FOSS4G 2013 Keynote: Kate Chapman


Adventures in Mapmaking: How to Map the Age of Buildings in Your Hometown


You may have seen some of the beautiful maps of building ages that have been cropping up around the internet. I first noticed an amazing one of Portland, and then another great one of Brooklyn. I decided I wanted to try to make one of San Francisco, but, as I still know very little about making maps, I knew I’d need help.


So I called up Thomas Rhiel of the independent journalism site Rhiel was willing to help, and it turns out he was just the right person to advise me. His map of Brooklyn building ages was his first foray into mapmaking, so he had just muddled his way down the same path I was about to navigate.


I’m going to tell you exactly how I made this map. I hope that people with little or no experience making maps will be able to use this as a guide to getting started on a map of their own hometown. And I also hope expert mapmakers will chime in to tell us how we can improve our maps.

If you are an expert and want to skip directly to the spots where your help is needed most, look for red italics, or go to the very end (Step #8) where I’ve listed some of the known issues that I need help with. (Also, thank you so much for your help!)

Step 1: Find Some Data

San Francisco building footprints.

I don’t want to scare you off right here at the beginning, but dealing with the data is can be hard, and is often the toughest part of making a map. It will be worth it in the end, I promise.

There are really two parts to dealing with the data: hunting it down and taming it. We’ll start with the hunt. WIRED HQ is in San Francisco, so I wanted to map the buildings here. Like more and more cities, San Francisco has a website where the public can access some of the city’s data. There’s lots of good stuff here, including data for crime incidents, campaign finance, city worker salaries, speed limits, tree locations and even wind monitoring.

When I saw a building footprint shapefile, I thought my search was over. But then I uploaded it into a program called QGIS (more on this later) to look at the actual data table, and I didn’t see any ages for the buildings. I spent some more time hunting for a better dataset but ultimately decided to go where the data is. I know Chicago has an active mapping community, and I figured this must mean there is data. Sure enough, I quickly found a building footprint shapefile for the city. I popped it into QGIS, and happily, there was a column labeled “YEAR_BUILT.”

You can try to find a building footprint shapefile for your own city, or if you get frustrated by this search, you could use the data I found for Chicago to get started. Once you have found your data and downloaded it, you are ready to get some mapping software.

Step 2: Choose a Mapping Platform

I made my map using an online mapping platform called MapBox that offers a free account. Without a paid subscription, you’ll be limited to just 50MB of upload storage and 3,000 monthly views of your map if you choose to publish it. This is enough to make some simple maps, but I couldn’t figure out how to stay under the storage limit, so I ended up getting a basic subscription for $5 a month that allows gets me 250MB and 10,000 monthly views.

There are several other options that offer limited free accounts as well, such as CartoDB and ArcGISI’m sure there are many other good ones too, and hopefully you’ll let us know what they are.

Step 3: Get Some Free Mapmaking Software

If you are lucky, your dataset will be relatively clean and you can skip straight to a mapping system called TileMill, where you will design your map. If you aren’t so lucky, you may need to start with QGIS.

Either way, you will need to download TileMill, which is made by MapBox. In TileMill you will get to do things like choose your color scheme and make a legend.

If your data needs some work, you’ll use QGIS, which is free, open source mapping software. I’m told it is pretty powerful, but it’s not super easy to use. Fortunately for you and me, a new better version was just released. You’ll find everything you need to download and install QGIS on the KyngChaos Wiki. Basically you will first install something called GDAL, which QGIS needs in order to work, and then install QGIS. (The previous version involved downloading and installing what felt like about a million things, so this is already a big improvement in my opinion)

Step 4: Tame the Data

I probably could have started with TileMill, but I didn’t figure this out until a bit later. My mapping coach for this project, Thomas Rhiel started with QGIS for the Brooklyn map because he needed to do some work on the data set. New York City recently made a whole bunch of data available to the public, and the mappers there are going nuts on it, making tons of awesome maps. But, there are (of course) many problems with the data, and Rhiel ran into one of them.

Rhiel had a shapefile of building footprints that had building ID numbers (BINs) in it, another file that connected the year each building was built to a “block lot number”, and a third dataset that connected each BIN to a block lot number. He probably could have reconciled all this in Excel, but he says QGIS was faster. QGIS can help with all sorts of data manipulation, such as converting polygons to points.

I noticed that many of the year built for many the buildings in Chicago was listed as 0. Obviously these buildings were not built in the year 0, so I figured that was representing missing data. I wasn’t sure how big my problem was, so I just forged ahead. Rhiel had the same problem with the buildings in Brooklyn — about 5,000 of them had no year data. So he set up a Mechanical Turk to get help filling in those blanks. Maybe someone has some pointers on how to fill in data gaps like this.

If you know your data is pretty much ready to go, skip down to the TileMill section and save fighting with QGIS for another day. If you’re not sure, maybe just give it a try in TileMill. If this fails, or you know your data will need some work, it’s time for QGIS.

Uploading data into QGIS

Chicago building footprints in QGIS

I used this QGIS tutorial for journalists from the UC Berkeley Graduate School of Journalism to get started (it uses the previous version of QGIS, but it was close enough to work for me on this first step).

Once you have QGIS installed and open, go to the Project menu and start a new project. Then go to the Layer menu and choose “Add Vector Layer.” You will then browse to your dataset folder (which you probably downloaded as a zip file) and select the file that ends in .shp (it will probably be the only file in there you’ll be allowed to select), and wait for a map to appear (like the one to the right). Now check your data by going to the Layer menu and choosing “Open Attribute Table” or click on the icon at the top of the map window that looks like a data table.

Does your data have a column that contains the year the buildings were built? I hope so. If it doesn’t, you may need to find some more data and somehow connect it in QGIS — I have’t had to do this yet, so I don’t know how, but hopefully if you need help with this and let us know in the comments, some awesome mapper like Rhiel (or the others that made these maps) will come to your rescue. You could also try this part of the Berkeley tutorial or this MapBox tutorial on joining dataI’ll add details on this in here later if someone has some good pointers.

Now we need to head to TileMill. If you made an changes to your data in QGIS, you’ll need to save them in a new shapefile. Go to the Layer menu and choose “Save As.” Choose a name and place for the file in the Browse field. Make sure it says ESRI shapefile, and leave everything else as is.

Step 5: Design Your Map in TileMill

Now the fun part starts. If you are completely unfamiliar with html and css, this part will seem really foreign to you at first, but you can definitely do it, so hang in there.

If you haven’t downloaded TileMill, do that now. Once you have it open, open a new project, and a map of the world will open with a style.mss field next to it. Click on the icon at the bottom left that looks like a stack of papers and then click on “Add layer.” In this box, find your .shp file, and then click “Save & Style.” There will now be two layers on your map: #countries and the layer you just added (mine is called #chicago_bldgs. Your data will look like a tiny dot on the world map, so you’ll need to zoom in to at least level 12 to see it and probably 16 or higher to get a good look at the individual building shapes.

On the style sheet (is that what it’s called, or is this an official name for something else?), you’ll see some css code that looks like this:

Map {
background-color: #b8dee6;

#countries {
::outline {
line-color: #85c5d3;
line-width: 2;
line-join: round;
polygon-fill: #fff;

#chicago_bldgs {

This is what makes the map look like it does. There are different codes for every possible color. Try changing the polygon-fill code to #b21 and hit save. Your buildings should all be red now. I spent some time just messing with the codes here to figure out what they all do. Then I went fishing online for examples of code that would help me make my map look like I wanted it to.

The most important step was getting the different age ranges to be different colors. I hunted around to find how to make a ramped color scheme, where increasing values are represented by changes in shade — in my case I decided to make the older buildings the lightest shade and newer buildings darker and darker. It made sense to me that the older buildings would be fading and the newer ones would be bright. But it might also make sense that the older buildings have had more time to make an imprint and should be darker. I don’t know if there is a standard convention for this, or if it’s just preference.

Here’s what my map’s final current style sheet looks like:

#chicago_bldgs {

#chicago_bldgs {
[YEAR_BUILT <=2020] { polygon-fill:#710303; }
[YEAR_BUILT <= 1999] { polygon-fill:#b30505; }
[YEAR_BUILT <= 1979] { polygon-fill:#f40808; }
[YEAR_BUILT <= 1959] { polygon-fill:#f94747}
[YEAR_BUILT <= 1939] { polygon-fill:#fb7878; }
[YEAR_BUILT <= 1919] { polygon-fill:#fca9a9; }
[YEAR_BUILT <= 1899] { polygon-fill:#fedbdb; }
[YEAR_BUILT = 0] { polygon-fill:#615e5e; }

You’ll see that I removed the #countries layer (by deleting it from the Layers field) because I don’t need them at the scale of my map, fiddled with the line color and width, and added some rules about the YEAR_BUILT data. I’m not in love with the color scheme, but after a few other attempts, I stopped trying to make it awesome. I made this one using a website called 0to255 (which I found in this MapBox color tutorial) that gives the codes for different shades of the same color. I’m sure there are many other options for how to build a color scheme, color is a very important element of map design, so hopefully someone will let me know how to do this better.

Here are quick examples of some of the beautiful color schemes in the gallery of building ages maps.

You’ll see in my map that I made all the buildings with no age data show up as grey. Maybe there’s a better option for this, though ideally I’ll find a way to fill in that data.

One other thing I noticed with my final map is that the missing data doesn’t look like an overwhelming problem when you are zoomed all the way into the map, but when you zoom out a few levels, it looks completely grey (below). I’m not sure why this is, but I suspect it has to do with the line width. TileMill defaulted to 0.5. I changed it to 1, which looks nice when zoomed. Perhaps it would be best to have no line at all?

Now would have been the time to create a legend, and determine what data people would see when they mouse over each building (called the teaser). For some reason I assumed I’d be doing that part in MapBox, so I jumped ahead, but apparently this needs to be done in TileMill. If you want to make a legend and teaser, click on the hand icon at the lower left. You can simply write a description in the legend box here. If you want to include a bar with your color scheme, I haven’t figured that out yet.

Now click to the “Teaser” field and you’ll see a drop down menu that shows “disabled.” Choose your layer instead, and that will bring up all the names of the fields in your data. Pick the ones you want to display. Here’s what mine would have looked like had I done it:

Year Built: {{{YEAR_BUILT}}}<br/>
Address: {{{F_ADD1}}} {{{PRE_DIR1}}} {{{ST_NAME1}}} {{{ST_TYPE1}}}

Those crazy looking triple parentheses are called mustache tags. On the first line I have the words “Year Built” and this is followed by the mustache tag for that column in the data table. The <br/> just means to go to the next line so that the address info displays below the year, rather than after it on the same line (as in the image to the right).

You can also make more data available when people click on a building by using the field marked “Full.” I’m not sure how the “Location” field works.

Once you have your colors, legend, teaser and the rest of the css set, it’s time to export your map. Click on the Export button and choose MBTiles (because this is the file type MapBox likes). Here you need to choose where your map will be centered and drag the highlighted box to cover the area you want to export.

The key at this step is to limit the size of the file you will export. I spent some time trying to get the file to be under the 50MB limit for the free MapBox account, but finally gave up and spent $5 on a basic subscription. I still had to work to get the file to be under my new 250MB limit. Tightening the bounds of your map to just cover the data should help.

But still, my file was so big that TileMill told me it would take something like 19k days to export it. Rhiel explained where I had gone wrong — the most important thing to do is limit the number of zoom levels you export. He chose levels 9-17, and I ended up going with 10-16 (more would have made it too big). The number on the high end is the more critical one. TileMill exports a number of images, called tiles, for each zoom level. The further in you zoom, the more tiles are needed to cover the area. My file ended up being around 160MB. There must be some more tricks to keep the file size down, please share if you know any.

Note: Once I realized I should have done the legend and teaser in TileMill, I went back and added them. But when I tried to export the file this time, it was too big to upload into MapBox. I’m not sure if I had changed another setting accidentally, or if adding the legend and teasers makes the file a lot bigger.

Step 6: Add the Final Touches in MapBox

As I said before, there are other options, but for this project I went with MapBox, largely because Rhiel made his map of Brooklyn in MapBox. Maybe someone will let us know how to make our TileMill export small enough to keep us in a free account at MapBox, but I bought the basic $5/month subscription.

Once you have your account, start by clicking the wrench icon at the top right and choose “Upload layer.” Find your .mbtiles file and upload it. Once it’s in, name your new map and add the layer (it may take a while to process the upload).

I think I am doing something wrong here, but I had to exit this map, which seemed to just be the layer, and make a new map. Once in the new map, go to the “Customize” tab and click on the icon for “Add custom layer,” and choose the layer you just uploaded.

Next, go to the “Presets” tab and choose a base layer. I think “Streets” works best for this map, and I chose a grey background. Then zoom in to where your data is, in my case to Chicago. Then go back to the “Customize” tab and you can mess with things like the color of the water, whether you want building footprints on there (which isn’t that important here because our layer takes care of that), and the transparency of various layers.

Step 7: Publish Your Map

At this point I stopped struggling to figure out how to do various things with my map and decided to publish it, write this post, and hope to get a discussion started about how to do the rest.

To publish your map, save your changes and then click “Publish.” A box will appear where you can get the URL for your map or make a custom-size embed code. At this point, nobody can find your map without knowing the URL. If you’d like to make it public (searchable?), go to the “Settings” tab and change the Privacy setting. Here’s my map at its URL:

Congrats on publishing your map! Please share it with us by putting the URL in the comments, and include any questions you have or problems you encountered — maybe someone will help you figure it out.

Step 8: Help Me Fix My Map

There are still a bunch of things I’m not satisfied with on this map. I could have have made Rhiel very sorry that he volunteered to help me by peppering him with questions about every step, but I figured I’d try to spread the pain a little. I’ll list the problems here, and if you know how to fix any of them, I’d love to hear from you in the comments!

Issue #1: I’m missing a lot of data. Rhiel suggested I call the City Planning Department, or whichever part of the government is appropriate. I haven’t tried that yet. He used Mechanical Turk to fill in his missing data for Brooklyn. Any other suggestions?

Issue #2: My color scheme isn’t that great. How do I make it better?

Issue #3: The labels from the Streets layer are obscured by the building footprint colors. I’ve noticed this is the case on some of the really professional looking maps like this, so maybe that’s not something that can be fixed. Or maybe it’s better this way for some reason I’m not thinking of. Rhiel has the same issue, but as he pointed out, he got lucky and doesn’t have a giant “…CAGO” peeking out from behind the behind the buildings like my map does. He suggested this:

“What you can do, though, is create two maps in MapBox:
  1. One that has your buildings layer placed on top of a terrain/street layer, but with NO labels.
  2. A separate map with JUST labels — no terrain or street layer.
Then, with MapBox.js, you can just sandwich the layers on top of each other in the browser:
It’ll take some fiddling, but it’s doable.”

I gave this a try and was stumped almost immediately when I tried to make a map with just labels. Anybody else have a fix for this?

Issue #4: The file size was too big for the 50MB limit that comes with a free MapBox account. How can newbies who don’t want to pay for a subscription make their map layer small enough to upload?

Issue #5: Adding a legend and teasers seemed to make my file too big for even the 250MB limit. Is this what happened? If so, is there a way to mitigate this?

Issue #6: My map turns grey when zoomed out. Is this a line-width problem?

Issue #7: How do I get the color-scheme bar in my legend?

Issue #8: I don’t even know what all the other issues are with this map! Please tell me what you see that needs to be fixed or could be better.

Thanks for any help, comments or advice you’d like to leave in the comments!

Population Estimates in Informal Settlements Using Object-Based Image Analysis and 3D Modeling

By Almeida et al. , posted on August 16th, 2011 in ArticlesEarth ObservationUrban Monitoring

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By Cláudia M. Almeida, Cléber G. Oliveira, Camilo D. Rennó, and Raul Q. Feitosa

Urban population has been drastically increasing in developing countries in recent decades, especially in informal settlements, which are characterized by high occupation densities. In Brazilian big cities, multistory residential buildings have also started to appear in informal urban areas. Assessing the total number of inhabitants in such places has become crucial for public policies and local management. Population estimates that do not depend on field surveys are strategic in these squatter settlements because of the safety risks to field workers. This article proposes an innovative method for estimating population in a squatter settlement of Rio de Janeiro city using not only 2D but also 3D information derived from digital elevation models obtained by orbital very high resolution images, demonstrating that remotely sensed data can provide fast and fairly accurate results that can be updated continuously in the inter-census periods.


According to a 2006 UN Habitat report, at the global level, 30 percent of all urban dwellers lived in slums in 2005, a proportion that has not changed significantly since the beginning of last decade. Of these slum dwellers, 60 percent, or 581 million, are living in Asia; 20 percent, or 199 million, in Sub-Saharan Africa; and 14 percent, or 134 million, in Latin America [1]. If the share of slum dwellers remains roughly stable, however, the absolute number of their inhabitants keeps growing steadily. In the last 15 years, 283 million additional slum dwellers have joined the global urban population, and it is presently believed that the total has already surpassed one billion.

In most cities in developing countries, we clearly observe a struggle for urban space. As a result, there has been a mushrooming of high-rise residential buildings in the most recent decades, initially in central neighborhoods of big cities. Now, high-rise buildings have started to appear on the outskirts of big cities and in medium-size and small cities as well. Until a few years ago, the construction of multistory buildings was restricted to the formal city. Nevertheless, this is a phenomenon that nowadays also takes place in squatter settlements of big cities in Brazil.

Informal settlements, especially vertical ones, concentrate a great number of dwellers in high occupation densities. Population estimates that do not depend on field surveys are crucial in such settlements because of the safety risks to field workers. Moreover, conventional estimates based on either on-site sampling or head counts are time-consuming and expensive. These estimates are necessary to support local management activities, and the development and implementation of public policies and security and civil defense initiatives related to emergency and rescue services in cases of conflicts or hazards such as floods, fires and landslides.


Since the end of the 1970s and the early 1980s remote sensing data have been used worldwide for the purpose of assessing the surface and the population density of human settlements at different scales. According to [2], population estimates can be performed at the local, regional and national level based on: 1) counts of individual dwelling units [3]; 2) measurement of urbanized land areas [4]; and 3) estimates derived from land-use/land-cover classification [5]. Further alternative approaches considered texture analysis [6] and residual kriging [7].

To date, all of the studies based on measurement of urbanized land areas and land-use/land-cover classification worked on a two-dimensional basis, i.e., flat habitable areas. But a clear future trend will be to embody the third dimension in population studies in those cases where multistory residential buildings are found. This is especially true in big cities of developing countries, and in Brazil, Russia, India and China, where there are commonly tens or even hundreds or thousands of high-rise residential buildings in a single town.

A couple of studies have attempted to include 3D information derived from LIDAR in population estimates for formal urban areas [8], [9]. This work, however, focuses on an innovative method to derive population estimates with the aid of an object-based image classification and 3D information obtained by digital elevation models (DSM and DTM) of IKONOS images for a vertical squatter settlement in Rio de Janeiro city.


Fig. 1. (a) Map of Brazil and Rio de Janeiro. (b) Satellite view of Jacarepaguá. (c) True color composition of the Rio das Pedras squatter settlement. Image acquired by IKONOS satellite on January 26, 2007.



The investigated squatter settlement, which is named Rio das Pedras, is located in Jacarepaguá, a western sector of Rio de Janeiro city. The site is situated on marshlands, close to the Pedras river (Fig. 1). The dwellers themselves executed the filling works for occupying the area, which experienced two occupation booms: in the mid-1960s and in the early 1980s. Multistory buildings started to appear in the mid-1990s.


A. DSM and DTM Generation

In order to cope with the third dimension of the study area, a digital surface model (DSM) was initially built using a stereo pair of IKONOS panchromatic images, acquired on January 26, 2007. The software Geomatica 10.0.3 executed the following processing operations: 1) collection of tie points (TPs); 2) estimation of the model parameters based on the image metadata and stereo pair orientation; 3) generation of epipolar images for the whole scene; 4) calculation of parallaxes through stereo-correlation; and 5) DSM generation.

The IKONOS RPC model provides a functional relationship from the object space to the image space [10]. The RPC functional model consists in a ratio of two cubic functions of the object space coordinates. Separate rational functions usually relate normalized (scaled and offset) line and sample coordinates (xijyij) to normalized latitude, longitude and ellipsoidal height (ø, λH). The RPC model is given as [10]:

where xijyij are image coordinates; ø, λH are latitude, longitude and height; and the polynomial (k = 1, 2, 3, 4) has the form below:

P(ø, λH) = C1 + C2λC3. ø + C4H + C5λ

C6λH + C7. ø. H + C8λ2 + C9. ø2C10H2

+ C11. ø. λH + C12λ3 + C13λ .ø2 + C14λH2       (3)

C15λ2. ø + C16. ø3 + C17. ø. H2 + C18λ2H

C19. ø2H + C20H3


Fig. 2. (a) Left epipolar image and (b) right epipolar image, both oriented to the East. (c) DSM for the whole scene, oriented to the North (UTM/WGS84). The study area is located close to the bottom right corner.

The epipolar images (Fig. 2a and 2b) were generated and oriented as a function of IKONOS orbit. After the stereo pair orientation and the generation of epipolar images, the parallaxes were automatically calculated. This task was accomplished by means of search and correlation windows that locate the tie points (a total of 35) in both images. The correlation measure employed by the Geomatica OrthoEngine algorithm is the normalized cross-correlation coefficient. The generated DSM (Fig. 2c) was used for orthorectifying the panchromatic and the multispectral bands, which were later pansharpened using the principal components method.


The digital terrain model (DTM) was generated by means of an OrthoEngine filtering operation employing the tie points collected on the ground. For the special purpose of this work, the elevation model actually used in the calculations resulted from the subtraction between the DSM and the DTM, containing information on the buildings’ heights and their distribution over the study area.

B. Object-Based Image Analysis


Fig. 3. Schematic view of the semantic network showing the land-cover classes found in the study area belonging to Levels 2 and 1. Level 3 only differentiates blocks, street network and water streams.

Object-Based Image Analysis (OBIA) belongs to the major field of expert systems, a subfield of artificial intelligence, and employs modeling strategies based on thematic or specific knowledge inserted by an expert user. The basic principles of OBIA comprise multiresolution segmentation; hierarchical semantic networks meant for expert knowledge modeling and storage; and fuzzy logic for handling uncertainties inherent in the input data. OBIA was used for discriminating residential from non-residential areas according to the land-cover classification and to the average target size. A three-level semantic network was conceived based on heuristics, in which a GIS layer containing the city streets network was segmented and classified at Level 3. A discrimination among vegetation, shadow, sandy soil, asphalt and built-up areas was carried out and assigned to Level 2. A more refined classification of urban land cover was finally executed at Level 1, in all cases considering spectral and geometric information (Fig. 3)


C. Population Estimates

The 37 census districts contained within Rio das Pedras were used to calculate the projected area of the habitable surface in this squatter settlement. For each of the districts, all areas corresponding to classes related to the residential use were summed up (e.g., asbestos, dark concrete, etc.), according to the OBIA classification results. Big targets not related to the residential use, shadow on streets, clay bare soil, water streams, metallic roofs and vegetation were excluded from the calculation.


Fig. 4. Object-based classification of urban land cover in the Rio das Pedras squatter settlement, conducted at Level 1 of the semantic network.

The population of Rio city in 2000 was compared to the estimated population in 2006 (produced by the Brazilian Institute for Geography and Statistics [IBGE]), and was assessed by field interviews at sampled dwellings. The IKONOS stereo pair was acquired in January of 2007, very close to the end of 2006, when the estimates were released, so 2006 was selected as the reference year. The percentage increase in population for the whole city was applied to each of the districts.


The population density was calculated according to data supplied from thePereira Passos Institute (IPP) in Rio as a function of the inhabitants of the settlement divided by its area. It was assumed that the density remained constant from 2000 to 2006. And finally, a height factor derived from the DSM was generally applied to all of the districts. This factor results from the division of the volume related to the residential use by the average headroom for the settlement, set as 2.1 m. A height factor of 1.3 m was employed, corresponding to a 30 percent increase in the orthogonally projected residential area.

In order to evaluate the estimation results statistically, a hypothesis test was employed, using a paired t-test for means. The null hypothesis (H0: μR – μE = 0) states that there are no significant difference between the means of the reference (μR) and the estimated data set (μE), and hypothesis one (H1: μR – μE ≠ 0) states the opposite.


The final classification of urban land cover for the study area conducted at Level 1 of the semantic network with its respective legend is presented in Fig. 4.

Table showing STATISTICAL ACCURACY OF THE POPULATION ESTIMATESThe 2000 Census reported the population in Rio city at 5,613,897, and IBGE estimated the 2006 population at 6,132,652. This increase percentage of 9.24 percent was applied to the 2000 population of each census district in the squatter settlement so as to generate the 2006 reference data. In this way, the total population in the settlement in 2006 was 43,342 inhabitants, derived from the total sum of population for each district, and the population estimated with the OBIA classification and the IKONOS digital height model was 43,295 inhabitants. Table 1 presents the results of the statistical accuracy tests.


The statistical tests showed a very good Pearson correlation coefficient (ρ) and a fairly good coefficient of determination (R2). Most importantly, the pvalue result surpassed 5 percent, which means that the reference and the estimated data sets present no significant difference between their means.

Although the digital height model resulting from the subtraction between the DSM and DTM provided accurate estimates, 3D models with finer elevation accuracy would be desirable, especially when dealing with dwelling units of reduced size, as observed in squatter settlements. In such cases, the height factor could be customized for each census district.

The reference data could be refined at the informal settlement level, in the sense that the projected population rate adopted would concern not the city of Rio de Janeiro as a whole, but specifically the Rio das Pedras settlement.


The association of OBIA and 3D information derived from a digital height model from IKONOS proved to be effective for the purpose of population estimates. The main advantage of conducting population estimates by means of remotely sensed data lies in the fact that they offer fast and fairly accurate results, and can be continuously updated in the inter-census periods.

The authors acknowledge that this is a work in progress and will continue to tackle the many open questions both in the DSM/DTM generation and in the classification process. As directions for future work, the authors plan to carry out a trend analysis and a statistical validation for the digital surface model, execute accuracy tests for the object-based image analysis and explore 3-D models with higher elevation accuracy, all in support of developing more consistent methods for population estimates.


[1] United Nations Habitat. (2006). State of the World´s Cities 2006/7. [Online].

[2] J. R. Jensen, Remote Sensing of the Environment: An Earth Resource Perspective, 2nd ed. Upper Saddle River, NJ: Prentice Hall, 2007, 592 p.

[3] C. P. Lo, “Automated population dwelling unit estimation from high-resolution satellite images: A GIS approach,” Int. J. Remote Sens., vol. 16, pp. 17-34, 1995.

[4] A. J. Tatem, A. Noor, and S. Hay, “Defining approaches to settlement mapping for public health management in Kenya using medium spatial resolution satellite imagery,” Remote Sens. Environ., vol. 93, pp. 42-52, 2004.

[5] F. M. Henderson and Z. G. Xia, “SAR applications in human settlement detection, population estimation and urban land use pattern analysis: A status report,” IEEE Trans. Geosci. Remote Sens., vol. 35, pp. 79–85, Jan. 1997.

[6] X. Liu, K. Clarke, and M. Herold, “Population density and image texture: A comparison study,” Photogramm. Eng. Rem. S., vol. 72, pp. 187-196, 2006.

[7] X. H. Liu, P. C. Kyriakidis, and M. F. Goodchild, “Population-density estimation using regression and area-to-point residual kriging,” Int. J. Geogr. Inf. Sci., vol. 22, pp. 431-447, 2008.

[8] J. Im, Z. Lu, L. Quackenbush, and K. Halligan, “Population estimation based on residential building volume using LIDAR remote sensing,” Proc. 55th Annu. Meet. Assoc. Am. Geogr., Washington, D.C., 2010.

[9] F. Qiu, H. Sridharan, and Y. Chun, “Spatial autoregressive model for population estimation at the census block level using LIDAR-derived building volume information,” Cartogr. Geogr. Inf. Sci., July, 2010.

[10] J. Grodecki, “IKONOS stereo feature extraction – RPC approach,” in Proc. ASPRS Conf. 2001, St. Louis, MO, 2001.

Cláudia M. Almeida obtained her B.Sc. in Town Planning at the University of São Paulo, Brazil, her M.Sc. in Infrastructure Planning from the University of Stuttgart, Germany and her Ph.D. in Remote Sensing from both the Centre for Advanced Spatial Analysis of the University College London (CASA-UCL), United Kingdom, and the National Institute for Space Research (INPE), São José dos Campos, Brazil.

Since 2004, she has been working as a Researcher at INPE. Her research interests include remote sensing, high spatial resolution sensors, fuzzy classification, spatial dynamic modeling and cellular automata.

Cléber G. Oliveira received his B.Sc. in Cartographic Engineering at São Paulo State University – UNESP, in Presidente Prudente, SP, Brazil, his M.Sc. and his Ph.D. in Remote Sensing at the National Institute for Space Research (INPE), São José dos Campos, Brazil.

His research interests include SAR images processing and classification, stereoscopy, radargrammetry and polarimetry.

Camilo D. Rennó obtained his B.Sc. in Agronomy Engineering at São Paulo State University – UNESP, in Presidente Prudente, SP, Brazil, his M.Sc. and his Ph.D. in Remote Sensing at the National Institute for Space Research (INPE), São José dos Campos, Brazil.

Since 2002, he has been working as a Researcher at INPE. His research interests include digital images processing, applied statistics, hydrological modeling and environmental studies related to the Amazon forest.

Raul Q. Feitosa received his B.Sc. in Electronic Engineering from the Aeronautics Technological Institute – ITA, in São José dos Campos, Brazil, and his Ph.D. in Computer Science from the Friedrich Alexander University Erlangen (FAUEN), Nürnberg, Germany.

Since March 1993, he has been working as a Professor at the Catholic University of Rio de Janeiro, Brazil. His research interests include digital images processing, high spatial resolution sensors, remote sensing, cognitive methods and computer visualization.


Egyptian pyramids found by infra-red satellite images

By Frances CroninBBC News

Modern day San El Hakkar and infrared image of ancient TanisThe infrared image on the right reveals the ancient city streets of Tanis near modern-day San El Hagar

Seventeen lost pyramids are among the buildings identified in a new satellite survey of Egypt.

More than 1,000 tombs and 3,000 ancient settlements were also revealed by looking at infra-red images which show up underground buildings.

Initial excavations have already confirmed some of the findings, including two suspected pyramids.

The work has been pioneered at the University of Alabama at Birmingham by US Egyptologist Dr Sarah Parcak.

satellite image of pyramidAn infra-red satellite image shows a buried pyramid, located in the centre of the highlight box.

She says she was amazed at how much she and her team has found.

“We were very intensely doing this research for over a year. I could see the data as it was emerging, but for me the “Aha!” moment was when I could step back and look at everything that we’d found and I couldn’t believe we could locate so many sites all over Egypt.

“To excavate a pyramid is the dream of every archaeologist,” she said.

The team analysed images from satellites orbiting 700km above the earth, equipped with cameras so powerful they can pin-point objects less than 1m in diameter on the earth’s surface.

Infra-red imaging was used to highlight different materials under the surface.

Test excavations

Ancient Egyptians built their houses and structures out of mud brick, which is much denser than the soil that surrounds it, so the shapes of houses, temples and tombs can be seen.

“It just shows us how easy it is to underestimate both the size and scale of past human settlements,” says Dr Parcak.

And she believes there are more antiquities to be discovered:

“These are just the sites [close to] the surface. There are many thousands of additional sites that the Nile has covered over with silt. This is just the beginning of this kind of work.”

BBC cameras followed Dr Parcak on her “nervous” journey when she travelled to Egypt to see if excavations could back up what her technology could see under the surface.

In the BBC documentary Egypt’s Lost Cities, they visit an area of Saqqara (Sakkara) where the authorities were not initially interested in her findings.

But after being told by Dr Parcak that she had seen two potential pyramids, they made test excavations, and they now believe it is one of the most important archaeological sites in Egypt.


An infra-red satellite image reveals the city of Tanis

But Dr Parcak said the most exciting moment was visiting the excavations at Tanis.

“They’d excavated a 3,000-year-old house that the satellite imagery had shown and the outline of the structure matched the satellite imagery almost perfectly. That was real validation of the technology.”

The Egyptian authorities plan to use the technology to help – among other things – protect the country’s antiquities in the future.

During the recent revolution, looters accessed some well-known archaeological sites.

Continue reading the main story

“Start Quote

Dr Sarah Parcak

Indiana Jones is old school, we’ve moved on from Indy, sorry Harrison Ford ”

Dr Sarah ParcakSpace Archaeologist

“We can tell from the imagery a tomb was looted from a particular period of time and we can alert Interpol to watch out for antiquities from that time that may be offered for sale.”

She also hopes the new technology will help engage young people in science and will be a major help for archaeologists around the world.

“It allows us to be more focused and selective in the work we do. Faced with a massive site, you don’t know where to start.

“It’s an important tool to focus where we’re excavating. It gives us a much bigger perspective on archaeological sites. We have to think bigger and that’s what the satellites allow us to do.”

“Indiana Jones is old school, we’ve moved on from Indy. Sorry, Harrison Ford.”

Egypt’s Lost Cities is on BBC One on Monday 30 May at 2030 BST. It will also be shown on the Discovery channel in the US.

APRS (Automatic Packet Reporting System) Untuk Penanggulangan Bencana

APRS (Automatic Packet Reporting System) Untuk Penanggulangan Bencana

By  On Oktober 14, 2013 · Leave a Comment · In Trainer Blog Posts

Ada yang pernah mendengar APRS? Bagi beberapa orang –termasuk saya, ini adalah pertama kalinya saya berkenalan APRS :) . Namun bagi teman-teman dari komunitas radio pasti sudah sangat mengenal APRS. APRS sendiri sebenarnya bukanlah teknologi baru mengingat APRS telah ada sejak 1980-an. Namun seiring dengan perkembangan zaman, APRS terus dikembangkan dan berinovasi, salah satunya dengan mengrintegrasikan APRS dengan data spasial. Bagaimana bisa? Ya, bisa, yaitu dengan menambahkan GPS (Global Positioning System) pada perangkat radio tim lapangan.

Macam Radio Komunikasi

Macam Radio Komunikasi

Apa Itu APRS ?

APRS adalah kepanjangan dari Automatic Packet Reporting System .  Sistem ini bekerja di lingkungan radio. Radio sendiri memiliki karakteristik bahwa komunikasi antar radio dapat dilakukan tanpa sinyal dan pulsa layaknya handphone, melainkan berdasarkan jangkauan frekuensi radio. Jadi, selama radio kita berada dalam jangkauan frekuensi radio lain, kita dapat berkomunikasi dengan sesama pengguna radio dan tentunya mengirimkan pesan ke operator/administrator. Hal ini menjadikan radio banyak digunakan pada wilayah-wilayah terpencil yang mana  seringkali tidak terjangkau oleh sinyal GSM ataupun CDMA. Karena alasan ini pula radio banyak digunakan dalam keadaan darurat, misalnya di suatu  wilayah yang tengah terjadi bencana, mengingat pada saat terjadi bencana terkadang beberapa infrastruktur seperti tower BTS ataupun jaringan listrik ikut terdampak bencana, sehingga komunikasi menjadi lumpuh :( .

Komponen inti dari APRS sendiri adalah suatu alat yang bernama TNC (Terminal Node Controler). Dengan menghubungkan perangkat radio kita dengan TNC dan GPS, maka kita telah membangun APRS kita sendiri.

Terminal Node Controller (TNC)

Terminal Node Controller (TNC)


Bagaimana APRS Dapat Menolong ?


1)      Sebagai Tracker 1 Arah

Tepat untuk digunakan oleh tim yang bertugas mencari data ataupun informasi di lapangan. Misalnya digunakan oleh pendaki gunung, tim SAR, tim pencari korban bencana. Jadi, setiap ada data baru yang diperoleh di lapangan, tim segera melaporkan data ke pos komando. Meskipun begitu, pergerakan tim lapangan tetap diketahui oleh koordinator tim lapangan/pos komando.
Untuk membangun APRS dengan fungsi sebagai tracker 1 arah, peralatan yang dibutuhkan adalah: (i) radio, (ii) TNC, dan (iii) GPS tanpa layar. Selanjutnya, agar lebih praktis, perangkat APRS tersebut dapat dimasukkan ke dalam ransel tim lapangan. Cukup ringan dan mudah dibawa :D .

APRS Sebagai Tracker 1 Arah

APRS Sebagai Tracker 1 Arah

2)      Sebagai Tracker 2 Arah

Pada tracker 2 arah, pengguna dapat mengetahui posisi pengguna APRS yang lain. Dengan demikian, APRS sebagai tracker 2 arah tepat untuk digunakan oleh koordinator lapangan pendaki gunung, tim SAR, tim medis bergerak, dan tentunya pos komando.
Untuk membangun APRS dengan fungsi sebagai tracker 2 arah, peralatan yang dibutuhkan adalah: (i) radio, (ii) TNC, dan (iii) GPS dengan layar.

APRS Sebagai Tracker 2 Arah

APRS Sebagai Tracker 2 Arah

3)      Sebagai Alat untuk Menerima/Mengirim Pesan Text

Seperti SMS, batasan maksimalnya 67 karakter setiap paket. Semua orang yg menerima sinyal, dapat membaca pesan. Uniknya, pesan ini dikirim tanpa pulsa dan sekali kita mengirim pesan maka pesan akan diterima oleh semua pengguna APRS dalam jangkauan frekuensi radio kita. Pesan apa yang dapat kita kirimkan? Banyak. Misalnya permintaan ambulans ataupun tim medis, serta informasi jumlah korban di suatu lokasi, beserta deskripsi luka dan obat yang diperlukan. Informasi lokasi tidak harus diinformasikan karena lokasi pelapor sudah terdetaksi oleh GPS, yang kemudian tervisualisasikan lokasinya di dalam GPS berlayar ataupun aplikasi SARTrack pada komputer. Dengan demikian, lokasi pelapor dapat diketahui secara pasti, dan tim penolong dapat menuju ke lokasi tersebut dengan mudah, walaupun bukan warga lokal.

Mengirim Pesan Singkat melalui APRS

Mengirim Pesan Singkat melalui APRS

Akan tetapi, fitur pengirimiman pesan ini hanya dapat dilakukan jika kita membangun APRS kita dengan radio yang memiliki keypad untuk menulis teks, yaitu tipe Kenwood Th – D7. Alternatif lain yaitu menggunakan handphone android (dengan aplikasi GPS yang aktif) yang kita hubungkan dengan TNC dan radio, dengan cara demikian handphone kita pun dapat berfungsi layaknya APRS.

Perangkat untuk Mengirim Pesan dengan APRS

Perangkat untuk Mengirim Pesan dengan APRS

4)      Sebagai Alat untuk Manajemen Informasi

Fungsi utamanya adalah berbagi dan update data secara cepat, mencakup lokasi/pergerakan para pengguna (misalnya tim lapangan)  beserta informasi/pesan yang dikirimkan tim lapangan ke pos komando . Hasilnya, kita akan mendapatkan siaran langsung yang menunjukkan update data terbaru secara real time. Siaran langsung ini menjadi lebih menarik dengan adanya integrasi data APRS dengan data spasial (peta) salah satunya peta OpenStreetMap.

Tampilan SARTrack dengan Background Peta OpenStreetMap (OSM)

Tampilan SARTrack dengan Background Peta OpenStreetMap (OSM)

Untuk menjalankan fungsi APRS sebagai alat untuk manajemen informasi ini,  dibutuhkan aplikasi lain, yaitu “Search and Rescue Tracking” (SARTrack). Aplikasi SARTrack  adalah aplikasi desktop (diinstal di komputer atau laptop) yang dirancang  untuk mendukung sistem APRS. Untuk menjalankan fungsinya itu, SARTrack dapat terhubung ke server APRS dan server-server peta online salah satunya OpenStreetMap. Untuk mendapatkan aplikasi yang merupakan opensource ini, silakan download di sini.

SARTrack wajib diinstal pada pos komando, mengingat selain harus memonitor pergerakan tim lapangan, pos komando juga bertanggung jawab untuk mengolah pesan yang dikirimkan tim lapangan ke posko, untuk selanjutnya  mengambil tindakan dan mengirimkan bantuan ke lokasi tersebut. Untuk menjalankan fungsi tersebut, peralatan yang dibutuhkan pada pos komando yaitu: (i) komputer/laptop yang sudah diinstal SARTrack, (ii) TNC, dan (iii) radio.

Peralatan untuk Membangun APRS pada Pos Komando

Peralatan untuk Membangun APRS pada Pos Komando

Dengan ketiga alat tersebut, pos komando dapat menerima informasi dari tim lapangan yang mana berkomunikasi dengan APRS. Selanjutnya, jika pos komando ingin menyebarluaskan informasi tersebut kepada pos komando yang lebih tinggi (misalnya BPBD Provinsi) ataupun posko LSM dan relawan yang lain, maka pos komando haruslah terhubung ke akses internet –agar data yang ada dapat tersimpan di server APRS :) .

Alur Komunikasi Lapangan dan Pos Komando dengan APRS dan Internet

Alur Komunikasi Lapangan dan Pos Komando dengan APRS dan Internet

Pos komando dapat berada di kantor BPBD (Badan Penanggulangan Bencana Daerah). Namun jika lokasi kantor BPBD terlalu jauh dari lokasi bencana –sehingga dapat mengakibatkan tidak terjangkaunya frekuensi radio, pos komando juga dapat bersifat semi mobile, misalnya  menggunakan mobil komunikasi satelit.

Mobil Komunikasi Satelit BPBD Jawa Timur, Bantuan dari BNPB

Mobil Komunikasi Satelit BPBD Jawa Timur, Bantuan dari BNPB


Beberapa Pertanyaan yang Sering Muncul


Apakah APRS akan mengganggu fungsi utama radio untuk saling berkomunikasi dengan suara?

Tidak. Selama pada pada radio tersebut terdapat cukup slot untuk mikrofon (karena beberapa slot akan pada radio akan digunakan untuk menghubungkan radio ke TNC dan GPS).

Seberapa jauh jangkauan komunikasi APRS?

Sejauh jangkauan frekuensi radio. Jika jangkauan radio Anda kecil dan Anda ingin meningkatkan jangkauan radio Anda, Anda dapat mengganti antena radio Anda dengan spesifikasi yang lebih baik dan membangun pemancar pada titik yang rawan tidak berada dalam jangakauan radio

TNC merupakan inti dari APRS. Berapakah harga TNC dan di mana bisa mendapatkannya? TNC belum dijual bebas di Indonesia, dan unuk membelinya kita harus membelinya dari agen di luar negeri (membeli secara online, atau jika dalam jumlah banyak bisa melalui skema pengadaan/tender). Harga TNC kurang lebih $65 (Rp 650.000,-). Ada pula TNC dengan GPS $130 (Rp 1.300.000,-). Info lebih lanjut silakan klikdi sini.


APRS dapat menjadi sarana komunikasi alternatif di daerah bencana, terutama jika sarana komunikasi yang ada lumpuh ataupun ketiadaan sinyal GSM dan CDMA. Sasaran yang ingin dicapai dari penggunaan APRS dalam bidang kebencanaan ini adalah efektivitas dalam rangka menolong orang lain. Sebisa mungkin menyampaikan informasi secara cepat, akurat, dan dapat dikelola dengan baik :) .

Dirangkum dari materi wokshop “Penggunaan APRS di Bidang Penanggulangan Bencana/Kedaruratan” yang diselenggarakan oleh LSM Amarylis, Jakarta Barat, 27 Agustus 2013.



Apa itu open source?

Istilah “open source” mengacu pada sesuatu yang dapat dimodifikasi karena desainnya yang dapat diakses publik.

Sementara itu berasal dalam konteks pengembangan perangkat lunak komputer, saat ini istilah “open source” menunjuk seperangkat nilai-apa yang kita sebut “jalan open source . “Secara umum, proyek-proyek open source, produk, atau inisiatif adalah mereka yang merangkul dan merayakan pertukaran terbuka, partisipasi kolaboratif, prototyping cepat, transparansi, meritokrasi, dan pengembangan masyarakat.

Apakah perangkat lunak open source?

Perangkat lunak open source adalah perangkat lunak yang kode sumber tersedia untuk modifikasi atau tambahan oleh siapapun.

“Source code” adalah bagian dari perangkat lunak yang kebanyakan pengguna komputer tidak pernah melihat, itu adalah pemrogram komputer kode yang dapat digunakan untuk mengubah bagaimana sepotong karya perangkat lunak. Programmer yang memiliki akses ke kode sumber program komputer dapat meningkatkan program yang dengan menambahkan fitur untuk itu atau memperbaiki bagian-bagian yang tidak selalu bekerja dengan benar.

Apa perbedaan antara perangkat lunak open source dan jenis lain dari perangkat lunak?

Beberapa perangkat lunak memiliki kode sumber yang tidak dapat diubah oleh siapa pun kecuali orang, tim, atau organisasi yang menciptakan dan mempertahankan kontrol eksklusif atas hal itu. Jenis perangkat lunak ini sering disebut “perangkat lunak berpemilik” atau “closed source” perangkat lunak, karena kode sumbernya adalah milik penulis aslinya, yang adalah satu-satunya hukum diperbolehkan untuk menyalin atau memodifikasinya. Microsoft Word dan Adobe Photoshop adalah contoh dari perangkat lunak berpemilik. Untuk menggunakan perangkat lunak berpemilik, pengguna komputer harus setuju (biasanya dengan menandatangani lisensi ditampilkan pertama kalinya mereka menjalankan software ini) bahwa mereka tidak akan melakukan apa-apa dengan perangkat lunak yang penulis perangkat lunak belum diizinkan secara tegas.

Perangkat lunak open source berbeda. Its penulis membuat kode sumber tersedia bagi orang lain yang ingin melihat kode itu, menyalinnya, belajar dari itu, mengubahnya, atau berbagi.LibreOffice dan GNU Image Manipulation Program adalah contoh dari perangkat lunak open source. Seperti yang mereka lakukan dengan perangkat lunak berpemilik, pengguna harus menerima persyaratan dari lisensi ketika mereka menggunakan open source software-tetapi istilah hukum lisensi open source sangat berbeda dengan lisensi proprietary. Lisensi perangkat lunak open source mempromosikan kolaborasi dan berbagi karena mereka memungkinkan orang lain untuk membuat modifikasi kode sumber dan memasukkan kode tersebut ke proyek-proyek mereka sendiri. Beberapa lisensi open source memastikan bahwa siapa pun yang mengubah dan kemudian saham program dengan orang lain harus juga berbagi kode sumber program tersebut tanpa memungut biaya lisensi untuk itu. Dengan kata lain, pemrogram komputer dapat mengakses, melihat, dan memodifikasi perangkat lunak open source kapan pun mereka suka-selama mereka membiarkan orang lain melakukan hal yang sama ketika mereka berbagi pekerjaan mereka. Bahkan, mereka bisa melanggar ketentuan beberapa lisensi open source jika mereka tidak melakukan hal ini.

Jadi sebagai Open Source Initiative menjelaskan, “open source tidak hanya berarti akses ke kode sumber.” Ini berarti bahwa setiap orang harus dapat memodifikasi kode sumber untuk memenuhi kebutuhan nya, dan tidak dapat mencegah orang lain melakukan hal yang sama.Definisi Initiative “open source” berisi beberapa lainnya ketentuan penting .

Mengapa orang lebih suka menggunakan perangkat lunak open source?

Banyak orang lebih memilih software open source karena mereka memiliki kontrol lebih atas bahwa jenis perangkat lunak. Mereka dapat memeriksa kode untuk memastikan itu tidak melakukan sesuatu yang mereka tidak ingin lakukan, dan mereka dapat mengubah bagian itu mereka tidak suka. Pengguna yang bukan programer juga mendapat manfaat dari perangkat lunak open source, karena mereka dapat menggunakan software ini untuk tujuan apapun yang mereka inginkan-bukan hanya cara orang lain berpikir mereka seharusnya.

Lainnya seperti perangkat lunak open source karena membantu mereka menjadi programmer yang lebih baik . Mereka dapat belajar untuk membuat perangkat lunak yang lebih baik dengan mempelajari kode sumber yang telah ditulis orang. Mereka juga dapat berbagi pekerjaan mereka dengan orang lain, komentar dan kritik mengundang.

Sebagian orang memilih perangkat lunak open source karena mereka menganggap lebihaman dan stabil dari perangkat lunak berpemilik. Karena siapa pun dapat melihat dan memodifikasi perangkat lunak open source, seseorang mungkin spot dan memperbaiki kesalahan atau kelalaian yang penulis asli sebuah program mungkin telah terjawab. Dan karena begitu banyak programmer dapat bekerja pada sebuah software open source tanpa meminta izin dari penulis aslinya, perangkat lunak open source umumnya tetap, diperbarui, dan ditingkatkan dengan cepat .

Banyak pengguna lebih memilih software open source untuk perangkat lunak berpemilik untuk penting, proyek jangka panjang. Karena source code untuk perangkat lunak open source yang didistribusikan secara terbuka , pengguna yang mengandalkan perangkat lunak untuk tugas-tugas penting dapat yakin alat-alat mereka tidak akan hilang atau jatuh ke dalam rusak jika pencipta asli mereka berhenti bekerja pada mereka.

Tidak “open source” hanya berarti sesuatu yang gratis?

No Ini adalah kesalahpahaman umum tentang apa yang “open source” berarti. Pemrogram dapat mengisi uang untuk perangkat lunak open source yang mereka buat atau yang mereka berkontribusi. Tetapi karena lisensi sumber paling terbuka mengharuskan mereka untuk merilis kode sumber mereka ketika mereka menjual perangkat lunak kepada orang lain, banyak sumber programmer perangkat lunak open merasa lebih menguntungkan untuk membebankan pengguna untuk layanan perangkat lunak dan dukungan bukan untuk perangkat lunak itu sendiri. Dengan cara ini, perangkat lunak mereka tetap gratis dan mereka membuat uang membantu orang lain menginstal, menggunakan, dan memecahkan masalah itu.

Apa yang open source “di luar software”?

Pada, kami ingin mengatakan bahwa kami tertarik pada cara open source dapat diterapkan ke dunia luar software . Kami suka berpikir tentang open source tidak hanya sebagai cara untuk mengembangkan dan lisensi perangkat lunak komputer, tetapi juga sikap .Mendekati semua aspek kehidupan “cara open source” berarti mengekspresikan kemauan untuk berbagi, berkolaborasi dengan orang lain dengan cara yang transparan (sehingga orang lain dapat melihat dan bergabung juga), merangkul kegagalan sebagai sarana untuk memperbaiki, dan mengharapkan-bahkan mendorong -orang lain untuk melakukan hal yang sama.

Ini berarti berkomitmen untuk memainkan peran aktif dalam meningkatkan dunia, yang hanya mungkin bila setiap orang memiliki akses ke cara agar dunia dirancang. Dunia ini penuh dengan “kode sumber” – cetak biru , resep , aturan -yang menuntun dan membentuk cara kita berpikir dan bertindak di dalamnya. Kami percaya kode ini mendasari (apapun bentuknya) harus terbuka, mudah diakses, dan shared-begitu banyak orang dapat memiliki tangan dalam mengubah menjadi lebih baik.

Di sini, kami menceritakan kisah tentang apa yang terjadi ketika nilai-nilai open source yang diterapkan untuk bisnis, pendidikan, pemerintahan, kesehatan, hukum, dan area lain dari kehidupan. Kami adalah komunitas berkomitmen untuk memberitahu orang lain bagaimana cara open source adalah yang terbaik cara-karena cinta open source seperti hal lain: lebih baik ketika itu bersama.

Dimana saya dapat mempelajari lebih lanjut tentang open source?

Kami merekomendasikan mengunjungi sumber daya halaman .


Here’s Where You’re Most Likely to Die From Air Pollution

Where on earth are you most likely to die early from air pollution? NASA provides the answer with this mortally serious view of the planet, and it is: lots of places.

Like tar stains on a healthy lung, the sickly yellow and brown areas in this visualizationrepresent regions with significant numbers of pollutant-influenced deaths. Heavily urbanized places in eastern China, India, Indonesia, and Europe are stippled by the darkest colors of snuff, meaning they experience rates of ruination as high as 1,000 deaths per 1,000 square kilometers*each year.

In good news, areas painted in blue show where humanity has managed to lower its output of choking smog since the 1850s. These safer havens include spots in the middle of South America and the southeastern United States, where the amount of agricultural burning has decreased since the mid-19th century.

This representation of our befouled atmosphere is based on the work of Jason West, an earth scientist at the University of North Carolina who’s investigating the health effects of bad air. According to computer models that West and his team constructed, an incredible 2.1 million deaths a year can be attributed to one type of pollution alone – fine particulate matter, or PM2.5, which are teensy specks that fly out of car-exhaust pipes, industrial smokestacks and other things. (They’re also what the NASA map is referencing.)

The medical community has linked breathing PM2.5 with afflictions from asthma to lung disease to heart attacks. It’s obviously bad to be in the middle of a toxic particle cloud caused by some intense human activity, as Singaporeans were this summer thanks to land-clearing firesset by their neighbors in Sumatra. But that’s not the only way that PM2.5 can getcha, says NASA:

In most cases, the most toxic pollution lingers for a few days or even weeks, bringing increases in respiratory and cardiac health problems at hospitals. Eventually the weather breaks, the air clears, and memories of foul air begin to fade. But that’s not to say that the health risks disappear as well. Even slightly elevated levels of air pollution can have a significant effect on human health. Over long periods and on a global scale, such impacts can add up.

Nowhere is that more obvious than in China and northern India, where dense bands of premature deaths pop out. You can practically hear the wheezing from space:

* Not “1,000 deaths per square kilometer,” as originally stated, thank god. Map made by NASA’s Robert Simmon based on data from Jason West