successfully passes the automated and semi automated tests that ensure we are 100% compliant with the standards. That means you can trust QGIS to be used by any WMS client seamlessly.
Moreover, that certification is now powered by a continuous integration system that checks every night in developement versions if we still pass the tests.

Daily compliance reports are available on the new tests.qgis.org website.

What’s next?

Building the automated testing platform and getting officially certified was only the first step. We now are starting to certify the WFS services, thanks to the latest grant application program support.

We also want QGIS server development to be performance-driven. The following projects are particularly relevant:

• MS-Perf produces benchmark reports with MapServer and GeoServer.
• graffiti  and PerfSuite tools have been designed to create a really light tool, easy to enrich with new datasets and performance tests, and easy to integrate in continuous integration systems. It compares QGIS-ltr, QGIS-rel and QGIS-dev nightlies for the same scenarios in details and produces html reports. It can also graph performance history for the development version to track regressions or improvements.

Many thanks to the supporters and voting members that helped bootstrap all those testing platforms and offer them to the community.

If you want to support or give a hand on the QGIS desktop client side, we think that area would deserve some love too!

Ya está disponible la certificación del curso gratuito de gvSIG aplicado a Medio Ambiente.

Esta certificación se abre tras la publicación de los últimos temas del curso, pero seguirá abierta de forma continua, por lo que cualquier usuario/a podrá obtenerla en el momento en que finalice los distintos temas.

Para poder obtener dicha certificación se deberán completar todos los ejercicios de cada tema. Los ejercicios validarán los conocimientos adquiridos durante el curso y serán evaluados por un tutor.

La plataforma y la opción de matrícula abierta la podéis encontrar en www.geoalternativa.com/gvsig-training. Es necesario elegir el curso de gvSIG aplicado a Medio Ambiente. Después debéis elegir la opción “Registrarse como usuario” y, por último, matricularos en él.

Aparte de la entrega y aprobación de los ejercicios, la certificación llevará un coste mínimo, necesario para cubrir los gastos relativos a la evaluación y certificación. Este coste será de 30 €. El pago es posible hacerlo a través de Paypal en el siguiente enlace: http://www.gvsig.com/es/curso-gvsig-aplicado-medio-ambiente

La certificación será emitida por la Asociación gvSIG, y estará compuesta por dos certificados:

– Certificado de aprovechamiento del curso, que incluirá toda la información relativa a los contenidos formativos adquiridos.

– Certificado oficial gvSIG Usuario, al haber completado los 90 créditos necesarios para ello, y que da derecho a poder obtener el certificado de gvSIG Usuario, realizando y aprobando los créditos necesarios para su convalidación, a través de los cursos ofrecidos por la Asociación gvSIG.

El tiempo de dedicación del curso se ha estimado en 90 horas.

We've got customers discovering PostGIS and GIS in general or migrating away from ArcGIS family of tools. When they ask, "How do I see my data?", we often point them at QGIS which is an open source GIS desktop with rich integration with PostGIS/PostgreSQL.

QGIS is something that is great for people who need to live in their GIS environment since it allows for easily laying on other datasources, web services and maps. The DBManager tool allows for more advanced querying (like writing Spatial SQL queries that take advantage of the 100s of functions PostGIS has to offer) , ability to import/export data, and create PostgreSQL views.

QGIS has this thing called Projects, which allow for defining map layers and the symbology associated with them. For example what colors do you color your roads, and any extra symbols, what field attributes do you overlay - street name etc. Projects are usually saved in files with a .qgs or .qgz extension. If you spent a lot of time styling these layers, chances are you want to share them with other people in your group. This can become challenging if your group is not connected via network share.

We are pleased to announce the release of QGIS 3.2 ‘Bonn’. The city of Bonn was the location of our 16th developer meeting.

This is the second release in the 3.x series. It comes with tons of new features (see our visual changelog).

Packages and installers for all major platforms are available from downloads.qgis.org.

We would like to thank the developers, documenters, testers and all the many folks out there who volunteer their time and effort (or fund people to do so). From the QGIS community we hope you enjoy this release! If you wish to donate time, money or otherwise get involved in making QGIS more awesome, please wander along to qgis.org and lend a hand!

QGIS is supported by donors and sponsors. A current list of donors who have made financial contributions large and small to the project can be seen on our donors list. If you would like to become and official project sponsor, please visit our sponsorship page for details. Sponsoring QGIS helps us to fund our six monthly developer meetings, maintain project infrastructure and fund bug fixing efforts. A complete list of current sponsors is provided below – our very great thank you to all of our sponsors!

QGIS is Free software and you are under no obligation to pay anything to use it – in fact we want to encourage people far and wide to use it regardless of what your financial or social status is – we believe empowering people with spatial decision making tools will result in a better society for all of humanity.

We are extremely pleased to announce the winning proposals for our 2018 QGIS.ORG grant programme. Funding for the programme was sourced by you, our project donors and sponsors! Note: For more context surrounding our grant programme, please see:

• QGIS Grants #3: Call for Grant Proposals 2018
• QGIS Grant Applications 2018

The QGIS.ORG Grant Programme aims to support work from our community that would typically not be funded by client/contractor agreements, and that contributes to the broadest possible swathe of our community by providing cross-cutting, foundational improvements to the QGIS Project.

Voting to select the successful projects was carried out by our QGIS Voting Members. Each voting member was allowed to select up to 6 of the 14 submitted proposals by means of a ranked selection form. The full list of votes are available here (on the first sheet). The second sheet contains the calculations used to determine the winner (for full transparency). The table below summarizes the voting tallies for the proposals:

A couple of extra notes about the voting process:

• The PSC has an ongoing program to fund documentation so elected to fund the QGIS Training Manual update even if this increases the total funded amount beyond the initial budget.
• Although the budget for the grant programme was €25,000, the total amount for the winning proposals is €35,500. This increase is possible thanks to the generous support by our donors and sponsors this year.
• Voting was carried out based on the technical merits of the proposals and the competency of the applicants to execute on these proposals.
• No restrictions were in place in terms of how many proposals could be submitted per person / organization, or how many proposals could be awarded to each proposing person / organization.
• Voting was ‘blind’ (voters could not see the existing votes that had been placed).

Of the 45 voting members, 29 registered their votes 17 community representatives and 12 user group representatives.

On behalf of the QGIS.ORG project, I would like to thank everyone who submitted proposals for this call!

A number of interesting and useful proposal didn’t make it because of our limited budget; we encourage organizations to pick up one of their choice and sponsor it.

One of the joys of geospatial processing is the variety of tools in the tool box, and the ways that putting them together can yield surprising results. I have been in the guts of PostGIS for so long that I tend to think in terms of primitives: either there’s a function that does what you want or there isn’t. I’m too quick to ignore the power of combining the parts that we already have.

A community member on the users list asked (paraphrased): “is there a way to split a polygon into sub-polygons of more-or-less equal areas?”

I didn’t see the question, which is lucky, because I would have said: “No, you’re SOL, we don’t have a good way to solve that problem.” (An exact algorithm showed up in the Twitter thread about this solution, and maybe I should implement that.)

PostGIS developer Darafei Praliaskouski did answer, and provided a working solution that is absolutely brilliant in combining the parts of the PostGIS toolkit to solve a pretty tricky problem. He said:

The way I see it, for any kind of polygon:

• Convert a polygon to a set of points proportional to the area by ST_GeneratePoints (the more points, the more beautiful it will be, guess 1000 is ok);
• Decide how many parts you’d like to split into, (ST_Area(geom)/max_area), let it be K;
• Take KMeans of the point cloud with K clusters;
• For each cluster, take a ST_Centroid(ST_Collect(point));
• Feed these centroids into ST_VoronoiPolygons, that will get you a mask for each part of polygon;
• ST_Intersection of original polygon and each cell of Voronoi polygons will get you a good split of your polygon into K parts.

Let’s take it one step at a time to see how it works.

We’ll use Peru as the example polygon, it’s got a nice concavity to it which makes it a little trickier than an average shape.

CREATE TABLE peru AS 
  SELECT *
  FROM countries
  WHERE name = 'Peru'

Now create a point field that fills the polygon. On average, each randomly placed point ends up “occupying” an equal area within the polygon.

CREATE TABLE peru_pts AS
  SELECT (ST_Dump(ST_GeneratePoints(geom, 2000))).geom AS geom
  FROM peru
  WHERE name = 'Peru'

Now, cluster the point field, setting the number of clusters to the number of pieces you want the polygon divided into. Visually, you can now see the divisions in the polygon! But, we still need to get actual lines to represent those divisions.

CREATE TABLE peru_pts_clustered AS
  SELECT geom, ST_ClusterKMmeans(geom, 10) over () AS cluster
  FROM peru_pts;

Using a point field and K-means clustering to get the split areas was inspired enough. The steps to get actual polygons are equally inspired.

First, calculate the centroid of each point cluster, which will be the center of mass for each cluster.

CREATE TABLE peru_centers AS
  SELECT cluster, ST_Centroid(ST_collect(geom)) AS geom
  FROM peru_pts_clustered
  GROUP BY cluster;

Now, use a voronoi diagram to get actual dividing edges between the cluster centroids, which end up closely matching the places where the clusters divide!

CREATE TABLE peru_voronoi AS
  SELECT (ST_Dump(ST_VoronoiPolygons(ST_collect(geom)))).geom AS geom
  FROM peru_centers;

Finally, intersect the voronoi areas with the original polygon to get final output polygons that incorporate both the outer edges of the polgyon and the voronoi dividing lines.

CREATE TABLE peru_divided AS
  SELECT ST_Intersection(a.geom, b.geom) AS geom
  FROM peru a
  CROSS JOIN peru_voronoi b;

Done!

Clustering a point field to get mostly equal areas, and then using the voronoi to extract actual dividing lines are wonderful insights into spatial processing. The final picture of all the components of the calculation is also beautiful.

I’m not 100% sure, but it might be possible to use Darafei’s technique for even more interesting subdivisions, like “map of the USA subdivided into areas of equal GDP”, or “map of New York subdivided into areas of equal population” by generating the initial point field using an economic or demographic weighting.

Los próximos días 18 y 19 de octubre se celebrarán las 5as Jornadas gvSIG Uruguay y 3as Jornadas de Tecnologías Libres de Información Geográfica y Datos Abiertos en Montevideo (Uruguay), bajo el lema ‘Información Geográfica en un ámbito abierto’.

Desde ahora está abierto el periodo de recepción de resúmenes, los cuales pueden enviarse a la dirección de correo jornadas.uruguay@gvsig.org siguiendo la plantilla facilitada en el apartado Comunicaciones de la web del evento, donde pueden consultarse también las normas para el envío. Los tipos de comunicación admitidos son ponencia y póster.

Durante las jornadas se entregarán los premios del Concurso de Uso de Tecnologías Libres de Información Geográfica 2018, organizado por GeoForAll Iberoamérica y OSGeo. para el cual los usuarios de cualquier Tecnología Libre de Información Geográfica pueden enviar ya sus trabajos.

Las jornadas serán gratuitas, y el periodo de inscripción se abrirá el 24 de septiembre.

Así mismo, cualquier entidad interesada en colaborar con las jornadas, puede hacerlo de varias formas, que incluyen desde una aportación económica hasta el aporte de recursos que de forma equivalente cubran las necesidades de apoyo detectado por el comité organizador. Toda la información relacionada con ello está disponible en la web de las jornadas.

Save as a text file ending in .xml like qgis_scales.xml

These are the scales OpenStreetMap tiles are rendered in for 96 dpi, so the map will look sharp on most monitors.

The xml file can then be loaded into the project from:

Project> Project Properties…> General> Project scales

<qgsScales version="1.0">
    <scale value="1:554678932"/>
    <scale value="1:277339466"/>
    <scale value="1:138669733"/>
    <scale value="1:69334866"/>
    <scale value="1:34667433"/>
    <scale value="1:17333716"/>
    <scale value="1:8666858"/>
    <scale value="1:4333429"/>
    <scale value="1:2166714"/>
    <scale value="1:1083357"/>
    <scale value="1:541678"/>
    <scale value="1:270839"/>
    <scale value="1:135419"/>
    <scale value="1:67709"/>
    <scale value="1:33854"/>
    <scale value="1:16927"/>
    <scale value="1:8463"/>
    <scale value="1:4231"/>
    <scale value="1:2115"/>
</qgsScales>

Example:

1,000,000 (QGIS default):

1,083,357 (OSM wiki):

1,155,584 (From: 3liz):

Scales from:
OSM wiki

We are happy to announce the release of GeoServer 2.12.4. Downloads are available (zip, war, and exe) along with docs and extensions.

A new mailing list for gvSIG Developers has been created, that replaces the previous one. This list will continue being the main contact point for English speaking developers to ask about any doubt or problem on gvSIG development (Java, Scripting…).

The previous mailing list was hosted in Joinup, but their support has been ended. Therefore, we have decided to migrate the mailing list to OSGeo.

The new mailing list is available here:

https://lists.osgeo.org/mailman/listinfo/gvsig-desktop-devel You can configure the mailing list to receive list traffic bunched in digests, or if you don’t want to receive the e-mails from the list you can choose that option at settings. Then you will be able to send the doubts to the list, and you can consult replies from

http://osgeo-org.1560.x6.nabble.com/gvSIG-developers-f4175719.html

We also want to thank OSGeo for their offer to host the mailing list.

Dear Reader,

We apologize in advance, but this post is for our italian readers (hence in Italian only) to announce that we have finalized a new version of the DCAT-AP_IT Metadata Profile leveraging on the CKAN Open Data product.

Siamo lieti di condividere con voi le ultime novità che caratterizzeranno la nuova versione dell’estensione CKAN per il supporto al profilo applicativo DCAT-AP_IT. Come forse molti di voi già sapranno il profilo per la documentazione dei dati delle pubbliche amministrazioni (DCAT-AP_IT), reso disponibile dall’Agenzia per l’Italia Digitale (AgID), è nato con l’obiettivo di armonizzare i metadati con cui vengono descritti i dataset pubblici, al fine di migliorarne la qualità e favorire il riuso delle informazioni.

La prima versione, rilasciata ufficialmente nel Febbraio del 2017 e disponibile gratuitamente con licenza AGPL v3.0, fu sostenuta in uno sforzo congiunto dalla Provincia di Bolzano/Sud Tirol e dalla Provincia di Trento e fornisce ancora oggi un insieme valido ed eterogeneo di funzionalità non solo per la creazione guidata di datasets, ma anche per l’integrazione di metadati provenienti da sorgenti esterne (CSW, RDF, JSON-LD) in conformità al Profilo Applicativo. Sviluppata con scrupolosa attenzione alla stabilità delle sue caratteristiche funzionali, l’estensione ckanext-dcatapit, disponibile su una repository dedicata sotto il nostro account GitHub, nasce garantendo la più alta compatibilità possibile con le altre estensioni che spesso sono presenti nelle piattaforme CKAN. Anche gli aspetti legati al multilinguismo e la localizzazione dell’interfaccia sono stati affrontati e resi disponibili per garantire la massima usabilità da parte di quelle realtà che li necessitano, come per esempio le Provincie di Bolzano/Sud Tirol e Trento: l’estensione fornisce i propri files di localizzazione che aiutano a snellire eventuali personalizzazioni in questi termini, mentre l’estensione ckanext-multilang fornisce supporto per il multilinguismo dei contenuti presenti nel catalogo (dataset, organizzazioni, gruppi, risorse e altro).

Ad oggi non pochi sono i portali open data italiani che utilizzano questa estensione e tra questi si annoverano sicuramente:

• Il portale OpenData della Provincia di Bolzano/Sud Tirol
• Il portale OpenData del Trentino
• L’infrastruttura federata OpenDataNetwork per il capofila Città Metropolitana di Firenze, che raccoglie e distribuisce i dati di vari enti toscani tra cui: Città Metropolitana di Firenze, Provincia di Prato, Provincia di Pistoia ed Autorità di Bacino dell’Arno.

Ma anche molti altri tra cui:

• Regione Sardegna
• Regione Emilia Romagna
• Comune di Francavilla Fontana
• Regione Sicilia
• Regione Toscana
• Comune di Matera
• Citta Metropolitana di Napoli
• Comune di Milano
• MIUR (Ministero dell'Istruzione dell'Università e della Ricerca)

Grazie all’interesse mostrato dall’Agenzia per l’Italia Digitale (AgID) riguardo alle potenzialità e alle caratteristiche proprie di questa estensione, hanno avuto inizio alla fine del 2017 gli sviluppi per la realizzazione di una nuova versione arricchita e migliorata.

Gli sforzi di AgID nel finanziare questo progetto hanno avuto l’obiettivo di creare un unico hub di raccolta nazionale per i dataset pubblici basato su CKAN e fornire quindi, in un unico punto di accesso, le principali informazioni sui dati aperti esposti dalle PA locali e centrali (si fa in particolar modo riferimento al progetto DAF e la sua componente Dataportal).

Un insieme eterogeneo di funzionalità e peculiari caratteristiche sono state introdotte nella nuova versione per garantire non solo una più completa adesione al Profilo Applicativo, ma anche per aiutare l’utente nella ricerca dei datasets con nuove funzioni di indicizzazione e raggruppamento dei dataset stessi per regione di provenienza. È stato aggiunto dunque il supporto alla cardinalità multipla per le proprietà che la richiedono (come per esempio gli identificativi del dataset,  temi e sottotemi, autori e altre) e le funzionalità di catalogo sono state arricchite per identificare il catalogo e l’organizzazione di origine dei dataset  harvestati. In aggiunta, nuove facets saranno disponibili per filtrare i datasets per catalogo di origine, regioni e sottotemi.

Anche il supporto al vocabolario controllato dei sottotemi, precedentemente mancante, è stato introdotto insieme al vocabolario controllato per la classificazione del territorio che consente l’associazione di una o più regioni italiane ad ogni organizzazione e facilitare quindi la ricerca dei datasets.

Gli sforzi per la realizzazione della nuova versione si sono concentrati anche sul consolidare e accrescere le funzionalità di harvesting dei dataset con lo scopo sia di organizzare e catalogare al meglio i dataset raccolti ma anche di correggere, per quanto possibile, eventuali difformità nei dataset di origine (per esempio temi non conformi al Profilo Applicativo). Tra gli aspetti importanti riguardanti l’harvesting dei dataset troviamo anche i seguenti:

• Migliorata la validazione dei tag in modo da gestire tag non conformi
• Introdotto la mappatura delle licenze non conformi con quelle del vocabolario controllato aggiornato
• Consolidamento delle funzionalità di harvesting già esistenti

Come ultimo punto, ma non per questo meno importante, lo sviluppo di una infrastruttura basata su Docker è stato messo a disposizione (attualmente ancora in fase di testing in vista del prossimo rilascio). Questo progetto, sviluppato in parallelo dal team di GeoSolutions, è disponibile sul GitHub Developers Italia e mette a disposizione tutto ciò di cui avete bisogno per ottenere rapidamente ed in pochi passi un’installazione completa di CKAN corredata dell’estensione ckanext-dcatapit.

Invitiamo tutti coloro che sono interessati a partecipare allo sforzo per lo sviluppo di questa estensione o che fossero interessati ad utilizzare questa estensione a seguire il nostro blog o iscriversi alla nostra newsletter; raccomandiamo di visionare anche i nostri pacchetti di supporto professionale GeoSolutions Enterprise Support Services nel caso si volesse usufruire di un supporto attento e qualificato per la messa in produzione di questa estensione. Allo stesso modo vi invitiamo a visionare le informazioni sugli altri nostri prodotti Open Source quali GeoServer, Mapstore, GeoNode e GeoNetwork.

The GeoSolutions team,

PostGIS 2.5 is just around the corner. One of the new functions coming is the ST_OrientedEnvelop. This is something I've been waiting for for years. It is the true minimum bounding rectangle, as opposed to ST_Envelope which is an axis aligned bounding rectable.

Below is a pictorial view showing the difference between the two.

La Asociación gvSIG, fiel a su compromiso de dar visibilidad a los proyectos de SIG Libre, colabora con el XVIII Congreso Nacional TIG que se realizará en Valencia entre los días 20 y 22 de junio.

Tendremos un stand donde estaremos encantados de hablar con cualquier institución o empresa interesada en implementar cualquiera de los productos de gvSIG, informaremos de las últimas novedades de la Suite gvSIG y tendremos algún que otro obsequio para las personas visitantes.

Además, impartiremos un taller para aprender scripting en gvSIG Desktop. Será el jueves 21 de 16:00 a 17:30.

Os facilitamos el enlace para ver el programa completo:

https://congresos.adeituv.es/tig2018/paginas/pagina_451_25.es.html

• implement remaining classes
• code documentation
• comprehensive support of projection methods (at least the set currently supported by GDAL)
• import from and export to PROJ strings for CRS definitions and coordinate operations
• use of the EPSG dataset

• never ever open (actually close) a SQLite database with regular file API while libsqlite3 is operating on it (in the same process)
• POSIX advisory locks are awful.

Some of the outputs from my Data Driven Cartography workshop at the 2nd OSGeo Ireland conference.

Gaelic Football:

Final frame:

Hurling:

Final frame:

Quando trabalhamos com Sistemas de Informações Geográficas (SIG), alguns trabalhos são bastante repetitivos, beirando à chatice.

Extrair, cortar, realizar analise, extrair, cortar, realizar analise. Quantas vezes você vai ficar repetindo esse processo? Por que não programar para realizar todo ele de uma vez só?

Já apresentamos como realizar esse tipo de procedimento utilizando o ArcGIS, e caso você queira mais detalhes sobre o que é Python, dê uma olhada na nossa postagem sobre como usar Python e a ferramenta Buffer no ArcGIS.

Nesta postagem, utilizaremos o QGIS 2.18 e Python para extrair o limite do município de Cocal do Sul (e de outros municípios), em seguida, iremos utilizar esse limite para recortar o mapa de solos do Estado de Santa Catarina.

Nos links abaixo, você poderá baixar os shapefiles que usaremos nesta postagem.

• Limites Municipais de Santa Catarina: IBGE.
• Mapa Pedológico de Santa Catarina: IFFSC / EPAGRI CIRAM.

Como acessar o Python no QGIS (PyQGIS)?

Antes de adicionarmos qualquer shapefile, vamos abrir o terminal e o editor python do QGIS para escrever e executar nossos comandos.

Vá em Plugins e clique em Python Console (1). Provavelmente somente o Terminal será aberto, mas você pode habilitar o editor clicando em “Show Editor” (2), conforme figura abaixo.

Iniciando o módulo Python no QGIS 2.18.

Com todas essas janelas abertas, vamos começar a programar na janela que foi aberta quando clicamos em “Show Editor”.

Carregando shapefile usando PyQGIS

Agora, vamos carregar algumas bibliotecas e vamos utilizar a função addVectorLayer() para adicionar nossos dois shapefiles.

#!/usr/bin/env python
#coding: utf-8

# Carregando bibliotecas no Python
from PyQt4.QtCore import *
from PyQt4.QtGui import *
from qgis.core import *
from qgis.gui import *
import processing
import sys

# Adicionando nossos arquivos shapefile
lim_mun = "C:/Users/ferna/Desktop/PyQGIS/42MUE250GC_SIR.shp"
solos_sc = "C:/Users/ferna/Desktop/PyQGIS/Solos_Santa_Catarina_250000_2004.shp"
iface.addVectorLayer(lim_mun, "Limites Municipais", "ogr")
iface.addVectorLayer(solos_sc, "Solos de SC", "ogr")

A função addVectorLayer recebe três parâmetros, são eles (1) o local onde foi salvo o shapefile; (2) nome da camada e (3) o identificador da fonte de dados, normalmente ogr.

Extraindo o limite municipal

Agora, com as camadas carregadas, iremos acessar os dados da camada “Limite Municipal” e por meio da ferramenta Extract by Attribute, iremos separar o limite do município de Cocal do Sul em um novo shapefile.

Iremos utilizar a função “processing” e dentro dela vamos especificar o algoritmo para extração de dado baseando-se em um atributo.

# Rodando algoritmo para extrair limites de Cocal do Sul
limCocal = "C:/Users/ferna/Desktop/PyQGIS/limCocal.shp"
processing.runalg('qgis:extractbyattribute', lim_mun, "NM_MUNICIP", 0, "COCAL DO SUL", limCocal)

Com o limite municipal de Cocal do Sul em mãos, já podemos rodar o nosso código seguinte para recortar os tipos de solos existentes dentro do município.

Cortando a partir de um polígono

Neste procedimento, utilizaremos a função Clip para recortar as classes de solo do município de Cocal do Sul.

Mas antes de executarmos esse código, nossos shapefiles estão em sistemas de projeção diferentes, isso pode ocasionar erros no momento do cruzamento dos mapas. Por isso, antes de rodar o código de recorte, vamos executar o qgis:reprojectlayer, para converter o shapefile do tipo de solo para SIRGAS2000.

# Convertendo os sistemas de coordenadas
Solos_SIRGAS = "C:/Users/ferna/Desktop/PyQGIS/Solos_SIRGAS.shp"
processing.runalg("qgis:reprojectlayer", solos_sc, "epsg:4674", Solos_SIRGAS)

# Recortando nossa area de estudo
solosCocal = "C:/Users/ferna/Desktop/PyQGIS/solosCocal.shp"
processing.runalg('qgis:clip', Solos_SIRGAS, limCocal, solosCocal)
iface.addVectorLayer(solosCocal, "Solos de Cocal do Sul", "ogr")

E dessa forma, conseguimos extrair os tipos de solos do município de Cocal do Sul.

Note que as funções Reproject Layer e Clip precisam dos seguintes argumentos:

• Reproject Layer: (1) Arquivo de Entrada, (2) Sistema de Coordenadas Final, (3) Arquivo de Saída.
• Clip: (1) Arquivo a ser cortado, (2) Limites do corte, (3) Arquivo de Saída.

Como realizar esse processo para varias cidades?

Para isso, vamos construir uma variável com os nosso municípios alvos (i.e. Ermo, Forquilhinha, Nova Veneza e Garopaba) e em seguida, criaremos um loop do tipo for para rodar todos eles, veja abaixo.

# Extraindo multiplos mapas de solo com um loop
cidades = ["ERMO", "FORQUILHINHA", "NOVA VENEZA", "GAROPABA"]
for n in cidades:
  limite = "C:/Users/ferna/Desktop/PyQGIS/lim" + n +".shp"
  processing.runalg('qgis:extractbyattribute', lim_mun, "NM_MUNICIP", 0, n, limite)
  solos = "C:/Users/ferna/Desktop/PyQGIS/solos" + n + ".shp"
  processing.runalg('qgis:clip', Solos_SIRGAS, limite, solos)
  iface.addVectorLayer(solos, "Solos de " + n , "ogr")

Note que após realizar esse processo, você poderá substituir a variável cidades pelos municípios que você deseja, desde que o nome esteja igual ao nome do município no shapefile com os limites municipais.

Obs.: Estou pesquisando como extrair os municípios que apresentam caracteres especiais em seus nomes (i.e. acentos, cedilhas), pois até agora, não consegui extrair o shape de solos deles. Até onde pesquisei, tem a ver com a codificação dos caracteres (UTF-8). Caso você tenha a resposta, deixe ela nos comentários que estaremos agradecendo e atualizando a postagem.

[Atualização em 18/06/2018]

Corrigindo o problemas de Encoding

Depois de pesquisar um pouco sobre como converter um shapefile para um Encoding diferente, conseguimos realizar o procedimento anterior para municípios com acentos.

O novo código tem uma parte para realizar essa correção, onde carregamos novamente o shapefile (função QgsVectorLayer()) e em seguida salvamos ele num novo formato (função QgsVectorFileWrite().writeAsVectorFormat())

Lembre-se também de colocar um “u” na frente dos textos que contém acentos, como fizemos na variável cidades.

Confira o código completo abaixo.

# -*- coding: utf-8 -*-

# Carregando pacotes no Python
import processing
import sys
import osgeo.ogr as ogr
import osgeo.osr as osr

# Carregando nossos arquivos shapefile
lim_mun = "C:/Users/ferna/Desktop/PyQGIS/42MUE250GC_SIR.shp"
solos_sc = "C:/Users/ferna/Desktop/PyQGIS/Solos_Santa_Catarina_250000_2004.shp"

# Convertendo os sistemas de coordenadas
Solos_SIRGAS = "C:/Users/ferna/Desktop/PyQGIS/Solos_SIRGAS.shp"
processing.runalg("qgis:reprojectlayer", solos_sc, "epsg:4674", Solos_SIRGAS)

# Corrigindo Encoding
Camada = QgsVectorLayer(lim_mun, None, 'ogr')
Camada.setProviderEncoding("utf8")
Camada.dataProvider().setEncoding("utf8")

lim_UTF8 = "C:/Users/ferna/Desktop/PyQGIS/lim_UTF8.shp"
QgsVectorFileWriter.writeAsVectorFormat(Camada, lim_UTF8, "utf_8_encode", Camada.crs(), "ESRI Shapefile")

# Extraindo vários itens com acentos
cidades = [u"CRICIÚMA", u"MORRO DA FUMAÇA"]
for n in cidades:
  limite = "C:/Users/ferna/Desktop/PyQGIS/lim" + n +".shp"
  processing.runalg('qgis:extractbyattribute', lim_UTF8, "NM_MUNICIP", 0, n, limite)
  solos = "C:/Users/ferna/Desktop/PyQGIS/solos" + n + ".shp"
  processing.runalg('qgis:clip', Solos_SIRGAS, limite, solos)
  iface.addVectorLayer(solos, "Solos de " + n , "ogr")

Caso tenha ficado com alguma dúvida, deixe ela nos comentários que estaremos respondendo assim que possível.

Sharing data and information related to risk management is a stringent necessity. Natural events have an ever increasing impact on global population and assets, mostly in areas with reduced capabilities to deal with emergencies. Disaster management, prevention, and planning activities require access to the most up to date and detailed information available for a geographic area. Often data resides in some remote corners of the web hardly discoverable or, in the worst case, is kept segregated in local storage reducing or completely cancelling its value. Moreover data often lacks fundamental information (metadata) regarding its contents and formats.

The GFDRR group of World Bank tackled this within the second round of its "Challenge Fund" initiative. One of its goals was designing a common data model to store and share data about exposures, hazards and vulnerabilities, and a web platform to ingest, explore and download these data.

GeoSolutions was committed for its design and development. The project has been based on a lightweight User Centered Design approach,  doing interviews to stakeholders and collecting suggestions from domain experts. The results of this phase was a mockup that in a few weeks has become the base of the HEV-E Platform.

Although being in its initial phase a first release of the platform was released a few weeks ago.  New and improved functionalities will be added in the future. Thanks to a great set of tools we were able to go online in a couple of months:

• GeoNode: it provides the metadata services the and layers views management
• Geoserver: (OGC) map services and layers styling
• Django: a dedicated project has been implemented to expose custom APIs and extending GeoNode functionalities
• MapStore and React: the frontend is completely based on MapStore (our flagship frontend framework) and new specific React components

The final result is a Catalog that let users explore Exposures, Hazards and Vulnerabilities functions currently available (for the moment just a small sample dataset).

The original dataset are split into single layers, that can be searched and filtered through the HEV-E catalog frontend. From the catalog a preview of the geographical area covered by the layer, and a preview of the layer content themselves, is shown on the contextual map. A user can add a layer to the map, to keep it around while continuing navigating through the catalog. Being in sync with the area currently shown on the map, the catalog helps the user obtaining only the relevant layers for the context currently explored.

For each layer a Detail view provides insights on the specific layer's contents, according to the layer type and contents. As an example, for exposures a chart with the number of occurrences by construction material or type of occupancy is shown.

During the exploration the user is able to add each layer to a Download area, a functionality is similar to common e-store "shopping carts". From this area layers can be selected to submit a download orders, that will be managed by the HEV-E platform asynchronously. A notification or an email will be sent to the user to communicate when the requested files are ready for download. A URL to the Shapefiles, CSV or GeoPackage (the format depends on layer type and user preferences) will be provided for direct download.

The project has proved again the benefits of adopting mainstream software integration to compose advanced custom applications. GeoNode's standard functionalities cannot cover all the specific needs and often a tailored user interface with custom tools and functionalities are required, but it can be adopted as a "backend service" with a bespoke frontend and, in this case, a dedicated backend API.

We wish long life to HEV-E and we hope to continue our contribute to it and its social goals!

Last but not least, we would want to thank the GFDRR group at the World Bank which provided the funding for this work.

If you are interested in learning about how we can help you achieving your goals with open source products like GeoServer, MapStore, GeoNode and GeoNetwork through our Enterprise Support Services and GeoServer Deployment Warranty offerings, feel free to contact us!

The GeoSolutions Team,

If you’re are following me on Twitter, you’ve certainly already read that I’m working on PyQGIS 101 a tutorial to help GIS users to get started with Python programming for QGIS.

I’ve often been asked to recommend Python tutorials for beginners and I’ve been surprised how difficult it can be to find an engaging tutorial for Python 3 that does not assume that the reader already knows all kinds of programming concepts.

It’s been a while since I started programming, but I do teach QGIS and Python programming for QGIS to university students and therefore have some ideas of which concepts are challenging. Nonetheless, it’s well possible that I overlook something that is not self explanatory. If you’re using PyQGIS 101 and find that some points could use further explanations, please leave a comment on the corresponding page.

PyQGIS 101 is a work in progress. I’d appreciate any feedback, particularly from beginners!