This conference presented the data integration project using Intelli3’s Intelligence Mapping solution Map4Decision.

You will find in this blog, the conference extract, the slides and a video of the application in action.

Synopsys:

The prospective and strategies’ section is part of the Planning department at Transport Québec, which in turn reports to the Transportation policies and security department at Transport Québec.  This section is responsible for the prospective aspects, of the offer and demand factors and of all the integrated multimodal projects at the national level, including the long term vision and the measures and strategies required in order to meet the different challenges in the transportation domain.

This section expressed the need to use the functionalities offered by Map4Decision location intelligence solution in order to exploit the different data sets on international, cross borders and interprovincial commerce.

Map4Decision, which development and evolution was supported over the years by the Quebec Department  of Transportation,  has already been used in order to fine-tune the Maritime transportation, people urban mobility and the inter cities trucking flow applications framework.

The location Intelligence approach will maximize the databases exploitation potential, and rationalize the management and processing of data.  The different representations as well as the transportation flows visualization functionalities (origin/destinations matrixes and flow maps) will offer and efficient and dynamic mean to acquire knowledge on the Quebec exchanges template and their evolution. It will also allow increased efficiency in the analysis of the commerce trends and in responding to the partner’s requests.

Diapositives (in French only):Analyse géodécisionnelle en commerce international from Marie-Josée Proulx- Intelli3

Vidéo:

Cet article Cross Border International Commerce Location Intelligence Analysis est apparu en premier sur Intelli3 Blog.

Needs assessment

This post is the first of a series on How to choose the best Geospatial Data? 5 steps to reach this goal!

Before being able to start the search for your geospatial datasets, you first have to define your needs and/or your client’s needs.

The following questions are some that we have to answer to better identify needs. These examples are in the transportation domain.

What geographic entities are required?

• Roads, ports, railway station, infrastructures, merchandises, etc.

What are the required features’ properties or
attributes?

• Speed limit, pavement, clearance height, merchandise weight, merchandise type, etc.

What is the use and/or what analysis do I need to perform with my data? The answer to this question will help in order to identify; the content, the required quality level and the type of analysis Tools you will need: GIS, CAD, SOLAP.

• Optimal path computation, address geocoding, shipped tonnage per year/per region, etc.

What positional accuracy do I require?

• Positional accuracy (required accuracy of the road center should be 0.5m, 1m, 5m, ..,) and
  shape accuracy (buildings are represented by a center point, a rectangle or detailed foundations shape)

What is the required level of completeness to satisfy my requirements?

• I absolutely need all the Ports and Railway stations; I only need major roads and highways; the rivers and lakes are only shown as contextual layers in my application so I only require the main ones.

What semantic accuracy do I need?  Will attributes value have to be 100% in conformance to reality?

• Does roads classification (highways, primary roads, secondary roads) must be precise? Can I accept road classification mismatches such as a local road classified as a primary road?

What is the accuracy of the measures stored in the attribute? The accuracy of these measures is very important in multidimensional databases because these values are aggregated considering several levels of
aggregation. If they are wrong at the start, the whole system will be wrong.

• What accuracy is desirable for: merchandise tonnage, decline rate, price, surface in square meters, length in meter, etc.?

Must the data be up-to-date (data topicality or temporal accuracy)?

• For some entities which evolved slowly like waterways, data published in 2000 can be quite satisfactory. But for others entities, temporal accuracy may vary depending on the location. For example, a municipality having all of its territory occupied will not have a lot of changes in its road network. On the other hand, in a new residential development, the addition of new streets is frequent.

In which format do I want the data to be delivered?

• Shapefile, Mid/Mif, Oracle Database, KLM, GML? It is possible that this question becomes irrelevant if I have an ETL tool as I will be able to transform the data to the required format.

What are my budgetary capabilities for acquiring and processing data?

These questions are only a subset of the questions we need to ask ourselves to define our needs in terms of geospatial data. Designing a conceptual data model with those needs, like an UML class diagram, will greatly facilitate the communications with the client. When we have a good idea of what we are looking for, we can go to the next step. Needs understanding will become a lot clearer along the process and it is possible that new needs appears along the way.

Follow me in my next post, Step 2 on How to select the best Geospatial Data? Geospatial data search

Cet article How to choose the best Geospatial Data? Step 1 est apparu en premier sur Intelli3 Blog.

The GeoBI software offer

• Querying and reporting tools,
• OLAP and visualization tools,
• Dashboard tools and,
• Predictive analysis tools.

Evaluating the software offer

• Level of integration in the current geospatial processes (data creation, data updates, …)
• Supported geospatial data formats (and standards)
• Types of maps
• Visual variables
• Types of geometries
• Cartographic functions (pan, zoom, …)
• Navigation functions on maps (drill-down, roll-up, swap, …)
• Spatial analysis functions
• Management of the geometric evolution of features
• Geospatial datasets integrated with the software

• Number of components to install/maintain
• Number of different sources/vendors
• Level of integration in the current software/data architectures
• Level of automation of the various processes (e.g. for producing a map)
• Level of security
• Format conversion required
• Delays of data propagation
• Development/integration efforts required

Application example using Oracle BIEE 11g

Geospatial Integration into Oracle BIEE

Cet article The GeoBI software market based on the Gartner’s Magic Quadrant for BI Platforms 2011 est apparu en premier sur Intelli3 Blog.

1. Facilitates of data visualization and interpretation

2. Adds a common reference in space

• people georeferenced by their address
• animal species georeferenced by their geographic coordinates
• administrative, political or physical regions.

3. Adds a common reference in time

Cet article How geospatial can enhance your BI applications? est apparu en premier sur Intelli3 Blog.

Road Infrastructures

A wave of interest in the democratization of public data struck Quebec (Montreal Open Data Hackathon, Capitale Ouverte) and the world (Open Data Day). The main objective of the “Open data” movement is to offer new applications or uses of public data.

In reality, the available information in public organizations can be very rich, but it is not always valued at its best. For example, the Bridges and Road-sites information to citizens posted by Transports Québec only provides information on the status of road infrastructure in the province (e.g., bridge, wall, culvert, and tunnel).

This site provides the pedigree of each individual structure and shows the list of structures by region, the accessibility index and general condition index. A few days after the posting of this site, a Google application was developed (The Gazette) to present the dots of the 5228 structures and a fact sheet on each of the structures.

However, the Quebec Infrastructure data offers the potential for much greater analysis than what has been demonstrated so far.

To demonstrate our point, we caught the ball on the rebound (and the data on this site). Now starting with the operational data available, we will bring a more strategic and analytical aspect by producing different groupings in order to compare structures in time and in space.

Based on the data we had at our disposal, it is important to mention that the process described hereafter required only a few hours for structuring data, and allowed us to add new information to the initial Quebec Transportation Dept. infrastructure data. While ensuring quick results, we can easily imagine the various analysis and data comparisons (crossings) that could be achieved with access to more elaborate data and collaboration from the Transportation Dept. experts.

Presentation of our geodecisional application

• Cartographic axis to locate individual structures, but also by municipality, county regional municipalities, administrative region and province.
• Temporal axis to group the dates of last inspection and next inspection by year for all years.
• Thematic axis for grouping structures by:
• Type of structure (e.g. suspended bridge), group (e.g. bridge), all types
• Accessibility Index (i.e. no restrictions), all indexes AI
• General condition index (e.g., requiring major work), all indexes GCI

Count of structures mapped by region (left) to the specific details of a structure at the most detailed level (right).

Map of the general condition index by county and regional municipalities (RCM left) and the map of the general condition index by structure (right).

Temporal evolution of inspections of structures enumeration by year

See the video demonstration for this application: Demonstration- Inspection of Quebec Transport structures.

It would be possible with this data to cartographically illustrate the assignment of structures to the road network and to visualize the structures by major highways to smaller segments of road. Further analysis of quantitative data would produce different derived measures such as the time between the last inspection and the current month. One could certainly derive a solicitation index of the structure from the road characteristics involved such as the percentage of trucks flowing through the annual average of daily traffic and the road class.

In fact, the added value of organizational data passes by the analysis of the nature of available data and a thorough knowledge of the various opportunities in the business domain being addressed. Then, with little experience with data structuring, and a good geovisualization tool, it is possible to make data speak as you never have done before!

Description of the structuring data process.

Without the official geographic slicing used by the Department, we had no nominal equivalence for the data set. Consequently, the KMZ file of point location for individual structures was spatially tied to the geographic files of municipalities, RCM, administrative regions available from the Quebec Natural Resources Dept. to identify the geometry of ownership structures. This process allowed us to determine, for example, to which municipality, RCM and administrative area polygon each structure belongs to.

Then, the data in CSV format were imported into a database, structured in a star schematic containing the facts and analysis axes. The facts are the details of each structure (identifiers and codes that relate to attributes of each axis). We added the sum of number of structures, starting from a calculation that crossed the data for all possible combinations of analysis levels listed previously.

A homemade aggregation program allowed us to produce 360 SQL queries required to compute the statistics on the crossings of the six analysis axes. This allowed us to produce 873 765 statistical combinations using this data set and the calculation process was performed in 39 seconds.

With this statistical result in hand, in addition to the cartographic levels previously constructed, our next step was to configure the geodecisionnal solution in order to add more user-friendly mapping capabilities, and different visualization mediums: map, diagram and table.

Cet article Adding value to Transport Quebec structures’ data est apparu en premier sur Intelli3 Blog.

Whether you are a land carrier or a marine carrier, the constant concern is the optimal use of vehicles and their maximum load. The more we carry goods by vehicle, the more you reduce the cost per kilometer.

• Turnaround time: This is the average time between the time of arrival of a vehicle at the loading site and the time of its departure.
• % of truckload capacity utilized: This is the loaded weight compared to the theoretical maximum capacity. An under usability of 15% is an opportunity to be more efficient.
• True Vehicle Utilization: This is the true utilisation time of vehicles compared to the total planned utilisation time.
• Transit time: Number of Days or Hours from the time of departure to the time of arrival of the shipment.
• Empty miles: Percentage of distance (or travel) without a load (no load).
• Average number of stops per trip: Number of stops per trip.
• Tonnage Moved: Weight of goods moved by tons.

Cet article Goods transportation efficiency est apparu en premier sur Intelli3 Blog.

(c) Mihails Jershovs

• Number of Defects: Counting of the number of defects on the infrastructure. In a railway context, these defects can be presented by type: sub-gauge, over-spacing, rail wear, clogging. In fact, the Track Evaluation Car of the Canadian National Railway (CNR) can measure approximately 25 different defects with its sensors during a passage on the track. The integration of these records in a geographic information system (GIS) allows to to locate these defects and to insure their count.
• Defects Length: The defects length deducted from their location. This is used to establish the damage ratio compared to the length of the track.
• Number of Maintenance: Count of maintenance interventions by type. In a railway context: replacement of rail, joins, connector, correction of the layout, the alignment, the leveling or compacting the ballast.
• Length of the work: Length of the work derived from the location of the maintenance work. Used for establishing the ratio of repair compared to the total length of the track.
• Gravity Code: A gravity code can be defined to describe the state of the defect (e.g. from fair to urgent)
• Priority code: A priority code can qualify what are the areas that will undergo a maintenance priority (e.g. from monitoring to very urgent).
Such a system developed for the Montreal Port Authority allows to quickly get a general picture of the required information (e.g. by sector of the port) and the detailed picture (by dock, by channel, by track segment and by track subsegment). The space and time analysis of these indicators on the state of the installations is not easy because it is generally the combination of several indicators, in the same location or over a long period of time, that provide significant data to justify an intervention. Eventually, the addition of indicators related to the solicitation of the track will complete the picture by providing balanced information on the likely causes of wear in certain sectors.
For more information on the application of rail maintenance for the Montreal Port Authority installations, see the conference presented on April 12, 2011 at the Congress of the Quebec’s Association for transport and roads
Planification de la gestion et de l’entretien des installations intermodales à l’aide de la géomatique décisionnelle
View more presentations from Marie-Josée Proulx- Intelli3

Cet article Railway infrastructure maintenance est apparu en premier sur Intelli3 Blog.

Today, there are more and more geospatial data available and, depending on our needs choosing the best Spatial dataset is a task that offers more and more complexity. For example, for the province of Quebec alone, we can easily find over one hundred datasets on Geogratis, close to ten on Geobase, many geographic entities on Open Street map, several spatial datasets produced by Governmental Departments (Department of Natural Resources and Wildlife via Géoboutique, Department of Transportation, …) and many municipalities, as well as data produced by private companies such as NAVTEQ, TOMTOM and DMTI. But how to select the best geospatial data for our needs?

I will write 5 posts on this subject, showing my approach to select the best spatial datasets according to needs. I will also discuss needs assessment, metadata and data quality with transportation-related examples to help the understanding.

Stay tuned for the first step: Needs assessment

Cet article How to choose the best Geospatial Data? 5 steps to reach this goal! est apparu en premier sur Intelli3 Blog.

All the families of business intelligence client tools now include a geospatial flavour, that can take various forms: from the integration of a cartographic engine within the components of a BI suite (BI-dominant solutions) to the connection of GIS platforms to multidimensional databases (GIS-dominant solutions), and encompasses software that creates a bridge between, or completely integrates (integrated solutions), BI and geospatial components.

Some tools that combine OLAP and geospatial capabilities can be called Spatial OLAP (or SOLAP) tools. The SOLAP acronym was created by Professor Yvan Bédard, in 1997, in parallel to the term « Spatial Database », which describes the integration of geospatial data management concepts in the general concept of databases. Indeed, it is during a presentation at the 6th Geomatics Conference of the Canadian Institute of Geomatics (Montreal branch) that Dr Bédard discussed the first SOLAP concepts and his vision of such a technology as a complement to GIS. Recognized as the “Father of SOLAP”, Dr Bédard defined a SOLAP tool as “a type of software that allows rapid and easy navigation within spatial databases and that offers many levels of information granularity, many themes, many epochs and many display modes synchronized or not: maps, tables and diagrams” (see Bédard, Rivest & Proulx, 2007 for more details). The first published research work about SOLAP and the first prototypes come from three research teams recognized internationally. Dr Yvan Bédard’s team, at Université Laval, Canada, tested the combination of various OLAP and GIS technologies before designing JMap SOLAP (now Map4Decision) and published a first set of features that SOLAP tools should include (see Bédard, 1997; Rivest, Bédard & Marchand, 2001). Dr Jiawei Han’s team, at Simon Fraser University, Canada, described a method for materializing geospatial datacubes (see Han, Stefanovic & Koperski, 1998; Stefanovic, Han & Koperski, 2000) and developed GeoMiner. Dr Shashi Shekhar’s team, at University of Minnesota, USA, developed the Map Cube tool aimed at visualizing the content of geospatial data warehouses (see Shekhar, Lu, Tan, Chawla & Vatsavai, 2001). Since then, many other research teams were established throughout the world and they work on various aspects related to SOLAP (e.g. geospatial datacube modelling, spatial indexing, spatial aggregation, raster SOLAP, etc). On the commercial side, innovations came from small companies offering bridge software between OLAP and GIS solutions (ex. ProClarity), or integrated solutions (ex. Map4Decision). Now, the main BI and database vendors offer a geospatial solution, with various degrees of integration and various degrees of geospatial capabilities. For example, Oracle released last spring its new version of OBIEE 11g (11.1.1.5), which shows a high level of consolidation and a good level of geospatial capabilities. Finally, a sign of maturity of this technology, the open source software providers now invest in SOLAP to integrate the basic capabilities of commercial products. That follows the trend noted by Gartner (2011) for BI in general.

After more than 15 years since the first prototypes, the market is maturing and we see more and more implementation, at various scales. A market that is in constant evolution and definitely worth watching!

Cet article The origins of SOLAP, or the little story of a great idea! est apparu en premier sur Intelli3 Blog.

• From imagination or experience (you then set the table for the range of possibilities in an ideal world)
• From the existing data of an organization (we put our hands a little faster on what is achievable)
• From a bank of indicators (Well it exists? Oh, yes!)

The KPI Mega Library (Baroudi, R., 2010) is a good example of book to have on hand when we want to evaluate if all the performance indicators of an application have been covered.
It is actually a brick of 17 000 performance indicators. Well, some are repeated from one topic to another, but the reading is worth the time invested and can be quite inspiring. To quickly identify the potential that a decisional application would have in your organization, look up the chapter that is best suited for your industry: transport, marine port, infrastructure, trade, economy, etc.

The book stops shy of providing the recipe to set up the indicator using your organizational data, nor on how to qualify it, nor on how to illustrate this indicator in a spatial dashboard. You will need to involve experts, but the book will help you to get a better idea of your needs ;-).
In my next GeoBlogPost, I will show some examples of performance indicators in different industries while highlighting the spatio-temporal aspect that we can add. Follow the link below for the first industry:

• Goods transportation efficiency

Cet article KPI mega library : the KPI bank ! est apparu en premier sur Intelli3 Blog.