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A Nationwide Drive Time Matrix Between U.S. ZIP Code Areas: Conclusion, Acknowledgement & References

Authors:
(1) Yujie Hu, Department of Geography, University of Florida, Gainesville, FL 32611 and UF Informatics Institute, University of Florida, Gainesville, FL 32611;
(2) Changzhen Wang, Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803;
(3) Ruiyang Li, Children’s Environmental Health Initiative, Rice University, Houston, TX 77005;
(4)Fahui Wang, Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803. Table of Links Abstract and 1 Introduction. Methodology Results Concluding comments, Acknowledgement and References Results Based on the 2010 block population data, population weighted centroids for the 32,840 ZIP codes in the U.S are created. Together with the two network datasets of different road levels, they are fed into Algorithm 1. This process results in a massive OD cost matrix of 1,078,432,760 records, each of which consists of estimation of drive time and distance for a ZIP code pair. It takes about 76 hours for Algorithm 1 to compute and export the national ZIP-to-ZIP time matrix, and the breakdowns are: Level 1 consumes 6 hours, Level 2 60 hours, and Level 3 10 hours. Algorithm 2 is implemented on a randomly-sampled subset of OD pairs derived from Algorithm 1. Given the free usage constraints discussed previously, Algorithm 2 is run to collect Google drive times over a period of four months. It finally gathers 124,350 (nsample) valid ZIP-to-ZIP trips, which are associated with 32,478 unique ZIP codes (about 99% of the national 32,840 ZIP codes). Each trip includes Google’s estimates of drive time and distance. Table 3 lists the frequency distribution of OD trips derived from Algorithms 1 and 2. The sampling intensity for the short-range trips (Level 1) is about triple those medium-range trips (Level 2) and quadruple those long-range trips (Level 3). Oversampling shorter trips is to enhance their representation and ensure the quality of subsequent interpolation for greater interests in acquiring shorter-range travel times. Figure 3 shows sampled ZIP code pairs of Level 1. Figures 4A, 4C, and 4E (and 4B, 4D, 4F) plot drive times (distances) estimated by Algorithm 1 against times (distances) by Algorithm 2 at Level 1, 2 and 3, respectively. Only the data points within 24 hours (1,200 miles, or 1,931 km) for Level 3 are shown. Clearly, the pattern is largely consistent between the two algorithms for ZIP code pairs across the three levels. A few observations merit discussion. It is obvious in Figures 4A and 4C that Algorithm 1 tends to underestimate travel times. One likely cause for this trend is the omission of congestion in Algorithm 1. Interestingly, such a trend of downward estimates is also observed in distance measurements in Figures 4B and 4F. This is because the distances returned by Algorithm 2 are simply the lengths in mileage of those quickest routes in terms of drive times, which are commonly through highways. Therefore, distances measured by Algorithm 1—the “shortest” routes in terms of mileage—are consistently lower than the values estimated by Algorithm 2. The exact fitting power of each needs to be examined by regression. Corresponding to each of the three levels in Algorithm 1, a regression is run to infer the relationship between Google drive times by Algorithm 2 (dependent variable) and Algorithm 1 drive times (independent variable). Results are summarized in Table 4. It shows that Algorithm 1 times explain the variation in the matched Google times by 91 percent at Level 1, 93 percent at Level 2, and 96 percent at Level 3. Table 4 also reports the results for regression models on distance measures from the two algorithms, with the R2 = 0.95, 0.93, and 0.99 for Levels 1, 2 and 3, respectively. The higher fitting powers by the travel distance models than the drive time ones are due to the uncertainty in traffic congestion effect on drive time measurement. As travel distances are not used as often as drive times in measuring spatial impedance, our discussion focused on drive time Note: Ta1 represents Algorithm 1 derived drive times and distances, and N denotes the number of observations. ***Significant at the 0.001 level. area Originally designed by the United States Postal Service (USPS), ZIP code is a fivedigit number corresponding to address points in the U.S., and such data have never been released by the USPS for public access. It is, therefore, not an areal unit where a ZIP code has a specified physical boundary. For the sake of spatial analysis, many entities have attempted to delineate ZIP code areas, such as the ZCTAs by the U.S. Census (Khan et al., 2019). Our recent experience in using this drive time matrix indicates that some USPS ZIP codes are not included in the ZCTAs data. Most of those missing ZIP codes have no associated area such as post office box ZIP codes and single site ZIP codes (government, building, or large volume customer). One may use the following algorithm to interpolate the drive times on corresponding missing OD pairs: For drive time between a missing ZIP code (say, origin) and a known ZIP code (say, destination): identify the 3 nearest known ZIP codes from the missing ZIP code, locate the 3 drive times between each of the 3 nearby known ZIP codes (origin) and the known ZIP (destination) from the provided matrix, and use their average drive time as the one between the missing ZIP code and the known ZIP code. For drive time between two missing ZIP codes: identify the 3 nearest known ZIP codes from the origin ZIP code and also the 3 nearest known ZIP codes from the destination ZIP code, locate the corresponding 9 drive times between each of the 3 nearby origins and each of the 3 nearby destinations from the provided OD cost matrix, and use their average drive time as the one between the two missing ZIP codes. The above proposition assumes that the location of a missing ZIP code can be approximated by the average of its three nearest ZIP codes. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Yujie Hu, Department of Geography, University of Florida, Gainesville, FL 32611 and UF Informatics Institute, University of Florida, Gainesville, FL 32611; (2) Changzhen Wang, Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803; (3) Ruiyang Li, Children’s Environmental Health Initiative, Rice University, Houston, TX 77005; (4)Fahui Wang, Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803. Authors: Authors: (1) Yujie Hu, Department of Geography, University of Florida, Gainesville, FL 32611 and UF Informatics Institute, University of Florida, Gainesville, FL 32611; (2) Changzhen Wang, Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803; (3) Ruiyang Li, Children’s Environmental Health Initiative, Rice University, Houston, TX 77005; (4)Fahui Wang, Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803. Table of Links Abstract and 1 Introduction. Abstract and 1 Introduction. Methodology Methodology Results Results Concluding comments, Acknowledgement and References Concluding comments, Acknowledgement and References Results Based on the 2010 block population data, population weighted centroids for the 32,840 ZIP codes in the U.S are created. Together with the two network datasets of different road levels, they are fed into Algorithm 1. This process results in a massive OD cost matrix of 1,078,432,760 records, each of which consists of estimation of drive time and distance for a ZIP code pair. It takes about 76 hours for Algorithm 1 to compute and export the national ZIP-to-ZIP time matrix, and the breakdowns are: Level 1 consumes 6 hours, Level 2 60 hours, and Level 3 10 hours. Algorithm 2 is implemented on a randomly-sampled subset of OD pairs derived from Algorithm 1. Given the free usage constraints discussed previously, Algorithm 2 is run to collect Google drive times over a period of four months. It finally gathers 124,350 (nsample) valid ZIP-to-ZIP trips, which are associated with 32,478 unique ZIP codes (about 99% of the national 32,840 ZIP codes). Each trip includes Google’s estimates of drive time and distance. Table 3 lists the frequency distribution of OD trips derived from Algorithms 1 and 2. The sampling intensity for the short-range trips (Level 1) is about triple those medium-range trips (Level 2) and quadruple those long-range trips (Level 3). Oversampling shorter trips is to enhance their representation and ensure the quality of subsequent interpolation for greater interests in acquiring shorter-range travel times. Figure 3 shows sampled ZIP code pairs of Level 1. Figures 4A, 4C, and 4E (and 4B, 4D, 4F) plot drive times (distances) estimated by Algorithm 1 against times (distances) by Algorithm 2 at Level 1, 2 and 3, respectively. Only the data points within 24 hours (1,200 miles, or 1,931 km) for Level 3 are shown. Clearly, the pattern is largely consistent between the two algorithms for ZIP code pairs across the three levels. A few observations merit discussion. It is obvious in Figures 4A and 4C that Algorithm 1 tends to underestimate travel times. One likely cause for this trend is the omission of congestion in Algorithm 1. Interestingly, such a trend of downward estimates is also observed in distance measurements in Figures 4B and 4F. This is because the distances returned by Algorithm 2 are simply the lengths in mileage of those quickest routes in terms of drive times, which are commonly through highways. Therefore, distances measured by Algorithm 1—the “shortest” routes in terms of mileage—are consistently lower than the values estimated by Algorithm 2. The exact fitting power of each needs to be examined by regression. Corresponding to each of the three levels in Algorithm 1, a regression is run to infer the relationship between Google drive times by Algorithm 2 (dependent variable) and Algorithm 1 drive times (independent variable). Results are summarized in Table 4. It shows that Algorithm 1 times explain the variation in the matched Google times by 91 percent at Level 1, 93 percent at Level 2, and 96 percent at Level 3. Table 4 also reports the results for regression models on distance measures from the two algorithms, with the R2 = 0.95, 0.93, and 0.99 for Levels 1, 2 and 3, respectively. The higher fitting powers by the travel distance models than the drive time ones are due to the uncertainty in traffic congestion effect on drive time measurement. As travel distances are not used as often as drive times in measuring spatial impedance, our discussion focused on drive time Note: Ta1 represents Algorithm 1 derived drive times and distances, and N denotes the number of observations. ***Significant at the 0.001 level. area Originally designed by the United States Postal Service (USPS), ZIP code is a fivedigit number corresponding to address points in the U.S., and such data have never been released by the USPS for public access. It is, therefore, not an areal unit where a ZIP code has a specified physical boundary. For the sake of spatial analysis, many entities have attempted to delineate ZIP code areas, such as the ZCTAs by the U.S. Census (Khan et al., 2019). Our recent experience in using this drive time matrix indicates that some USPS ZIP codes are not included in the ZCTAs data. Most of those missing ZIP codes have no associated area such as post office box ZIP codes and single site ZIP codes (government, building, or large volume customer). One may use the following algorithm to interpolate the drive times on corresponding missing OD pairs: For drive time between a missing ZIP code (say, origin) and a known ZIP code (say, destination): identify the 3 nearest known ZIP codes from the missing ZIP code, locate the 3 drive times between each of the 3 nearby known ZIP codes (origin) and the known ZIP (destination) from the provided matrix, and use their average drive time as the one between the missing ZIP code and the known ZIP code. For drive time between a missing ZIP code (say, origin) and a known ZIP code (say, destination): identify the 3 nearest known ZIP codes from the missing ZIP code, locate the 3 drive times between each of the 3 nearby known ZIP codes (origin) and the known ZIP (destination) from the provided matrix, and use their average drive time as the one between the missing ZIP code and the known ZIP code. For drive time between two missing ZIP codes: identify the 3 nearest known ZIP codes from the origin ZIP code and also the 3 nearest known ZIP codes from the destination ZIP code, locate the corresponding 9 drive times between each of the 3 nearby origins and each of the 3 nearby destinations from the provided OD cost matrix, and use their average drive time as the one between the two missing ZIP codes. For drive time between two missing ZIP codes: identify the 3 nearest known ZIP codes from the origin ZIP code and also the 3 nearest known ZIP codes from the destination ZIP code, locate the corresponding 9 drive times between each of the 3 nearby origins and each of the 3 nearby destinations from the provided OD cost matrix, and use their average drive time as the one between the two missing ZIP codes. The above proposition assumes that the location of a missing ZIP code can be approximated by the average of its three nearest ZIP codes. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

A Nationwide Drive Time Matrix Between U.S. ZIP Code Areas: Results

A Nationwide Drive Time Matrix Between U.S. ZIP Code Areas: Methodology

Authors:
(1) Yujie Hu, Department of Geography, University of Florida, Gainesville, FL 32611 and UF Informatics Institute, University of Florida, Gainesville, FL 32611;
(2) Changzhen Wang, Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803;
(3) Ruiyang Li, Children’s Environmental Health Initiative, Rice University, Houston, TX 77005;
(4)Fahui Wang, Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803. Table of Links Abstract and 1 Introduction. Methodology Results Concluding comments, Acknowledgement and References Abstract Estimating a massive drive time matrix between locations is a practical but challenging task. The challenges include availability of reliable road network (including traffic) data, programming expertise, and access to high-performance computing resources. This research proposes a method for estimating a nationwide drive time matrix between ZIP code areas in the U.S.—a geographic unit at which many national datasets such as health information are compiled and distributed. The method (1) does not rely on intensive efforts in data preparation or access to advanced computing resources, (2) uses algorithms of varying complexity and computational time to estimate drive times of different trip lengths, and (3) accounts for both interzonal and intrazonal drive times. The core design samples ZIP code pairs with various intensities according to trip lengths and derives the drive times via Google Maps API, and the Google times are then used to adjust and improve some primitive estimates of drive times with low computational costs. The result provides a valuable resource for researchers. Introduction Estimating a drive time matrix between locations is a critical task in spatial analysis, commonly encountered by researchers in geography, urban planning, transportation engineering, business management, and operational research, etc. To list a few, analytical models such as spatial interaction modeling (Simini et al., 2012), travel demand estimation (McFadden, 1974), location-allocation problems (ReVelle and Swain, 1970; Hu et al., 2019), spatial accessibility measures (Luo and Wang, 2003; Dony et al., 2015; Balomenos et al., 2019; Zhu et al., 2020), and delineation of health care market areas (Wang et al. 2020), rely on attainment of reliable drive time estimation from a set of origin locations to a set of destination locations. Such a task for a small-size matrix has become routine in many GIS and transportation packages, such as ESRI® ArcGIS and Caliper® TransCAD. However, it can be a challenging task for a large matrix. Most studies in this scope focus on small areas like individual cities or counties. Estimating the time matrix at larger geographic scales, such as a national scope, is of increasingly great importance to researchers and policy makers. Take the U.S. health care as an example, a national travel time matrix is the most critical component to study the average geographic access to health care (Onega et al., 2008; Boscoe et al., 2012), measure the variations in geographic access across regions (Onega et al., 2017), identify the areas where the health care geographic access is significantly lower than average, suggest the most appropriate sites for new care facilities, and facilitate the implementation of other strategies for reducing health care disparities such as remote health care, health care on wheels, and transit to care. The challenges for calibrating a large drive time matrix include availability of reliable road network (including traffic) data, programming expertise, and computational power. Many studies assume a free flow condition on roads to eliminate the data requirement on traffic, and are often limited to estimation of drive times from areas (e.g., ZIP code area, census tract) to the nearest locations (Onega et al., 2008; 2017; Boscoe et al., 2012; Ikram et al., 2015) or between locations within a short range (Shi et al., 2012) with a significantly reduced number of OD (origin-destination) pairs. Most recently, Saxon and Snow (2019) estimated a drive time matrix from each census tract to each primary care location within 62 miles (100 km) in the U.S. by tapping into advanced computing resources such as distributed computing and sophisticated algorithms, which may not be accessible by most researchers. They did not consider traffic conditions or node impedance, and the result tended to underestimate drive times especially for short trips. One way to account for traffic effect in drive time estimation relies on the utilization of traffic sensors or auxiliary data sources. These sensors include loop detectors (Kwon et al., 2000; Coifman, 2002) and automatic vehicle identification systems—such as toll collection system (El Faouzi et al., 2009), license plate recognition system (van Hinsbergen et al., 2009), and Bluetooth-based system (Bhaskar and Chung, 2013)—that are installed at certain locations along the road. They can accurately capture travel speeds and times. Other sensors are rather flexible, such as the floating or probe vehicles that consist of a sample of vehicles equipped with GPS units running with traffic (De Fabritiis et al., 2008). Based on collected information on a vehicle’s location, direction, and speed in a short time interval, drive times between any two locations in a network can be readily attained (Semanjski, 2015). A few recent studies attempted to estimate drive times using big data. Toole et al. (2015) used call detail records (CDRs) to obtain drive times for all road segments in five selected cities worldwide. Woodard et al. (2017) derived drive times on all roadways in the Seattle metropolitan region based on collected mobile phone GPS data. Although these methods can provide highly accurate estimates on actual drive times in traffic, their reliance on installation of physical equipment or big crowdsourced data restricts their usage to only small areas ranging from major corridors to metro regions. An alternative approach is to use third-party web mapping services such as Google Maps and MapQuest. For example, Google Maps Distance Matrix API uses the Google data such as its road network and collected traffic information to estimate drive times between a set of origins and a set of destinations. Similarly, MapQuest’s Route Matrix API uses open-source mapping data from the OpenStreetMap project to achieve this goal. Another benefit of using these commercial web services is to relieve analysts of the burden of preparing street network data and accessing GIS/transportation software (Boscoe et al., 2012). However, the free usage of these services comes with request limits. For instance, Google Maps offers free usage up to 40,000 OD records per month (Hu and Downs, 2019). A similar restriction applies to MapQuest. As a result, researchers usually use this approach to derive drive times for a limited number of OD pairs (Wang and Xu, 2011). Another issue related to drive time estimation between areas is the so-called aggregation error (Hu and Wang, 2016). The centroid-to-centroid approach assumes that all people in an area live at the centroid of an area (Hewko et al., 2002), and inevitably overlooks intrazonal travels (Kordi et al., 2012; Bhatta and Larsen, 2011). For example, the average commuting time within a traffic analysis zone (TAZ) is 11.3 minutes for auto drivers in Cleveland, Ohio (Wang, 2003). Given the average area of 82.25 square miles for the ZIP code areas in the U.S., intrazonal drive times at the ZIP code area level can be significant, especially in low-density suburban or rural areas. Its omission accounts for a high percentage in error for short-range trips. A common approach approximates intrazonal travel distance as the radius of an area-equivalent circle (Frost et al., 1998, Horner and Murray, 2002; Hu and Wang, 2015). Some recent studies use Monte Carlo simulation techniques to improve the estimation of trip lengths between area units (Hu and Wang, 2016; Hu et al. 2017), and offers a viable solution to intrazonal drive time estimation. This study seeks to estimate a very large drive time matrix between ZIP code areas in the U.S. ZIP code area is a popular geographic unit used in many nationwide datasets. For instance, ZIP code area is often the finest geographic scale at which health information is compiled and distributed in the U.S. (Berke and Shi, 2009). Our method (1) does not rely on intensive efforts in data preparation or access to advanced computing resources, (2) uses algorithms of varying complexity and computational time to estimate drive times of different trip lengths, and (3) accounts for both interzonal and intrazonal drive times. Both the program and results will be available for free download, and provide a valuable resource for researchers. This paper is available on arxiv under CC BY 4.0 DEED license. [1T] his is a preprint of: Hu, Y., Wang, C., Li, R., & Wang, F. (2020). Estimating a large drive time matrix between ZIP codes in the United States: A differential sampling approach. Journal of Transport Geography, 86, 102770. https://doi.org/10.1016/j.jtrangeo.2020.102770 Authors: (1) Yujie Hu, Department of Geography, University of Florida, Gainesville, FL 32611 and UF Informatics Institute, University of Florida, Gainesville, FL 32611; (2) Changzhen Wang, Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803; (3) Ruiyang Li, Children’s Environmental Health Initiative, Rice University, Houston, TX 77005; (4)Fahui Wang, Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803. Authors: Authors: (1) Yujie Hu, Department of Geography, University of Florida, Gainesville, FL 32611 and UF Informatics Institute, University of Florida, Gainesville, FL 32611; (2) Changzhen Wang, Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803; (3) Ruiyang Li, Children’s Environmental Health Initiative, Rice University, Houston, TX 77005; (4)Fahui Wang, Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803. Table of Links Abstract and 1 Introduction. Abstract and 1 Introduction. Methodology Methodology Results Results Concluding comments, Acknowledgement and References Concluding comments, Acknowledgement and References Abstract Estimating a massive drive time matrix between locations is a practical but challenging task. The challenges include availability of reliable road network (including traffic) data, programming expertise, and access to high-performance computing resources. This research proposes a method for estimating a nationwide drive time matrix between ZIP code areas in the U.S.—a geographic unit at which many national datasets such as health information are compiled and distributed. The method (1) does not rely on intensive efforts in data preparation or access to advanced computing resources, (2) uses algorithms of varying complexity and computational time to estimate drive times of different trip lengths, and (3) accounts for both interzonal and intrazonal drive times. The core design samples ZIP code pairs with various intensities according to trip lengths and derives the drive times via Google Maps API, and the Google times are then used to adjust and improve some primitive estimates of drive times with low computational costs. The result provides a valuable resource for researchers. Introduction Estimating a drive time matrix between locations is a critical task in spatial analysis, commonly encountered by researchers in geography, urban planning, transportation engineering, business management, and operational research, etc. To list a few, analytical models such as spatial interaction modeling (Simini et al., 2012), travel demand estimation (McFadden, 1974), location-allocation problems (ReVelle and Swain, 1970; Hu et al., 2019), spatial accessibility measures (Luo and Wang, 2003; Dony et al., 2015; Balomenos et al., 2019; Zhu et al., 2020), and delineation of health care market areas (Wang et al. 2020), rely on attainment of reliable drive time estimation from a set of origin locations to a set of destination locations. Such a task for a small-size matrix has become routine in many GIS and transportation packages, such as ESRI® ArcGIS and Caliper® TransCAD. However, it can be a challenging task for a large matrix. Most studies in this scope focus on small areas like individual cities or counties. Estimating the time matrix at larger geographic scales, such as a national scope, is of increasingly great importance to researchers and policy makers. Take the U.S. health care as an example, a national travel time matrix is the most critical component to study the average geographic access to health care (Onega et al., 2008; Boscoe et al., 2012), measure the variations in geographic access across regions (Onega et al., 2017), identify the areas where the health care geographic access is significantly lower than average, suggest the most appropriate sites for new care facilities, and facilitate the implementation of other strategies for reducing health care disparities such as remote health care, health care on wheels, and transit to care. The challenges for calibrating a large drive time matrix include availability of reliable road network (including traffic) data, programming expertise, and computational power. Many studies assume a free flow condition on roads to eliminate the data requirement on traffic, and are often limited to estimation of drive times from areas (e.g., ZIP code area, census tract) to the nearest locations (Onega et al., 2008; 2017; Boscoe et al., 2012; Ikram et al., 2015) or between locations within a short range (Shi et al., 2012) with a significantly reduced number of OD (origin-destination) pairs. Most recently, Saxon and Snow (2019) estimated a drive time matrix from each census tract to each primary care location within 62 miles (100 km) in the U.S. by tapping into advanced computing resources such as distributed computing and sophisticated algorithms, which may not be accessible by most researchers. They did not consider traffic conditions or node impedance, and the result tended to underestimate drive times especially for short trips. One way to account for traffic effect in drive time estimation relies on the utilization of traffic sensors or auxiliary data sources. These sensors include loop detectors (Kwon et al., 2000; Coifman, 2002) and automatic vehicle identification systems—such as toll collection system (El Faouzi et al., 2009), license plate recognition system (van Hinsbergen et al., 2009), and Bluetooth-based system (Bhaskar and Chung, 2013)—that are installed at certain locations along the road. They can accurately capture travel speeds and times. Other sensors are rather flexible, such as the floating or probe vehicles that consist of a sample of vehicles equipped with GPS units running with traffic (De Fabritiis et al., 2008). Based on collected information on a vehicle’s location, direction, and speed in a short time interval, drive times between any two locations in a network can be readily attained (Semanjski, 2015). A few recent studies attempted to estimate drive times using big data. Toole et al. (2015) used call detail records (CDRs) to obtain drive times for all road segments in five selected cities worldwide. Woodard et al. (2017) derived drive times on all roadways in the Seattle metropolitan region based on collected mobile phone GPS data. Although these methods can provide highly accurate estimates on actual drive times in traffic, their reliance on installation of physical equipment or big crowdsourced data restricts their usage to only small areas ranging from major corridors to metro regions. An alternative approach is to use third-party web mapping services such as Google Maps and MapQuest. For example, Google Maps Distance Matrix API uses the Google data such as its road network and collected traffic information to estimate drive times between a set of origins and a set of destinations. Similarly, MapQuest’s Route Matrix API uses open-source mapping data from the OpenStreetMap project to achieve this goal. Another benefit of using these commercial web services is to relieve analysts of the burden of preparing street network data and accessing GIS/transportation software (Boscoe et al., 2012). However, the free usage of these services comes with request limits. For instance, Google Maps offers free usage up to 40,000 OD records per month (Hu and Downs, 2019). A similar restriction applies to MapQuest. As a result, researchers usually use this approach to derive drive times for a limited number of OD pairs (Wang and Xu, 2011). Another issue related to drive time estimation between areas is the so-called aggregation error (Hu and Wang, 2016). The centroid-to-centroid approach assumes that all people in an area live at the centroid of an area (Hewko et al., 2002), and inevitably overlooks intrazonal travels (Kordi et al., 2012; Bhatta and Larsen, 2011). For example, the average commuting time within a traffic analysis zone (TAZ) is 11.3 minutes for auto drivers in Cleveland, Ohio (Wang, 2003). Given the average area of 82.25 square miles for the ZIP code areas in the U.S., intrazonal drive times at the ZIP code area level can be significant, especially in low-density suburban or rural areas. Its omission accounts for a high percentage in error for short-range trips. A common approach approximates intrazonal travel distance as the radius of an area-equivalent circle (Frost et al., 1998, Horner and Murray, 2002; Hu and Wang, 2015). Some recent studies use Monte Carlo simulation techniques to improve the estimation of trip lengths between area units (Hu and Wang, 2016; Hu et al. 2017), and offers a viable solution to intrazonal drive time estimation. This study seeks to estimate a very large drive time matrix between ZIP code areas in the U.S. ZIP code area is a popular geographic unit used in many nationwide datasets. For instance, ZIP code area is often the finest geographic scale at which health information is compiled and distributed in the U.S. (Berke and Shi, 2009). Our method (1) does not rely on intensive efforts in data preparation or access to advanced computing resources, (2) uses algorithms of varying complexity and computational time to estimate drive times of different trip lengths, and (3) accounts for both interzonal and intrazonal drive times. Both the program and results will be available for free download, and provide a valuable resource for researchers. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [1T] his is a preprint of: Hu, Y., Wang, C., Li, R., & Wang, F. (2020). Estimating a large drive time matrix between ZIP codes in the United States: A differential sampling approach. Journal of Transport Geography, 86, 102770. https://doi.org/10.1016/j.jtrangeo.2020.102770

A Nationwide Drive Time Matrix Between U.S. ZIP Code Areas: Abstract and Introduction

Differentially Private Data Science - What, Why And How?

HackerNoon: What is your company in 2–5 words? Stephen Chen: Your Financial Confidence Platform. Why is now the time for your company to exist? There is growing awareness that financial wellness and making good financial choices over your lifetime can change your life (and your family’s life). Just like we have learned that being physically & mentally healthy requires good diet, exercise, sleep and stress management; we’re learning that saving, investing and understanding our own behavior are key to financial wellness. Building wealth and financial security is simple but not easy — we are working on making it accessible to anyone through free / low cost software, classes, coaching and a community. What do you love about your team, and why are you the ones to solve this problem? Everyone at Boldin (formerly NewRetirement) is purpose driven. We see how complex it is for people to be able to make confident decisions about their money — it’s a huge social problem. Our team is deeply invested in listening to our large and growing community. Driven by their feedback, we’ve created a financial planning platform, built from the ground up to put the power of planning in everyone’s hands. In building for them, we’ve created a product that’s truly aligned with their needs. Part of delivering on our customer-first approach has been disrupting traditional financial service models. Instead of unexpected fees based on transactions and assets, our customers pay a small, fixed, transparent price for a subscription to our platform. If you weren’t building your startup, what would you be doing? Working on fixing the climate. I see our work as a way to help do that indirectly, since many people want to solve big problems like this but feel locked in their current careers since they have low financial confidence in whether they can take risks like retiring or transitioning to a more fulfilling career. We want to unlock the potential of our community by helping them get financially confident. At the moment, how do you measure success? What are your metrics? Because we have a mission to help people, our key success metric is monthly active users - people who access our platform to find financial confidence, regardless of if they are paying us or not. In a few sentences, what do you offer to whom? We offer a financial planning platform and services that enable anyone to build their own financial plan and gain financial confidence.  We help people get organized, assess their financial strengths and weaknesses, make better financial decisions, and stay on track to the life they want (now and into the future). Consumers can invite coaches and advisors to work with them. They can also take classes or participate in our community. The platform can also be white labeled by 3rd party advisors, employers, or financial services providers who want to scale financial planning. What’s most exciting about your traction to date? Our numbers are up and to the right, but I love it when people I know tell me that they discovered our product and are getting value from it.  I've had old high school friends reach out and tell me that they are retiring early because of our product. And, last week I was in the grocery store and a friend from when my kids were in elementary school came up and told me how much value they are getting from Boldin. I imagine it's like hearing your song on the radio. Where do you think your growth will be next year? In terms of revenue and users, we anticipate our growth to be better than 100% this year. We are also focused on continuing to grow the value we provide to end users. Tell us about your first paying customer and revenue expectations over the next year. We distribute our tools direct to consumer, through the workplace, and via financial advisors. Our first paying customer was 5 years ago, I believe they are still on the platform.  For workplace, it was Nationwide Insurance. And, for advisors it was a doctor from Kaiser Permanente.  We are trying to get to $10M ARR this year. What’s your biggest threat? Getting flamed on Hackernoon :-)  jk - someone else beats us to scaling via AI or a cyber attack. HackerNoon : What is your company in 2–5 words? HackerNoon Stephen Chen : Your Financial Confidence Platform. Stephen Chen Stephen Chen Why is now the time for your company to exist? There is growing awareness that financial wellness and making good financial choices over your lifetime can change your life (and your family’s life). Just like we have learned that being physically & mentally healthy requires good diet, exercise, sleep and stress management; we’re learning that saving, investing and understanding our own behavior are key to financial wellness. Building wealth and financial security is simple but not easy — we are working on making it accessible to anyone through free / low cost software, classes, coaching and a community. What do you love about your team, and why are you the ones to solve this problem? Everyone at Boldin (formerly NewRetirement) is purpose driven. We see how complex it is for people to be able to make confident decisions about their money — it’s a huge social problem. Our team is deeply invested in listening to our large and growing community. Boldin Driven by their feedback, we’ve created a financial planning platform, built from the ground up to put the power of planning in everyone’s hands. In building for them, we’ve created a product that’s truly aligned with their needs. Part of delivering on our customer-first approach has been disrupting traditional financial service models. Instead of unexpected fees based on transactions and assets, our customers pay a small, fixed, transparent price for a subscription to our platform. If you weren’t building your startup, what would you be doing? Working on fixing the climate. I see our work as a way to help do that indirectly, since many people want to solve big problems like this but feel locked in their current careers since they have low financial confidence in whether they can take risks like retiring or transitioning to a more fulfilling career. We want to unlock the potential of our community by helping them get financially confident. At the moment, how do you measure success? What are your metrics? Because we have a mission to help people, our key success metric is monthly active users - people who access our platform to find financial confidence, regardless of if they are paying us or not. In a few sentences, what do you offer to whom? We offer a financial planning platform and services that enable anyone to build their own financial plan and gain financial confidence.  We help people get organized, assess their financial strengths and weaknesses, make better financial decisions, and stay on track to the life they want (now and into the future). Consumers can invite coaches and advisors to work with them. They can also take classes or participate in our community. The platform can also be white labeled by 3rd party advisors, employers, or financial services providers who want to scale financial planning. What’s most exciting about your traction to date? Our numbers are up and to the right, but I love it when people I know tell me that they discovered our product and are getting value from it.  I've had old high school friends reach out and tell me that they are retiring early because of our product. And, last week I was in the grocery store and a friend from when my kids were in elementary school came up and told me how much value they are getting from Boldin. I imagine it's like hearing your song on the radio. Where do you think your growth will be next year? In terms of revenue and users, we anticipate our growth to be better than 100% this year. We are also focused on continuing to grow the value we provide to end users. Tell us about your first paying customer and revenue expectations over the next year. We distribute our tools direct to consumer, through the workplace, and via financial advisors. Our first paying customer was 5 years ago, I believe they are still on the platform.  For workplace, it was Nationwide Insurance. And, for advisors it was a doctor from Kaiser Permanente.  We are trying to get to $10M ARR this year. What’s your biggest threat? Getting flamed on Hackernoon :-)  jk - someone else beats us to scaling via AI or a cyber attack.

"Our approach has been disrupting traditional financial service models." says Boldin CEO/Founder

„Нашият подход нарушава традиционните модели на финансови услуги“, казва Болдин, главен изпълнителен директор/основател

„Unser Ansatz hat traditionelle Finanzdienstleistungsmodelle aufgebrochen“, sagt CEO/Gründer von Boldin

"Nuestro enfoque ha sido revolucionar los modelos tradicionales de servicios financieros", afirma el director ejecutivo y fundador de Boldin

"Il nostro approccio ha rivoluzionato i modelli tradizionali dei servizi finanziari", afferma il CEO/fondatore di Boldin

「私たちのアプローチは、伝統的な金融サービスモデルを破壊してきました」とボルディンのCEO兼創設者は語る。

Болдин башкы директору/негиздөөчүсү мындай дейт: "Биздин мамиле салттуу финансылык тейлөө моделдерин бузуп жатат"

„Нашиот пристап ги нарушува традиционалните модели на финансиски услуги“, вели извршниот директор/основач на Boldin

"Maitiro edu anga achikanganisa tsika dzechinyakare masevhisi emari" anodaro Boldin CEO/Muvambi

"Vårt tillvägagångssätt har stört traditionella finansiella tjänstemodeller", säger Boldin VD/grundare

"Муносибати мо моделҳои анъанавии хидматрасонии молиявиро вайрон мекунад" мегӯяд CEO / Муассис Болдин

Boldin CEO'su/Kurucusu, "Yaklaşımımız geleneksel finansal hizmet modellerini altüst ediyor" diyor

"Indlela yethu ibilokhu iphazamisa amamodeli wesevisi yezezimali ajwayelekile" kusho i-CEO/UMsunguli we-Boldin

Data centers have become an integral part of the digital age. Unfortunately, many communities realize they are loud, resource-intensive, and visually unappealing only after they’ve been built. Are these buildings neighborhood-ruiners, or are peoples’ concerns overblown? Are Data Centers Already Ruining Neighborhoods? The residents of the sleepy, tree-lined subdivision of Great Oak didn’t realize what was in store for them when construction began for an Amazon Web Services (AWS) data center. This small community in Prince William County, Virginia, soon became the epicenter of an ongoing quarrel between a multinational tech giant and a local homeowners association. Dale Browne, the president of the Great Oak Homeowners Association, said the building’s “constant whir” was “obnoxious” and regularly exceeded 60 decibels (dB), according to measurements taken by some of the neighborhood’s residents. Community members went as far as to stage protests out front, holding signs condemning the noise. Others didn’t object to the building’s construction or operation. While some saw it as an innovative move, others were apathetic because their once-rural oasis had already become a minimetropolis. After all, what’s one data center among highways, power lines, and supersized shopping centers? Still, many residents were unhappy seeing these massive server warehouses pop up nearby. In the neighboring Loudoun County, companies have built them as close as 50 feet — a mere backyard away — from residential properties. Construction has picked up pace in the United States, prompting similar quarrels and concerns nationwide. Why Are Data Centers Popping up Everywhere? Data centers are proliferating despite pockets of concern. The artificial intelligence market — which experts expect will reach $407 billion by 2027 — is one of the main drivers of this trend. Cloud computing technology and the Internet of Things (IoT) are also pushing demand exponentially higher. Many modern technologies are power-hungry and water-thirsty. They need cutting-edge servers and a tremendous amount of storage to function. Whether companies train a machine learning model or store 8K videos on cloud servers, they need data centers. While data center proliferation has been on the rise for years, it only became a well-known point of contention recently when AI’s popularity prompted firms to reinvest in server warehouses. Why are data centers being built in Virginia? Decision-makers are quickly making this state the so-called “data center capital of the world” for good reason. While some counties are inviting tech companies in hopes of inspiring a second Silicon Valley, others are just in convenient locations — the land and labor are cheap in rural areas. Of course, the robust electric and telecommunications infrastructure helps. How These Buildings Impact Nearby Neighborhoods Why are people against data centers? One of the public’s biggest complaints is noise — many don’t realize how loud these buildings are until they live next to one. Other common concerns include resource usage, greenhouse gas emissions, traffic congestion, and potential pollution. Water Drainage Cooling a massive warehouse packed to the brim with servers, routers, storage systems, and processors takes a lot of water. Research shows one large data center can use up between 1 million and 5 million gallons of water daily — which is enough to supply a town of 10,000-50,000 people. Noise Pollution Many data centers have on-site generators. Their cooling systems — essential for keeping hardware operational — contain intake and exhaust fans, which are objectionably loud. They produce between 55 and 85 dB typically. The noise is even more noticeable in rural areas where massive, nondescript buildings replace spaces that used to be forests or farmland. Are data centers noisy at night? Most are since they run around the clock. Even if their volume doesn’t increase after hours, their loudness is more noticeable when it gets quiet. People often describe the noise as a buzzing, tinny whining or low-pitched roar. Even 60 dB — the low end of the typical spectrum — sounds like overlapping conversations or background music. Power Depletion Do data centers use a lot of energy? They are among the most energy-intensive buildings in existence, consuming up to 50 times more power than standard offices. Although some have on-site power sources — diesel generators are a common choice — many rely on the local electrical infrastructure. The Implications of Having a Data Center Nearby Many people are concerned about having a structure that constantly produces 55-85 dB in their backyards — and for good reason. How loud is a data center? While 60 dB is equivalent to conversation, 70 dB seems as loud as a packed business, and 80 dB sounds as loud as a police siren or power tools. At 85 dB and above, hearing loss occurs. There is also concern about intense resource usage. In Maryland, a proposed 70-mile power line that would cut across the state recently angered residents. While environmental advocates and homeowners have vehemently opposed the $424 million project on the grounds of ecosystem and property preservation, it could be up and running as early as 2027. The health of local communities and ecosystems may also be at stake. If data centers use generators for on-site power, they could produce toxic pollutants like nitrogen oxide or diesel exhaust. The cooling system’s noise alone could drive local wildlife away, damaging environmentally sensitive areas. Counties that initially welcomed large technology and cloud computing companies with open arms have wavered after realizing how loud and resource-intensive these structures are. Many may soon raise taxes on hardware, revise their noise ordinances, or enforce strict project review processes to make it worthwhile. Are Communities’ Data Center Fears Justified? The worst-case scenario is using 5 million gallons of water, outputting 85 dB, and producing toxic diesel exhaust daily. That said, most data centers will be noisy and resource-intensive. Air cooling is loud but standard, accounting for 99% of methods in use today. However, that doesn’t mean engineers and operators are unwilling to change to help their unhappy neighbors. The Great Oak Homeowners Association is an excellent example of this fact. While Amazon initially denied the residents’ claims its building was outputting 60 dB — even going as far as to conduct a strategically engineered study to back its claim — it eventually took action. One year after the spat between the AWS data centers and the Great Oak community made headlines, Amazon lengthened its exhaust tubes and directed airflow vertically. Measurements from it and the Homeowners Association revealed the noise leveldropped by 10 dB to about 50 dB, effectively cutting it in half. Currently, it seems decision-makers aren’t considering the long-term repercussions of building near neighborhoods — they’re quickly seizing whatever land is cheap and available. To ease communities’ fears, they should consider their projects’ long-term viability, accounting for how construction and operation will affect nearby residents and local ecosystems. When considering all of the negatives in a vacuum, data centers sound like one of the worst structures a company could ever build. However, they support essential services and help connect people around the world. Most of today’s problems are growing pains — now that engineers and operators know what not to do, they can build better or retrofit existing buildings. For example, while air cooling generates significant noise, immersion cooling — which involves submerging hardware in a non-conductive liquid — apparently sounds considerably quieter in comparison. Engineers could also use on-site renewables to reduce their reliance on the city’s power and water supply, further easing residents’ concerns. What Companies and Communities Can Do Going Forward Gathering and implementing feedback is the only way to permanently ease communities’ concerns. This strategy may even pay off for technology companies because it streamlines site research and lets them know what interventions to prioritize. For example, if people worry about unsightly power lines but don’t care about noise, firms only need to build on-site power. Data centers have become an integral part of the digital age. Unfortunately, many communities realize they are loud, resource-intensive, and visually unappealing only after they’ve been built. Are these buildings neighborhood-ruiners, or are peoples’ concerns overblown? Data centers have become an integral part of the digital age. Unfortunately, many communities realize they are loud, resource-intensive, and visually unappealing only after they’ve been built. Are these buildings neighborhood-ruiners, or are peoples’ concerns overblown? Are Data Centers Already Ruining Neighborhoods? Are Data Centers Already Ruining Neighborhoods? The residents of the sleepy, tree-lined subdivision of Great Oak didn’t realize what was in store for them when construction began for an Amazon Web Services (AWS) data center. This small community in Prince William County, Virginia, soon became the epicenter of an ongoing quarrel between a multinational tech giant and a local homeowners association. Dale Browne, the president of the Great Oak Homeowners Association, said the building’s “constant whir” was “obnoxious” and regularly exceeded 60 decibels (dB), according to measurements taken by some of the neighborhood’s residents. Community members went as far as to stage protests out front, holding signs condemning the noise. regularly exceeded 60 decibels regularly exceeded 60 decibels Others didn’t object to the building’s construction or operation. While some saw it as an innovative move, others were apathetic because their once-rural oasis had already become a minimetropolis. After all, what’s one data center among highways, power lines, and supersized shopping centers? Still, many residents were unhappy seeing these massive server warehouses pop up nearby. In the neighboring Loudoun County, companies have built them as close as 50 feet — a mere backyard away — from residential properties. Construction has picked up pace in the United States, prompting similar quarrels and concerns nationwide. as close as 50 feet as close as 50 feet Why Are Data Centers Popping up Everywhere? Data centers are proliferating despite pockets of concern. The artificial intelligence market — which experts expect will reach $407 billion by 2027 — is one of the main drivers of this trend. Cloud computing technology and the Internet of Things (IoT) are also pushing demand exponentially higher. Many modern technologies are power-hungry and water-thirsty. They need cutting-edge servers and a tremendous amount of storage to function. will reach $407 billion will reach $407 billion Whether companies train a machine learning model or store 8K videos on cloud servers, they need data centers. While data center proliferation has been on the rise for years, it only became a well-known point of contention recently when AI’s popularity prompted firms to reinvest in server warehouses. Why are data centers being built in Virginia? Decision-makers are quickly making this state the so-called “data center capital of the world” for good reason. While some counties are inviting tech companies in hopes of inspiring a second Silicon Valley, others are just in convenient locations — the land and labor are cheap in rural areas. Of course, the robust electric and telecommunications infrastructure helps. land and labor are cheap land and labor are cheap How These Buildings Impact Nearby Neighborhoods Why are people against data centers? One of the public’s biggest complaints is noise — many don’t realize how loud these buildings are until they live next to one. Other common concerns include resource usage, greenhouse gas emissions, traffic congestion, and potential pollution. Water Drainage Cooling a massive warehouse packed to the brim with servers, routers, storage systems, and processors takes a lot of water. Research shows one large data center can use up between 1 million and 5 million gallons of water daily — which is enough to supply a town of 10,000-50,000 people. between 1 million and 5 million gallons between 1 million and 5 million gallons Noise Pollution Many data centers have on-site generators. Their cooling systems — essential for keeping hardware operational — contain intake and exhaust fans, which are objectionably loud. They produce between 55 and 85 dB typically. The noise is even more noticeable in rural areas where massive, nondescript buildings replace spaces that used to be forests or farmland. produce between 55 and 85 dB produce between 55 and 85 dB Are data centers noisy at night? Most are since they run around the clock. Even if their volume doesn’t increase after hours, their loudness is more noticeable when it gets quiet. People often describe the noise as a buzzing, tinny whining or low-pitched roar. Even 60 dB — the low end of the typical spectrum — sounds like overlapping conversations or background music. Power Depletion Do data centers use a lot of energy? They are among the most energy-intensive buildings in existence, consuming up to 50 times more power than standard offices. Although some have on-site power sources — diesel generators are a common choice — many rely on the local electrical infrastructure. up to 50 times more power up to 50 times more power The Implications of Having a Data Center Nearby Many people are concerned about having a structure that constantly produces 55-85 dB in their backyards — and for good reason. How loud is a data center? While 60 dB is equivalent to conversation , 70 dB seems as loud as a packed business, and 80 dB sounds as loud as a police siren or power tools. At 85 dB and above, hearing loss occurs. is equivalent to conversation is equivalent to conversation There is also concern about intense resource usage. In Maryland, a proposed 70-mile power line that would cut across the state recently angered residents. While environmental advocates and homeowners have vehemently opposed the $424 million project on the grounds of ecosystem and property preservation, it could be up and running as early as 2027. proposed 70-mile power line proposed 70-mile power line The health of local communities and ecosystems may also be at stake. If data centers use generators for on-site power, they could produce toxic pollutants like nitrogen oxide or diesel exhaust. The cooling system’s noise alone could drive local wildlife away, damaging environmentally sensitive areas. Counties that initially welcomed large technology and cloud computing companies with open arms have wavered after realizing how loud and resource-intensive these structures are. Many may soon raise taxes on hardware, revise their noise ordinances, or enforce strict project review processes to make it worthwhile. Are Communities’ Data Center Fears Justified? The worst-case scenario is using 5 million gallons of water, outputting 85 dB, and producing toxic diesel exhaust daily. That said, most data centers will be noisy and resource-intensive. Air cooling is loud but standard, accounting for 99% of methods in use today. However, that doesn’t mean engineers and operators are unwilling to change to help their unhappy neighbors. accounting for 99% of methods accounting for 99% of methods The Great Oak Homeowners Association is an excellent example of this fact. While Amazon initially denied the residents’ claims its building was outputting 60 dB — even going as far as to conduct a strategically engineered study to back its claim — it eventually took action. conduct a strategically engineered study conduct a strategically engineered study One year after the spat between the AWS data centers and the Great Oak community made headlines, Amazon lengthened its exhaust tubes and directed airflow vertically. Measurements from it and the Homeowners Association revealed the noise level dropped by 10 dB to about 50 dB, effectively cutting it in half. dropped by 10 dB dropped by 10 dB Currently, it seems decision-makers aren’t considering the long-term repercussions of building near neighborhoods — they’re quickly seizing whatever land is cheap and available. To ease communities’ fears, they should consider their projects’ long-term viability, accounting for how construction and operation will affect nearby residents and local ecosystems. When considering all of the negatives in a vacuum, data centers sound like one of the worst structures a company could ever build. However, they support essential services and help connect people around the world. Most of today’s problems are growing pains — now that engineers and operators know what not to do, they can build better or retrofit existing buildings. For example, while air cooling generates significant noise, immersion cooling — which involves submerging hardware in a non-conductive liquid — apparently sounds considerably quieter in comparison. Engineers could also use on-site renewables to reduce their reliance on the city’s power and water supply, further easing residents’ concerns. apparently sounds considerably quieter apparently sounds considerably quieter What Companies and Communities Can Do Going Forward Gathering and implementing feedback is the only way to permanently ease communities’ concerns. This strategy may even pay off for technology companies because it streamlines site research and lets them know what interventions to prioritize. For example, if people worry about unsightly power lines but don’t care about noise, firms only need to build on-site power.

Will Data Centers Ruin Your Neighborhood?

هل مراكز البيانات ستدمر حيّك؟

¿Centros de Datos ukanakax Barrioman jan walt’ayaspati?

ডেটা সেন্টার কি আপনার প্রতিবেশীকে ধ্বংস করবে?

Hoće li data centri uništiti vaše susjedstvo?

¿Los centros de datos arruinarán su vecindario?

آیا مراکز داده محله شما را خراب خواهند کرد؟

データセンターはあなたの近所を台無しにしますか?

Na Disenthara tša Data di tla Senya Tikologo ya Gago?

¿Centros de Datos nisqakuna Barrioykita waqllichinqachu?

Zničia dátové centrá vaše okolie?

Чи центри обробки даних зруйнують ваше сусідство?

Ma'lumot markazlari sizning mahallangizni buzadimi?

Perhaps best known globally for sunny vacations, folkloric Flamenco, and savory Paella, Spain is also home to one of the fastest-growing technology ecosystems in Europe. According to data from Dealroom, last year, Spanish startups were collectively worth around €100 billion (USD 111 billion), outpacing countries like Norway and Denmark, but still well behind more established ecosystems like the United Kingdom, Germany, and The Netherlands. The country ranked seventh in Europe in terms of attracting venture capital and had the fourth-highest number of funding rounds, according to the report which was made in collaboration with BBVA. While cities like Madrid and Barcelona lead the country for development on Startup Genome’s Global Ecosystems report, other cities like Valencia are also playing a big role in transforming Spain into an innovation hub on the continent. Valencia, a startup hub Valencia, Europe’s fifth busiest port city, is located in Eastern Spain on the Mediterranean Sea. It’s the third most populated municipality in the country and in addition to being one of Spain’s most important cities for export and logistics, it also houses a strong manufacturing sector and is a tourism hotspot. The city’s blend of old-world architecture (it was believed to be founded in 138 B.C.) with the futuristic buildings of one of its most famous children, renowned architect Santiago Calatrava, make for an aesthetic ripe for technological innovation. And founders are taking note. According to Startup Genome data, the city’s estimated startup ecosystem value – which measures exits and startup valuations – is over USD 5 billion, and the city saw 12 exits in 2022 and five in 2023. Particularly strong are startups innovating in AI. According to data from Startup Valencia, a non-profit dedicated to promoting startups in the city, over 20% of Valencian startups are powered by AI. What’s more, nearly 35% of startup companies in the city count at least one female co-founder, and 81% of startups surveyed say they align with the United Nations Sustainable Development Goals (SDGs). Overall, the non-profit reported that the city’s startup sector is growing at a rate of 16%. One big initiative from Startup Valencia that is helping to fuel that growth is their annual technology conference, VDS (formerly Valencia Digital Summit). The two-day gathering, which will host its seventh edition on October 23 and 24 under the theme “Embracing Evolution: Invest in the Leaders of Tomorrow,” convenes some of the world’s leading technology thought leaders to southern Europe. According to a company statement, the event is expected to bring together over 2,500 startups, and over 12,000 attendees from more than 100 countries. Also in attendance will be 700 investors, including representatives from global funds like Softbank and Techstars. Startups will also have a chance to pitch to investors on the demo stage, and featured speakers include Steve Chen, co-founder and former Chief Technology Officer of YouTube, Randi Zuckerberg, founder and CEO of Zuckerberg Media and HUG, Juan Antonio Samaranch, Vice President of the International Olympic Committee, among many others. Other initiatives too are driving the startup ecosystem forward in Valencia. Recently, Startup Valencia announced a partnership with the German research institute The Center for Digital Technology and Management, which aims to help the center scale in Spain and help foment local entrepreneurship. Also, Invest in Valencia, which promotes foreign investment into the city, is creating initiatives to promote Valencia’s 5G, cybersecurity and innovation sectors. Spain’s nation-wide support of startups On a national level, Spain has been taking steps in recent years to help foster startups and the tech sector. In December of 2022, the country passed its Startups Law, which aimed to cut some bureaucratic processes for starting companies, as well as tax incentives for startups and investors, as well as lower barriers for foreigners to enter the country to start a business or work with a startup. Earlier this year, the government announced the creation of a National Entrepreneurship Office (ONE), which focuses principally on prompting startups. The platform offers information for founders who want to recruit international talent, provides spaces for networking and startup events, information on grants and accelerators, and other information relevant to entrepreneurs. And in May, the government approved its AI strategy, which will deploy a total of €2.1 billion to help shore up the country’s AI sector. The strategy includes more funding for homegrown supercomputing, a structure for building a sustainable network of data centers to power AI, as well as investments and loan opportunities for companies innovating in AI. Taken together, the push from Spain as a whole, but also from Valencia as a startup hub, aims to help propel the country to be a leader in tech in southern Europe in the years to come. At least, that is the hope. The Sociable Perhaps best known globally for sunny vacations, folkloric Flamenco, and savory Paella, Spain is also home to one of the fastest-growing technology ecosystems in Europe. According to data from Dealroom, last year, Spanish startups were collectively worth around €100 billion (USD 111 billion), outpacing countries like Norway and Denmark, but still well behind more established ecosystems like the United Kingdom, Germany, and The Netherlands. data The country ranked seventh in Europe in terms of attracting venture capital and had the fourth-highest number of funding rounds, according to the report which was made in collaboration with BBVA. according While cities like Madrid and Barcelona lead the country for development on Startup Genome’s Global Ecosystems report , other cities like Valencia are also playing a big role in transforming Spain into an innovation hub on the continent. report Valencia, a startup hub Valencia, a startup hub Valencia, Europe’s fifth busiest port city, is located in Eastern Spain on the Mediterranean Sea. It’s the third most populated municipality in the country and in addition to being one of Spain’s most important cities for export and logistics, it also houses a strong manufacturing sector and is a tourism hotspot. The city’s blend of old-world architecture (it was believed to be founded in 138 B.C.) with the futuristic buildings of one of its most famous children, renowned architect Santiago Calatrava, make for an aesthetic ripe for technological innovation. And founders are taking note. According to Startup Genome data , the city’s estimated startup ecosystem value – which measures exits and startup valuations – is over USD 5 billion, and the city saw 12 exits in 2022 and five in 2023. data Particularly strong are startups innovating in AI. According to data from Startup Valencia, a non-profit dedicated to promoting startups in the city, over 20% of Valencian startups are powered by AI. data What’s more, nearly 35% of startup companies in the city count at least one female co-founder, and 81% of startups surveyed say they align with the United Nations Sustainable Development Goals (SDGs). Overall, the non-profit reported that the city’s startup sector is growing at a rate of 16%. One big initiative from Startup Valencia that is helping to fuel that growth is their annual technology conference, VDS (formerly Valencia Digital Summit). The two-day gathering, which will host its seventh edition on October 23 and 24 under the theme “Embracing Evolution: Invest in the Leaders of Tomorrow,” convenes some of the world’s leading technology thought leaders to southern Europe. VDS According to a company statement, the event is expected to bring together over 2,500 startups, and over 12,000 attendees from more than 100 countries. Also in attendance will be 700 investors, including representatives from global funds like Softbank and Techstars. Startups will also have a chance to pitch to investors on the demo stage, and featured speakers include Steve Chen , co-founder and former Chief Technology Officer of YouTube, Randi Zuckerberg , founder and CEO of Zuckerberg Media and HUG, Juan Antonio Samaranch , Vice President of the International Olympic Committee, among many others. Steve Chen Randi Zuckerberg Juan Antonio Samaranch Other initiatives too are driving the startup ecosystem forward in Valencia. Recently, Startup Valencia announced a partnership with the German research institute The Center for Digital Technology and Management, which aims to help the center scale in Spain and help foment local entrepreneurship. Also, Invest in Valencia , which promotes foreign investment into the city, is creating initiatives to promote Valencia’s 5G, cybersecurity and innovation sectors. announced Invest in Valencia Spain’s nation-wide support of startups Spain’s nation-wide support of startups On a national level, Spain has been taking steps in recent years to help foster startups and the tech sector. In December of 2022, the country passed its Startups Law, which aimed to cut some bureaucratic processes for starting companies, as well as tax incentives for startups and investors, as well as lower barriers for foreigners to enter the country to start a business or work with a startup. passed Earlier this year, the government announced the creation of a National Entrepreneurship Office (ONE), which focuses principally on prompting startups. The platform offers information for founders who want to recruit international talent, provides spaces for networking and startup events, information on grants and accelerators, and other information relevant to entrepreneurs. announced And in May, the government approved its AI strategy, which will deploy a total of €2.1 billion to help shore up the country’s AI sector. The strategy includes more funding for homegrown supercomputing, a structure for building a sustainable network of data centers to power AI, as well as investments and loan opportunities for companies innovating in AI. Taken together, the push from Spain as a whole, but also from Valencia as a startup hub, aims to help propel the country to be a leader in tech in southern Europe in the years to come. At least, that is the hope. The Sociable The Sociable The Sociable

NATIONWIDE

nationwide.com

10K+ employees

Since 1985

COMPANY RANKING

NATIONWIDE

EVERGREEN INDEX #8689

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Nationwide WIKI

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NATIONWIDE

nationwide.com

10K+ employees

Since 1985

COMPANY RANKING

NATIONWIDE

EVERGREEN INDEX #8689

RELATED COMPANIESRANK

STORIES ABOUT NATIONWIDE

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