FOSS4G 2013 Keynote: Kate Chapman

October 22, 2013 by syafraufgisqu

October 19, 2013 by syafraufgisqu

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

BY BETSY MASON
10.07.13
12:33 PM

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.

Maps That Inspired This Post

- Brilliant Maps Reveal Age of the World’s Buildings

So I called up Thomas Rhiel of the independent journalism site BKLYNR.com. 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 ArcGIS. I’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 data. I’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 {
line-color:#594;
line-width:0.5;
polygon-opacity:1;
polygon-fill:#ae8;
}

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 {
line-color:#615e5e;
line-width:1;
polygon-opacity:1;
}

#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: http://a.tiles.mapbox.com/v3/wiredmaplab.map-ku6szhel/page.html

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:

One that has your buildings layer placed on top of a terrain/street layer, but with NO labels.

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: http://www.mapbox.com/mapbox.js/example/v1.0.0/layers/

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!

October 19, 2013 by syafraufgisqu

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

By Almeida et al. , posted on August 16th, 2011 in Articles, Earth Observation, Urban Monitoring

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.

I. INTRODUCTION

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.

II. POPULATION ESTIMATES

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.

III. STUDY AREA

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.

IV. CONCEPTS AND METHODS

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 (x_ij, y_ij) to normalized latitude, longitude and ellipsoidal height (ø, λ, H). The RPC model is given as [10]:

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

P(ø, λ, H) = C₁ + C₂. λ+ C₃. ø + C₄. H + C₅. λ.ø

+ C₆. λ, H + C₇. ø. H + C₈. λ² + C₉. ø²+ C₁₀. H²

+ C11. ø. λ. H + C₁₂. λ³ + C₁₃. λ .ø² + C₁₄. λ. H² (3)

+ C₁₅. λ². ø + C₁₆. ø³ + C₁₇. ø. H² + C₁₈. λ². H

+ C₁₉. ø². H + C₂₀. H³

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

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.

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: μ_R – μ_E = 0) states that there are no significant difference between the means of the reference (μ_R) and the estimated data set (μ_E), and hypothesis one (H₁: μ_R – μ_E ≠ 0) states the opposite.

V. RESULTS

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.

The 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.

VI. DISCUSSION

The statistical tests showed a very good Pearson correlation coefficient (ρ) and a fairly good coefficient of determination (R₂). Most importantly, the p_value 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.

VII. CONCLUSION

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.

REFERENCES

[1] United Nations Habitat. (2006). State of the World´s Cities 2006/7. [Online]. Available:www.unhabitat.org/pmss/getElectronicVersion.asp?nr=2101&alt=1

[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.

October 19, 2013 by syafraufgisqu

Egyptian pyramids found by infra-red satellite images

By Frances CroninBBC News

Modern day San El Hakkar and infrared image of ancient Tanis

The infrared image on the right reveals the ancient city streets of Tanis near modern-day San El Hagar

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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.

Syafraufgisqu.wordpress.com

Monthly Archives: October 2013

FOSS4G 2013 Keynote: Kate Chapman

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

Step 1: Find Some Data

Step 2: Choose a Mapping Platform

Step 3: Get Some Free Mapmaking Software

Step 4: Tame the Data

Step 5: Design Your Map in TileMill

Step 6: Add the Final Touches in MapBox

Step 7: Publish Your Map

Step 8: Help Me Fix My Map

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

Egyptian pyramids found by infra-red satellite images

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